categories
string | doi
string | id
string | year
float64 | venue
string | link
string | updated
string | published
string | title
string | abstract
string | authors
sequence |
|---|---|---|---|---|---|---|---|---|---|---|
null | null | 2406.05395 | null | null | http://arxiv.org/pdf/2406.05395v1 | 2024-06-08T08:12:41Z | 2024-06-08T08:12:41Z | Dynamic importance learning using fisher information gain for nonlinear
system identification | The Fisher Information Matrix (FIM) provides a way for quantifying the information content of an observable random variable concerning unknown parameters within a model that characterizes the variable. When parameters in a model are directly linked to individual features, the diagonal elements of the FIM can signify the relative importance of each feature. However, in scenarios where feature interactions may exist, a comprehensive exploration of the full FIM is necessary rather than focusing solely on its diagonal elements. This paper presents an end-to-end black box system identification approach that integrates the FIM into the training process to gain insights into dynamic importance and overall model structure. A decision module is added to the first layer of the network to determine the relevance scores using the entire FIM as input. The forward propagation is then performed on element-wise multiplication of inputs and relevance scores. Simulation results demonstrate that the proposed methodology effectively captures various types of interactions between dynamics, outperforming existing methods limited to polynomial interactions. Moreover, the effectiveness of this novel approach is confirmed through its application in identifying a real-world industrial system, specifically the PH neutralization process. | [
"['Vahid MohammadZadeh Eivaghi' 'Mahdi Aliyari Shoorehdeli']"
] |
null | null | 2406.05396 | null | null | http://arxiv.org/pdf/2406.05396v1 | 2024-06-08T08:24:06Z | 2024-06-08T08:24:06Z | Mean-field Chaos Diffusion Models | In this paper, we introduce a new class of score-based generative models (SGMs) designed to handle high-cardinality data distributions by leveraging concepts from mean-field theory. We present mean-field chaos diffusion models (MF-CDMs), which address the curse of dimensionality inherent in high-cardinality data by utilizing the propagation of chaos property of interacting particles. By treating high-cardinality data as a large stochastic system of interacting particles, we develop a novel score-matching method for infinite-dimensional chaotic particle systems and propose an approximation scheme that employs a subdivision strategy for efficient training. Our theoretical and empirical results demonstrate the scalability and effectiveness of MF-CDMs for managing large high-cardinality data structures, such as 3D point clouds. | [
"['Sungwoo Park' 'Dongjun Kim' 'Ahmed Alaa']"
] |
null | null | 2406.05405 | null | null | http://arxiv.org/pdf/2406.05405v1 | 2024-06-08T08:56:47Z | 2024-06-08T08:56:47Z | Robust Conformal Prediction Using Privileged Information | We develop a method to generate prediction sets with a guaranteed coverage rate that is robust to corruptions in the training data, such as missing or noisy variables. Our approach builds on conformal prediction, a powerful framework to construct prediction sets that are valid under the i.i.d assumption. Importantly, naively applying conformal prediction does not provide reliable predictions in this setting, due to the distribution shift induced by the corruptions. To account for the distribution shift, we assume access to privileged information (PI). The PI is formulated as additional features that explain the distribution shift, however, they are only available during training and absent at test time. We approach this problem by introducing a novel generalization of weighted conformal prediction and support our method with theoretical coverage guarantees. Empirical experiments on both real and synthetic datasets indicate that our approach achieves a valid coverage rate and constructs more informative predictions compared to existing methods, which are not supported by theoretical guarantees. | [
"['Shai Feldman' 'Yaniv Romano']"
] |
null | null | 2406.05413 | null | null | http://arxiv.org/pdf/2406.05413v1 | 2024-06-08T09:22:32Z | 2024-06-08T09:22:32Z | Discover Your Neighbors: Advanced Stable Test-Time Adaptation in Dynamic
World | Despite progress, deep neural networks still suffer performance declines under distribution shifts between training and test domains, leading to a substantial decrease in Quality of Experience (QoE) for multimedia applications. Existing test-time adaptation (TTA) methods are challenged by dynamic, multiple test distributions within batches. This work provides a new perspective on analyzing batch normalization techniques through class-related and class-irrelevant features, our observations reveal combining source and test batch normalization statistics robustly characterizes target distributions. However, test statistics must have high similarity. We thus propose Discover Your Neighbours (DYN), the first backward-free approach specialized for dynamic TTA. The core innovation is identifying similar samples via instance normalization statistics and clustering into groups which provides consistent class-irrelevant representations. Specifically, Our DYN consists of layer-wise instance statistics clustering (LISC) and cluster-aware batch normalization (CABN). In LISC, we perform layer-wise clustering of approximate feature samples at each BN layer by calculating the cosine similarity of instance normalization statistics across the batch. CABN then aggregates SBN and TCN statistics to collaboratively characterize the target distribution, enabling more robust representations. Experimental results validate DYN's robustness and effectiveness, demonstrating maintained performance under dynamic data stream patterns. | [
"['Qinting Jiang' 'Chuyang Ye' 'Dongyan Wei' 'Yuan Xue' 'Jingyan Jiang'\n 'Zhi Wang']"
] |
null | null | 2406.05424 | null | null | http://arxiv.org/pdf/2406.05424v1 | 2024-06-08T10:07:33Z | 2024-06-08T10:07:33Z | Recent advancements in computational morphology : A comprehensive survey | Computational morphology handles the language processing at the word level. It is one of the foundational tasks in the NLP pipeline for the development of higher level NLP applications. It mainly deals with the processing of words and word forms. Computational Morphology addresses various sub problems such as morpheme boundary detection, lemmatization, morphological feature tagging, morphological reinflection etc. In this paper, we present exhaustive survey of the methods for developing computational morphology related tools. We survey the literature in the chronological order starting from the conventional methods till the recent evolution of deep neural network based approaches. We also review the existing datasets available for this task across the languages. We discuss about the effectiveness of neural model compared with the traditional models and present some unique challenges associated with building the computational morphology tools. We conclude by discussing some recent and open research issues in this field. | [
"['Jatayu Baxi' 'Brijesh Bhatt']"
] |
null | null | 2406.05426 | null | null | http://arxiv.org/pdf/2406.05426v1 | 2024-06-08T10:11:10Z | 2024-06-08T10:11:10Z | Baking Symmetry into GFlowNets | GFlowNets have exhibited promising performance in generating diverse candidates with high rewards. These networks generate objects incrementally and aim to learn a policy that assigns probability of sampling objects in proportion to rewards. However, the current training pipelines of GFlowNets do not consider the presence of isomorphic actions, which are actions resulting in symmetric or isomorphic states. This lack of symmetry increases the amount of samples required for training GFlowNets and can result in inefficient and potentially incorrect flow functions. As a consequence, the reward and diversity of the generated objects decrease. In this study, our objective is to integrate symmetries into GFlowNets by identifying equivalent actions during the generation process. Experimental results using synthetic data demonstrate the promising performance of our proposed approaches. | [
"['George Ma' 'Emmanuel Bengio' 'Yoshua Bengio' 'Dinghuai Zhang']"
] |
null | null | 2406.05427 | null | null | http://arxiv.org/pdf/2406.05427v1 | 2024-06-08T10:12:00Z | 2024-06-08T10:12:00Z | Decision Mamba: A Multi-Grained State Space Model with Self-Evolution
Regularization for Offline RL | While the conditional sequence modeling with the transformer architecture has demonstrated its effectiveness in dealing with offline reinforcement learning (RL) tasks, it is struggle to handle out-of-distribution states and actions. Existing work attempts to address this issue by data augmentation with the learned policy or adding extra constraints with the value-based RL algorithm. However, these studies still fail to overcome the following challenges: (1) insufficiently utilizing the historical temporal information among inter-steps, (2) overlooking the local intrastep relationships among states, actions and return-to-gos (RTGs), (3) overfitting suboptimal trajectories with noisy labels. To address these challenges, we propose Decision Mamba (DM), a novel multi-grained state space model (SSM) with a self-evolving policy learning strategy. DM explicitly models the historical hidden state to extract the temporal information by using the mamba architecture. To capture the relationship among state-action-RTG triplets, a fine-grained SSM module is designed and integrated into the original coarse-grained SSM in mamba, resulting in a novel mamba architecture tailored for offline RL. Finally, to mitigate the overfitting issue on noisy trajectories, a self-evolving policy is proposed by using progressive regularization. The policy evolves by using its own past knowledge to refine the suboptimal actions, thus enhancing its robustness on noisy demonstrations. Extensive experiments on various tasks show that DM outperforms other baselines substantially. | [
"['Qi Lv' 'Xiang Deng' 'Gongwei Chen' 'Michael Yu Wang' 'Liqiang Nie']"
] |
null | null | 2406.05464 | null | null | http://arxiv.org/pdf/2406.05464v1 | 2024-06-08T12:58:13Z | 2024-06-08T12:58:13Z | DAISY: Data Adaptive Self-Supervised Early Exit for Speech
Representation Models | Self-supervised speech models have shown to be useful for various tasks, but their large size limits the use in devices with low computing power and memory. In this work, we explore early exit, an approach for reducing latency by exiting the forward process of a network early. Most approaches of early exit need a separate early exit model for each task, with some even requiring fine-tuning of the entire pretrained model. We introduce Data Adaptive Self-Supervised Early Exit (DAISY), an approach that decides when to exit based on the self-supervised loss, eliminating the need for multiple round of training and fine-tuning. DAISY matches the performance of HuBERT on the MiniSUPERB benchmark, but with much faster inference times. Our analysis on the adaptivity of DAISY shows that the model exits early (using fewer layers) on clean data while exits late (using more layers) on noisy data, dynamically adjusting the computational cost of inference based on the noise level of each sample. | [
"['Tzu-Quan Lin' 'Hung-yi Lee' 'Hao Tang']"
] |
null | null | 2406.05469 | null | null | http://arxiv.org/pdf/2406.05469v1 | 2024-06-08T13:19:18Z | 2024-06-08T13:19:18Z | Bayesian vs. PAC-Bayesian Deep Neural Network Ensembles | Bayesian neural networks address epistemic uncertainty by learning a posterior distribution over model parameters. Sampling and weighting networks according to this posterior yields an ensemble model referred to as Bayes ensemble. Ensembles of neural networks (deep ensembles) can profit from the cancellation of errors effect: Errors by ensemble members may average out and the deep ensemble achieves better predictive performance than each individual network. We argue that neither the sampling nor the weighting in a Bayes ensemble are particularly well-suited for increasing generalization performance, as they do not support the cancellation of errors effect, which is evident in the limit from the Bernstein-von~Mises theorem for misspecified models. In contrast, a weighted average of models where the weights are optimized by minimizing a PAC-Bayesian generalization bound can improve generalization performance. This requires that the optimization takes correlations between models into account, which can be achieved by minimizing the tandem loss at the cost that hold-out data for estimating error correlations need to be available. The PAC-Bayesian weighting increases the robustness against correlated models and models with lower performance in an ensemble. This allows us to safely add several models from the same learning process to an ensemble, instead of using early-stopping for selecting a single weight configuration. Our study presents empirical results supporting these conceptual considerations on four different classification datasets. We show that state-of-the-art Bayes ensembles from the literature, despite being computationally demanding, do not improve over simple uniformly weighted deep ensembles and cannot match the performance of deep ensembles weighted by optimizing the tandem loss, which additionally come with non-vacuous generalization guarantees. | [
"['Nick Hauptvogel' 'Christian Igel']"
] |
null | null | 2406.05470 | null | null | http://arxiv.org/pdf/2406.05470v1 | 2024-06-08T13:20:48Z | 2024-06-08T13:20:48Z | RandONet: Shallow-Networks with Random Projections for learning linear
and nonlinear operators | Deep Operator Networks (DeepOnets) have revolutionized the domain of scientific machine learning for the solution of the inverse problem for dynamical systems. However, their implementation necessitates optimizing a high-dimensional space of parameters and hyperparameters. This fact, along with the requirement of substantial computational resources, poses a barrier to achieving high numerical accuracy. Here, inpsired by DeepONets and to address the above challenges, we present Random Projection-based Operator Networks (RandONets): shallow networks with random projections that learn linear and nonlinear operators. The implementation of RandONets involves: (a) incorporating random bases, thus enabling the use of shallow neural networks with a single hidden layer, where the only unknowns are the output weights of the network's weighted inner product; this reduces dramatically the dimensionality of the parameter space; and, based on this, (b) using established least-squares solvers (e.g., Tikhonov regularization and preconditioned QR decomposition) that offer superior numerical approximation properties compared to other optimization techniques used in deep-learning. In this work, we prove the universal approximation accuracy of RandONets for approximating nonlinear operators and demonstrate their efficiency in approximating linear nonlinear evolution operators (right-hand-sides (RHS)) with a focus on PDEs. We show, that for this particular task, RandONets outperform, both in terms of numerical approximation accuracy and computational cost, the ``vanilla" DeepOnets. | [
"['Gianluca Fabiani' 'Ioannis G. Kevrekidis' 'Constantinos Siettos'\n 'Athanasios N. Yannacopoulos']"
] |
null | null | 2406.05477 | null | null | http://arxiv.org/pdf/2406.05477v1 | 2024-06-08T13:52:02Z | 2024-06-08T13:52:02Z | Attri-Net: A Globally and Locally Inherently Interpretable Model for
Multi-Label Classification Using Class-Specific Counterfactuals | Interpretability is crucial for machine learning algorithms in high-stakes medical applications. However, high-performing neural networks typically cannot explain their predictions. Post-hoc explanation methods provide a way to understand neural networks but have been shown to suffer from conceptual problems. Moreover, current research largely focuses on providing local explanations for individual samples rather than global explanations for the model itself. In this paper, we propose Attri-Net, an inherently interpretable model for multi-label classification that provides local and global explanations. Attri-Net first counterfactually generates class-specific attribution maps to highlight the disease evidence, then performs classification with logistic regression classifiers based solely on the attribution maps. Local explanations for each prediction can be obtained by interpreting the attribution maps weighted by the classifiers' weights. Global explanation of whole model can be obtained by jointly considering learned average representations of the attribution maps for each class (called the class centers) and the weights of the linear classifiers. To ensure the model is ``right for the right reason", we further introduce a mechanism to guide the model's explanations to align with human knowledge. Our comprehensive evaluations show that Attri-Net can generate high-quality explanations consistent with clinical knowledge while not sacrificing classification performance. | [
"['Susu Sun' 'Stefano Woerner' 'Andreas Maier' 'Lisa M. Koch'\n 'Christian F. Baumgartner']"
] |
null | null | 2406.05482 | null | null | http://arxiv.org/pdf/2406.05482v3 | 2024-06-17T05:08:14Z | 2024-06-08T14:14:19Z | Efficient Topology-aware Data Augmentation for High-Degree Graph Neural
Networks | In recent years, graph neural networks (GNNs) have emerged as a potent tool for learning on graph-structured data and won fruitful successes in varied fields. The majority of GNNs follow the message-passing paradigm, where representations of each node are learned by recursively aggregating features of its neighbors. However, this mechanism brings severe over-smoothing and efficiency issues over high-degree graphs (HDGs), wherein most nodes have dozens (or even hundreds) of neighbors, such as social networks, transaction graphs, power grids, etc. Additionally, such graphs usually encompass rich and complex structure semantics, which are hard to capture merely by feature aggregations in GNNs. Motivated by the above limitations, we propose TADA, an efficient and effective front-mounted data augmentation framework for GNNs on HDGs. Under the hood, TADA includes two key modules: (i) feature expansion with structure embeddings, and (ii) topology- and attribute-aware graph sparsification. The former obtains augmented node features and enhanced model capacity by encoding the graph structure into high-quality structure embeddings with our highly-efficient sketching method. Further, by exploiting task-relevant features extracted from graph structures and attributes, the second module enables the accurate identification and reduction of numerous redundant/noisy edges from the input graph, thereby alleviating over-smoothing and facilitating faster feature aggregations over HDGs. Empirically, TADA considerably improves the predictive performance of mainstream GNN models on 8 real homophilic/heterophilic HDGs in terms of node classification, while achieving efficient training and inference processes. | [
"['Yurui Lai' 'Xiaoyang Lin' 'Renchi Yang' 'Hongtao Wang']"
] |
null | null | 2406.05488 | null | null | http://arxiv.org/pdf/2406.05488v1 | 2024-06-08T14:40:53Z | 2024-06-08T14:40:53Z | Online Policy Distillation with Decision-Attention | Policy Distillation (PD) has become an effective method to improve deep reinforcement learning tasks. The core idea of PD is to distill policy knowledge from a teacher agent to a student agent. However, the teacher-student framework requires a well-trained teacher model which is computationally expensive.In the light of online knowledge distillation, we study the knowledge transfer between different policies that can learn diverse knowledge from the same environment.In this work, we propose Online Policy Distillation (OPD) with Decision-Attention (DA), an online learning framework in which different policies operate in the same environment to learn different perspectives of the environment and transfer knowledge to each other to obtain better performance together. With the absence of a well-performance teacher policy, the group-derived targets play a key role in transferring group knowledge to each student policy. However, naive aggregation functions tend to cause student policies quickly homogenize. To address the challenge, we introduce the Decision-Attention module to the online policies distillation framework. The Decision-Attention module can generate a distinct set of weights for each policy to measure the importance of group members. We use the Atari platform for experiments with various reinforcement learning algorithms, including PPO and DQN. In different tasks, our method can perform better than an independent training policy on both PPO and DQN algorithms. This suggests that our OPD-DA can transfer knowledge between different policies well and help agents obtain more rewards. | [
"['Xinqiang Yu' 'Chuanguang Yang' 'Chengqing Yu' 'Libo Huang' 'Zhulin An'\n 'Yongjun Xu']"
] |
null | null | 2406.05504 | null | null | http://arxiv.org/pdf/2406.05504v3 | 2024-06-27T08:42:11Z | 2024-06-08T16:04:33Z | G-Transformer: Counterfactual Outcome Prediction under Dynamic and
Time-varying Treatment Regimes | In the context of medical decision making, counterfactual prediction enables clinicians to predict treatment outcomes of interest under alternative courses of therapeutic actions given observed patient history. Prior machine learning approaches for counterfactual predictions under time-varying treatments focus on static time-varying treatment regimes where treatments do not depend on previous covariate history. In this work, we present G-Transformer, a Transformer-based framework supporting g-computation for counterfactual prediction under dynamic and time-varying treatment strategies. G-Transfomer captures complex, long-range dependencies in time-varying covariates using a Transformer architecture. G-Transformer estimates the conditional distribution of relevant covariates given covariate and treatment history at each time point using an encoder architecture, then produces Monte Carlo estimates of counterfactual outcomes by simulating forward patient trajectories under treatment strategies of interest. We evaluate G-Transformer extensively using two simulated longitudinal datasets from mechanistic models, and a real-world sepsis ICU dataset from MIMIC-IV. G-Transformer outperforms both classical and state-of-the-art counterfactual prediction models in these settings. To the best of our knowledge, this is the first Transformer-based architecture for counterfactual outcome prediction under dynamic and time-varying treatment strategies. | [
"['Hong Xiong' 'Feng Wu' 'Leon Deng' 'Megan Su' 'Li-wei H Lehman']"
] |
null | null | 2406.05510 | null | null | http://arxiv.org/pdf/2406.05510v1 | 2024-06-08T16:19:18Z | 2024-06-08T16:19:18Z | Representation Learning with Conditional Information Flow Maximization | This paper proposes an information-theoretic representation learning framework, named conditional information flow maximization, to extract noise-invariant sufficient representations for the input data and target task. It promotes the learned representations have good feature uniformity and sufficient predictive ability, which can enhance the generalization of pre-trained language models (PLMs) for the target task. Firstly, an information flow maximization principle is proposed to learn more sufficient representations by simultaneously maximizing both input-representation and representation-label mutual information. In contrast to information bottleneck, we handle the input-representation information in an opposite way to avoid the over-compression issue of latent representations. Besides, to mitigate the negative effect of potential redundant features, a conditional information minimization principle is designed to eliminate negative redundant features while preserve noise-invariant features from the input. Experiments on 13 language understanding benchmarks demonstrate that our method effectively improves the performance of PLMs for classification and regression. Extensive experiments show that the learned representations are more sufficient, robust and transferable. | [
"['Dou Hu' 'Lingwei Wei' 'Wei Zhou' 'Songlin Hu']"
] |
null | null | 2406.05516 | null | null | http://arxiv.org/pdf/2406.05516v1 | 2024-06-08T16:35:31Z | 2024-06-08T16:35:31Z | Verbalized Probabilistic Graphical Modeling with Large Language Models | Faced with complex problems, the human brain demonstrates a remarkable capacity to transcend sensory input and form latent understandings of perceived world patterns. However, this cognitive capacity is not explicitly considered or encoded in current large language models (LLMs). As a result, LLMs often struggle to capture latent structures and model uncertainty in complex compositional reasoning tasks. This work introduces a novel Bayesian prompting approach that facilitates training-free Bayesian inference with LLMs by using a verbalized Probabilistic Graphical Model (PGM). While traditional Bayesian approaches typically depend on extensive data and predetermined mathematical structures for learning latent factors and dependencies, our approach efficiently reasons latent variables and their probabilistic dependencies by prompting LLMs to adhere to Bayesian principles. We evaluated our model on several compositional reasoning tasks, both close-ended and open-ended. Our results indicate that the model effectively enhances confidence elicitation and text generation quality, demonstrating its potential to improve AI language understanding systems, especially in modeling uncertainty. | [
"['Hengguan Huang' 'Xing Shen' 'Songtao Wang' 'Dianbo Liu' 'Hao Wang']"
] |
null | null | 2406.05531 | null | null | http://arxiv.org/pdf/2406.05531v1 | 2024-06-08T17:25:31Z | 2024-06-08T17:25:31Z | Enhancing Adversarial Transferability via Information Bottleneck
Constraints | From the perspective of information bottleneck (IB) theory, we propose a novel framework for performing black-box transferable adversarial attacks named IBTA, which leverages advancements in invariant features. Intuitively, diminishing the reliance of adversarial perturbations on the original data, under equivalent attack performance constraints, encourages a greater reliance on invariant features that contributes most to classification, thereby enhancing the transferability of adversarial attacks. Building on this motivation, we redefine the optimization of transferable attacks using a novel theoretical framework that centers around IB. Specifically, to overcome the challenge of unoptimizable mutual information, we propose a simple and efficient mutual information lower bound (MILB) for approximating computation. Moreover, to quantitatively evaluate mutual information, we utilize the Mutual Information Neural Estimator (MINE) to perform a thorough analysis. Our experiments on the ImageNet dataset well demonstrate the efficiency and scalability of IBTA and derived MILB. Our code is available at https://github.com/Biqing-Qi/Enhancing-Adversarial-Transferability-via-Information-Bottleneck-Constraints. | [
"['Biqing Qi' 'Junqi Gao' 'Jianxing Liu' 'Ligang Wu' 'Bowen Zhou']"
] |
null | null | 2406.05532 | null | null | http://arxiv.org/pdf/2406.05532v1 | 2024-06-08T17:25:48Z | 2024-06-08T17:25:48Z | Exploring Adversarial Robustness of Deep State Space Models | Deep State Space Models (SSMs) have proven effective in numerous task scenarios but face significant security challenges due to Adversarial Perturbations (APs) in real-world deployments. Adversarial Training (AT) is a mainstream approach to enhancing Adversarial Robustness (AR) and has been validated on various traditional DNN architectures. However, its effectiveness in improving the AR of SSMs remains unclear. While many enhancements in SSM components, such as integrating Attention mechanisms and expanding to data-dependent SSM parameterizations, have brought significant gains in Standard Training (ST) settings, their potential benefits in AT remain unexplored. To investigate this, we evaluate existing structural variants of SSMs with AT to assess their AR performance. We observe that pure SSM structures struggle to benefit from AT, whereas incorporating Attention yields a markedly better trade-off between robustness and generalization for SSMs in AT compared to other components. Nonetheless, the integration of Attention also leads to Robust Overfitting (RO) issues. To understand these phenomena, we empirically and theoretically analyze the output error of SSMs under AP. We find that fixed-parameterized SSMs have output error bounds strictly related to their parameters, limiting their AT benefits, while input-dependent SSMs may face the problem of error explosion. Furthermore, we show that the Attention component effectively scales the output error of SSMs during training, enabling them to benefit more from AT, but at the cost of introducing RO due to its high model complexity. Inspired by this, we propose a simple and effective Adaptive Scaling (AdS) mechanism that brings AT performance close to Attention-integrated SSMs without introducing the issue of RO. | [
"['Biqing Qi' 'Yang Luo' 'Junqi Gao' 'Pengfei Li' 'Kai Tian' 'Zhiyuan Ma'\n 'Bowen Zhou']"
] |
null | null | 2406.05533 | null | null | http://arxiv.org/pdf/2406.05533v1 | 2024-06-08T17:27:27Z | 2024-06-08T17:27:27Z | PAPR in Motion: Seamless Point-level 3D Scene Interpolation | We propose the problem of point-level 3D scene interpolation, which aims to simultaneously reconstruct a 3D scene in two states from multiple views, synthesize smooth point-level interpolations between them, and render the scene from novel viewpoints, all without any supervision between the states. The primary challenge is on achieving a smooth transition between states that may involve significant and non-rigid changes. To address these challenges, we introduce "PAPR in Motion", a novel approach that builds upon the recent Proximity Attention Point Rendering (PAPR) technique, which can deform a point cloud to match a significantly different shape and render a visually coherent scene even after non-rigid deformations. Our approach is specifically designed to maintain the temporal consistency of the geometric structure by introducing various regularization techniques for PAPR. The result is a method that can effectively bridge large scene changes and produce visually coherent and temporally smooth interpolations in both geometry and appearance. Evaluation across diverse motion types demonstrates that "PAPR in Motion" outperforms the leading neural renderer for dynamic scenes. For more results and code, please visit our project website at https://niopeng.github.io/PAPR-in-Motion/ . | [
"['Shichong Peng' 'Yanshu Zhang' 'Ke Li']"
] |
null | null | 2406.05534 | null | null | http://arxiv.org/pdf/2406.05534v1 | 2024-06-08T17:30:54Z | 2024-06-08T17:30:54Z | Online DPO: Online Direct Preference Optimization with Fast-Slow Chasing | Direct Preference Optimization (DPO) improves the alignment of large language models (LLMs) with human values by training directly on human preference datasets, eliminating the need for reward models. However, due to the presence of cross-domain human preferences, direct continual training can lead to catastrophic forgetting, limiting DPO's performance and efficiency. Inspired by intraspecific competition driving species evolution, we propose a Online Fast-Slow chasing DPO (OFS-DPO) for preference alignment, simulating competition through fast and slow chasing among models to facilitate rapid adaptation. Specifically, we first derive the regret upper bound for online learning, validating our motivation with a min-max optimization pattern. Based on this, we introduce two identical modules using Low-rank Adaptive (LoRA) with different optimization speeds to simulate intraspecific competition, and propose a new regularization term to guide their learning. To further mitigate catastrophic forgetting in cross-domain scenarios, we extend the OFS-DPO with LoRA modules combination strategy, resulting in the Cross domain Online Fast-Slow chasing DPO (COFS-DPO). This method leverages linear combinations of fast modules parameters from different task domains, fully utilizing historical information to achive continual value alignment. Experimental results show that OFS-DPO outperforms DPO in in-domain alignment, while COFS-DPO excels in cross-domain continual learning scenarios. | [
"['Biqing Qi' 'Pengfei Li' 'Fangyuan Li' 'Junqi Gao' 'Kaiyan Zhang'\n 'Bowen Zhou']"
] |
null | null | 2406.05535 | null | null | http://arxiv.org/pdf/2406.05535v1 | 2024-06-08T17:33:23Z | 2024-06-08T17:33:23Z | Perturbation Towards Easy Samples Improves Targeted Adversarial
Transferability | The transferability of adversarial perturbations provides an effective shortcut for black-box attacks. Targeted perturbations have greater practicality but are more difficult to transfer between models. In this paper, we experimentally and theoretically demonstrated that neural networks trained on the same dataset have more consistent performance in High-Sample-Density-Regions (HSDR) of each class instead of low sample density regions. Therefore, in the target setting, adding perturbations towards HSDR of the target class is more effective in improving transferability. However, density estimation is challenging in high-dimensional scenarios. Further theoretical and experimental verification demonstrates that easy samples with low loss are more likely to be located in HSDR. Perturbations towards such easy samples in the target class can avoid density estimation for HSDR location. Based on the above facts, we verified that adding perturbations to easy samples in the target class improves targeted adversarial transferability of existing attack methods. A generative targeted attack strategy named Easy Sample Matching Attack (ESMA) is proposed, which has a higher success rate for targeted attacks and outperforms the SOTA generative method. Moreover, ESMA requires only 5% of the storage space and much less computation time comparing to the current SOTA, as ESMA attacks all classes with only one model instead of seperate models for each class. Our code is available at https://github.com/gjq100/ESMA. | [
"['Junqi Gao' 'Biqing Qi' 'Yao Li' 'Zhichang Guo' 'Dong Li' 'Yuming Xing'\n 'Dazhi Zhang']"
] |
null | null | 2406.05540 | null | null | http://arxiv.org/pdf/2406.05540v2 | 2024-07-08T16:39:35Z | 2024-06-08T18:11:30Z | A Fine-tuning Dataset and Benchmark for Large Language Models for
Protein Understanding | The parallels between protein sequences and natural language in their sequential structures have inspired the application of large language models (LLMs) to protein understanding. Despite the success of LLMs in NLP, their effectiveness in comprehending protein sequences remains an open question, largely due to the absence of datasets linking protein sequences to descriptive text. Researchers have then attempted to adapt LLMs for protein understanding by integrating a protein sequence encoder with a pre-trained LLM. However, this adaptation raises a fundamental question: "Can LLMs, originally designed for NLP, effectively comprehend protein sequences as a form of language?" Current datasets fall short in addressing this question due to the lack of a direct correlation between protein sequences and corresponding text descriptions, limiting the ability to train and evaluate LLMs for protein understanding effectively. To bridge this gap, we introduce ProteinLMDataset, a dataset specifically designed for further self-supervised pretraining and supervised fine-tuning (SFT) of LLMs to enhance their capability for protein sequence comprehension. Specifically, ProteinLMDataset includes 17.46 billion tokens for pretraining and 893,000 instructions for SFT. Additionally, we present ProteinLMBench, the first benchmark dataset consisting of 944 manually verified multiple-choice questions for assessing the protein understanding capabilities of LLMs. ProteinLMBench incorporates protein-related details and sequences in multiple languages, establishing a new standard for evaluating LLMs' abilities in protein comprehension. The large language model InternLM2-7B, pretrained and fine-tuned on the ProteinLMDataset, outperforms GPT-4 on ProteinLMBench, achieving the highest accuracy score. | [
"['Yiqing Shen' 'Zan Chen' 'Michail Mamalakis' 'Luhan He' 'Haiyang Xia'\n 'Tianbin Li' 'Yanzhou Su' 'Junjun He' 'Yu Guang Wang']"
] |
null | null | 2406.05545 | null | null | http://arxiv.org/pdf/2406.05545v1 | 2024-06-08T18:21:12Z | 2024-06-08T18:21:12Z | Privacy-Preserving Optimal Parameter Selection for Collaborative
Clustering | This study investigates the optimal selection of parameters for collaborative clustering while ensuring data privacy. We focus on key clustering algorithms within a collaborative framework, where multiple data owners combine their data. A semi-trusted server assists in recommending the most suitable clustering algorithm and its parameters. Our findings indicate that the privacy parameter ($epsilon$) minimally impacts the server's recommendations, but an increase in $epsilon$ raises the risk of membership inference attacks, where sensitive information might be inferred. To mitigate these risks, we implement differential privacy techniques, particularly the Randomized Response mechanism, to add noise and protect data privacy. Our approach demonstrates that high-quality clustering can be achieved while maintaining data confidentiality, as evidenced by metrics such as the Adjusted Rand Index and Silhouette Score. This study contributes to privacy-aware data sharing, optimal algorithm and parameter selection, and effective communication between data owners and the server. | [
"['Maryam Ghasemian' 'Erman Ayday']"
] |
null | null | 2406.05551 | null | null | http://arxiv.org/pdf/2406.05551v1 | 2024-06-08T18:57:13Z | 2024-06-08T18:57:13Z | Autoregressive Diffusion Transformer for Text-to-Speech Synthesis | Audio language models have recently emerged as a promising approach for various audio generation tasks, relying on audio tokenizers to encode waveforms into sequences of discrete symbols. Audio tokenization often poses a necessary compromise between code bitrate and reconstruction accuracy. When dealing with low-bitrate audio codes, language models are constrained to process only a subset of the information embedded in the audio, which in turn restricts their generative capabilities. To circumvent these issues, we propose encoding audio as vector sequences in continuous space $mathbb R^d$ and autoregressively generating these sequences using a decoder-only diffusion transformer (ARDiT). Our findings indicate that ARDiT excels in zero-shot text-to-speech and exhibits performance that compares to or even surpasses that of state-of-the-art models. High-bitrate continuous speech representation enables almost flawless reconstruction, allowing our model to achieve nearly perfect speech editing. Our experiments reveal that employing Integral Kullback-Leibler (IKL) divergence for distillation at each autoregressive step significantly boosts the perceived quality of the samples. Simultaneously, it condenses the iterative sampling process of the diffusion model into a single step. Furthermore, ARDiT can be trained to predict several continuous vectors in one step, significantly reducing latency during sampling. Impressively, one of our models can generate $170$ ms of $24$ kHz speech per evaluation step with minimal degradation in performance. Audio samples are available at http://ardit-tts.github.io/ . | [
"['Zhijun Liu' 'Shuai Wang' 'Sho Inoue' 'Qibing Bai' 'Haizhou Li']"
] |
null | null | 2406.05564 | null | null | http://arxiv.org/pdf/2406.05564v1 | 2024-06-08T20:07:24Z | 2024-06-08T20:07:24Z | Automata Extraction from Transformers | In modern machine (ML) learning systems, Transformer-based architectures have achieved milestone success across a broad spectrum of tasks, yet understanding their operational mechanisms remains an open problem. To improve the transparency of ML systems, automata extraction methods, which interpret stateful ML models as automata typically through formal languages, have proven effective for explaining the mechanism of recurrent neural networks (RNNs). However, few works have been applied to this paradigm to Transformer models. In particular, understanding their processing of formal languages and identifying their limitations in this area remains unexplored. In this paper, we propose an automata extraction algorithm specifically designed for Transformer models. Treating the Transformer model as a black-box system, we track the model through the transformation process of their internal latent representations during their operations, and then use classical pedagogical approaches like L* algorithm to interpret them as deterministic finite-state automata (DFA). Overall, our study reveals how the Transformer model comprehends the structure of formal languages, which not only enhances the interpretability of the Transformer-based ML systems but also marks a crucial step toward a deeper understanding of how ML systems process formal languages. Code and data are available at https://github.com/Zhang-Yihao/Transfomer2DFA. | [
"['Yihao Zhang' 'Zeming Wei' 'Meng Sun']"
] |
null | null | 2406.05588 | null | null | http://arxiv.org/pdf/2406.05588v1 | 2024-06-08T22:17:52Z | 2024-06-08T22:17:52Z | CERET: Cost-Effective Extrinsic Refinement for Text Generation | Large Language Models (LLMs) are powerful models for generation tasks, but they may not generate good quality outputs in their first attempt. Apart from model fine-tuning, existing approaches to improve prediction accuracy and quality typically involve LLM self-improvement / self-reflection that incorporate feedback from models themselves. Despite their effectiveness, these methods are hindered by their high computational cost and lack of scalability. In this work, we propose CERET, a method for refining text generations by considering semantic stability, entailment and inter-sample uncertainty measures. Experimental results show that CERET outperforms Self-consistency and Self-rerank baselines consistently under various task setups, by ~1.6% in Rouge-1 for abstractive summarization and ~3.5% in hit rate for question answering. Compared to LLM Self-rerank method, our approach only requires 9.4% of its latency and is more cost-effective. | [
"['Jason Cai' 'Hang Su' 'Monica Sunkara' 'Igor Shalyminov' 'Saab Mansour']"
] |
null | null | 2406.05590 | null | null | http://arxiv.org/pdf/2406.05590v1 | 2024-06-08T22:21:42Z | 2024-06-08T22:21:42Z | NYU CTF Dataset: A Scalable Open-Source Benchmark Dataset for Evaluating
LLMs in Offensive Security | Large Language Models (LLMs) are being deployed across various domains today. However, their capacity to solve Capture the Flag (CTF) challenges in cybersecurity has not been thoroughly evaluated. To address this, we develop a novel method to assess LLMs in solving CTF challenges by creating a scalable, open-source benchmark database specifically designed for these applications. This database includes metadata for LLM testing and adaptive learning, compiling a diverse range of CTF challenges from popular competitions. Utilizing the advanced function calling capabilities of LLMs, we build a fully automated system with an enhanced workflow and support for external tool calls. Our benchmark dataset and automated framework allow us to evaluate the performance of five LLMs, encompassing both black-box and open-source models. This work lays the foundation for future research into improving the efficiency of LLMs in interactive cybersecurity tasks and automated task planning. By providing a specialized dataset, our project offers an ideal platform for developing, testing, and refining LLM-based approaches to vulnerability detection and resolution. Evaluating LLMs on these challenges and comparing with human performance yields insights into their potential for AI-driven cybersecurity solutions to perform real-world threat management. We make our dataset open source to public https://github.com/NYU-LLM-CTF/LLM_CTF_Database along with our playground automated framework https://github.com/NYU-LLM-CTF/llm_ctf_automation. | [
"['Minghao Shao' 'Sofija Jancheska' 'Meet Udeshi' 'Brendan Dolan-Gavitt'\n 'Haoran Xi' 'Kimberly Milner' 'Boyuan Chen' 'Max Yin' 'Siddharth Garg'\n 'Prashanth Krishnamurthy' 'Farshad Khorrami' 'Ramesh Karri'\n 'Muhammad Shafique']"
] |
null | null | 2406.05596 | null | null | http://arxiv.org/pdf/2406.05596v1 | 2024-06-08T23:23:28Z | 2024-06-08T23:23:28Z | Aligning Human Knowledge with Visual Concepts Towards Explainable
Medical Image Classification | Although explainability is essential in the clinical diagnosis, most deep learning models still function as black boxes without elucidating their decision-making process. In this study, we investigate the explainable model development that can mimic the decision-making process of human experts by fusing the domain knowledge of explicit diagnostic criteria. We introduce a simple yet effective framework, Explicd, towards Explainable language-informed criteria-based diagnosis. Explicd initiates its process by querying domain knowledge from either large language models (LLMs) or human experts to establish diagnostic criteria across various concept axes (e.g., color, shape, texture, or specific patterns of diseases). By leveraging a pretrained vision-language model, Explicd injects these criteria into the embedding space as knowledge anchors, thereby facilitating the learning of corresponding visual concepts within medical images. The final diagnostic outcome is determined based on the similarity scores between the encoded visual concepts and the textual criteria embeddings. Through extensive evaluation of five medical image classification benchmarks, Explicd has demonstrated its inherent explainability and extends to improve classification performance compared to traditional black-box models. | [
"['Yunhe Gao' 'Difei Gu' 'Mu Zhou' 'Dimitris Metaxas']"
] |
null | null | 2406.05605 | null | null | http://arxiv.org/pdf/2406.05605v1 | 2024-06-09T01:12:41Z | 2024-06-09T01:12:41Z | Deep Learning to Predict Glaucoma Progression using Structural Changes
in the Eye | Glaucoma is a chronic eye disease characterized by optic neuropathy, leading to irreversible vision loss. It progresses gradually, often remaining undiagnosed until advanced stages. Early detection is crucial to monitor atrophy and develop treatment strategies to prevent further vision impairment. Data-centric methods have enabled computer-aided algorithms for precise glaucoma diagnosis. In this study, we use deep learning models to identify complex disease traits and progression criteria, detecting subtle changes indicative of glaucoma. We explore the structure-function relationship in glaucoma progression and predict functional impairment from structural eye deterioration. We analyze statistical and machine learning methods, including deep learning techniques with optical coherence tomography (OCT) scans for accurate progression prediction. Addressing challenges like age variability, data imbalances, and noisy labels, we develop novel semi-supervised time-series algorithms: 1. Weakly-Supervised Time-Series Learning: We create a CNN-LSTM model to encode spatiotemporal features from OCT scans. This approach uses age-related progression and positive-unlabeled data to establish robust pseudo-progression criteria, bypassing gold-standard labels. 2. Semi-Supervised Time-Series Learning: Using labels from Guided Progression Analysis (GPA) in a contrastive learning scheme, the CNN-LSTM architecture learns from potentially mislabeled data to improve prediction accuracy. Our methods outperform conventional and state-of-the-art techniques. | [
"['Sayan Mandal']"
] |
null | null | 2406.05612 | null | null | http://arxiv.org/pdf/2406.05612v2 | 2024-06-29T12:26:42Z | 2024-06-09T02:01:25Z | Which Backbone to Use: A Resource-efficient Domain Specific Comparison
for Computer Vision | In contemporary computer vision applications, particularly image classification, architectural backbones pre-trained on large datasets like ImageNet are commonly employed as feature extractors. Despite the widespread use of these pre-trained convolutional neural networks (CNNs), there remains a gap in understanding the performance of various resource-efficient backbones across diverse domains and dataset sizes. Our study systematically evaluates multiple lightweight, pre-trained CNN backbones under consistent training settings across a variety of datasets, including natural images, medical images, galaxy images, and remote sensing images. This comprehensive analysis aims to aid machine learning practitioners in selecting the most suitable backbone for their specific problem, especially in scenarios involving small datasets where fine-tuning a pre-trained network is crucial. Even though attention-based architectures are gaining popularity, we observed that they tend to perform poorly under low data finetuning tasks compared to CNNs. We also observed that some CNN architectures such as ConvNeXt, RegNet and EfficientNet performs well compared to others on a diverse set of domains consistently. Our findings provide actionable insights into the performance trade-offs and effectiveness of different backbones, facilitating informed decision-making in model selection for a broad spectrum of computer vision domains. Our code is available here: https://github.com/pranavphoenix/Backbones | [
"['Pranav Jeevan' 'Amit Sethi']"
] |
null | null | 2406.05616 | null | null | http://arxiv.org/pdf/2406.05616v1 | 2024-06-09T02:38:52Z | 2024-06-09T02:38:52Z | Domain Agnostic Conditional Invariant Predictions for Domain
Generalization | Domain generalization aims to develop a model that can perform well on unseen target domains by learning from multiple source domains. However, recent-proposed domain generalization models usually rely on domain labels, which may not be available in many real-world scenarios. To address this challenge, we propose a Discriminant Risk Minimization (DRM) theory and the corresponding algorithm to capture the invariant features without domain labels. In DRM theory, we prove that reducing the discrepancy of prediction distribution between overall source domain and any subset of it can contribute to obtaining invariant features. To apply the DRM theory, we develop an algorithm which is composed of Bayesian inference and a new penalty termed as Categorical Discriminant Risk (CDR). In Bayesian inference, we transform the output of the model into a probability distribution to align with our theoretical assumptions. We adopt sliding update approach to approximate the overall prediction distribution of the model, which enables us to obtain CDR penalty. We also indicate the effectiveness of these components in finding invariant features. We evaluate our algorithm against various domain generalization methods on multiple real-world datasets, providing empirical support for our theory. | [
"['Zongbin Wang' 'Bin Pan' 'Zhenwei Shi']"
] |
null | null | 2406.05628 | null | null | http://arxiv.org/pdf/2406.05628v1 | 2024-06-09T03:32:32Z | 2024-06-09T03:32:32Z | Domain Generalization Guided by Large-Scale Pre-Trained Priors | Domain generalization (DG) aims to train a model from limited source domains, allowing it to generalize to unknown target domains. Typically, DG models only employ large-scale pre-trained models during the initialization of fine-tuning. However, large-scale pre-trained models already possess the ability to resist domain shift. If we reference pre-trained models continuously during fine-tuning to maintain this ability, it could further enhance the generalization ability of the DG model. For this purpose, we introduce a new method called Fine-Tune with Large-scale pre-trained Priors (FT-LP), which incorporates the pre-trained model as a prior into the DG fine-tuning process, ensuring that the model refers to its pre-trained model at each optimization step. FT-LP comprises a theoretical framework and a simple implementation strategy. In theory, we verify the rationality of FT-LP by introducing a generalization error bound with the pre-trained priors for DG. In implementation, we utilize an encoder to simulate the model distribution, enabling the use of FT-LP when only pre-trained weights are available. In summary, we offer a new fine-tuning method for DG algorithms to utilize pre-trained models throughout the fine-tuning process. Through experiments on various datasets and DG models, our proposed method exhibits significant improvements, indicating its effectiveness. | [
"['Zongbin Wang' 'Bin Pan' 'Shiyu Shen' 'Tianyang Shi' 'Zhenwei Shi']"
] |
null | null | 2406.05629 | null | null | http://arxiv.org/pdf/2406.05629v1 | 2024-06-09T03:38:21Z | 2024-06-09T03:38:21Z | Separating the "Chirp" from the "Chat": Self-supervised Visual Grounding
of Sound and Language | We present DenseAV, a novel dual encoder grounding architecture that learns high-resolution, semantically meaningful, and audio-visually aligned features solely through watching videos. We show that DenseAV can discover the ``meaning'' of words and the ``location'' of sounds without explicit localization supervision. Furthermore, it automatically discovers and distinguishes between these two types of associations without supervision. We show that DenseAV's localization abilities arise from a new multi-head feature aggregation operator that directly compares dense image and audio representations for contrastive learning. In contrast, many other systems that learn ``global'' audio and video representations cannot localize words and sound. Finally, we contribute two new datasets to improve the evaluation of AV representations through speech and sound prompted semantic segmentation. On these and other datasets we show DenseAV dramatically outperforms the prior art on speech and sound prompted semantic segmentation. DenseAV outperforms the previous state-of-the-art, ImageBind, on cross-modal retrieval using fewer than half of the parameters. Project Page: href{https://aka.ms/denseav}{https://aka.ms/denseav} | [
"['Mark Hamilton' 'Andrew Zisserman' 'John R. Hershey' 'William T. Freeman']"
] |
null | null | 2406.05631 | null | null | http://arxiv.org/pdf/2406.05631v1 | 2024-06-09T03:52:21Z | 2024-06-09T03:52:21Z | CCSI: Continual Class-Specific Impression for Data-free Class
Incremental Learning | In real-world clinical settings, traditional deep learning-based classification methods struggle with diagnosing newly introduced disease types because they require samples from all disease classes for offline training. Class incremental learning offers a promising solution by adapting a deep network trained on specific disease classes to handle new diseases. However, catastrophic forgetting occurs, decreasing the performance of earlier classes when adapting the model to new data. Prior proposed methodologies to overcome this require perpetual storage of previous samples, posing potential practical concerns regarding privacy and storage regulations in healthcare. To this end, we propose a novel data-free class incremental learning framework that utilizes data synthesis on learned classes instead of data storage from previous classes. Our key contributions include acquiring synthetic data known as Continual Class-Specific Impression (CCSI) for previously inaccessible trained classes and presenting a methodology to effectively utilize this data for updating networks when introducing new classes. We obtain CCSI by employing data inversion over gradients of the trained classification model on previous classes starting from the mean image of each class inspired by common landmarks shared among medical images and utilizing continual normalization layers statistics as a regularizer in this pixel-wise optimization process. Subsequently, we update the network by combining the synthesized data with new class data and incorporate several losses, including an intra-domain contrastive loss to generalize the deep network trained on the synthesized data to real data, a margin loss to increase separation among previous classes and new ones, and a cosine-normalized cross-entropy loss to alleviate the adverse effects of imbalanced distributions in training data. | [
"['Sana Ayromlou' 'Teresa Tsang' 'Purang Abolmaesumi' 'Xiaoxiao Li']"
] |
null | null | 2406.05633 | null | null | http://arxiv.org/pdf/2406.05633v1 | 2024-06-09T04:02:08Z | 2024-06-09T04:02:08Z | Heterogeneous Treatment Effects in Panel Data | We address a core problem in causal inference: estimating heterogeneous treatment effects using panel data with general treatment patterns. Many existing methods either do not utilize the potential underlying structure in panel data or have limitations in the allowable treatment patterns. In this work, we propose and evaluate a new method that first partitions observations into disjoint clusters with similar treatment effects using a regression tree, and then leverages the (assumed) low-rank structure of the panel data to estimate the average treatment effect for each cluster. Our theoretical results establish the convergence of the resulting estimates to the true treatment effects. Computation experiments with semi-synthetic data show that our method achieves superior accuracy compared to alternative approaches, using a regression tree with no more than 40 leaves. Hence, our method provides more accurate and interpretable estimates than alternative methods. | [
"['Retsef Levi' 'Elisabeth Paulson' 'Georgia Perakis' 'Emily Zhang']"
] |
null | null | 2406.05636 | null | null | http://arxiv.org/pdf/2406.05636v1 | 2024-06-09T04:11:41Z | 2024-06-09T04:11:41Z | What is my quantum computer good for? Quantum capability learning with
physics-aware neural networks | Quantum computers have the potential to revolutionize diverse fields, including quantum chemistry, materials science, and machine learning. However, contemporary quantum computers experience errors that often cause quantum programs run on them to fail. Until quantum computers can reliably execute large quantum programs, stakeholders will need fast and reliable methods for assessing a quantum computer's capability-i.e., the programs it can run and how well it can run them. Previously, off-the-shelf neural network architectures have been used to model quantum computers' capabilities, but with limited success, because these networks fail to learn the complex quantum physics that determines real quantum computers' errors. We address this shortcoming with a new quantum-physics-aware neural network architecture for learning capability models. Our architecture combines aspects of graph neural networks with efficient approximations to the physics of errors in quantum programs. This approach achieves up to $sim50%$ reductions in mean absolute error on both experimental and simulated data, over state-of-the-art models based on convolutional neural networks. | [
"['Daniel Hothem' 'Ashe Miller' 'Timothy Proctor']"
] |
null | null | 2406.05637 | null | null | http://arxiv.org/pdf/2406.05637v1 | 2024-06-09T04:25:10Z | 2024-06-09T04:25:10Z | A Generalized Version of Chung's Lemma and its Applications | Chung's lemma is a classical tool for establishing asymptotic convergence rates of (stochastic) optimization methods under strong convexity-type assumptions and appropriate polynomial diminishing step sizes. In this work, we develop a generalized version of Chung's lemma, which provides a simple non-asymptotic convergence framework for a more general family of step size rules. We demonstrate broad applicability of the proposed generalized Chung's lemma by deriving tight non-asymptotic convergence rates for a large variety of stochastic methods. In particular, we obtain partially new non-asymptotic complexity results for stochastic optimization methods, such as stochastic gradient descent and random reshuffling, under a general $(theta,mu)$-Polyak-Lojasiewicz (PL) condition and for various step sizes strategies, including polynomial, constant, exponential, and cosine step sizes rules. Notably, as a by-product of our analysis, we observe that exponential step sizes can adapt to the objective function's geometry, achieving the optimal convergence rate without requiring exact knowledge of the underlying landscape. Our results demonstrate that the developed variant of Chung's lemma offers a versatile, systematic, and streamlined approach to establish non-asymptotic convergence rates under general step size rules. | [
"['Li Jiang' 'Xiao Li' 'Andre Milzarek' 'Junwen Qiu']"
] |
null | null | 2406.05645 | null | null | http://arxiv.org/pdf/2406.05645v1 | 2024-06-09T05:07:39Z | 2024-06-09T05:07:39Z | Anomaly Multi-classification in Industrial Scenarios: Transferring
Few-shot Learning to a New Task | In industrial scenarios, it is crucial not only to identify anomalous items but also to classify the type of anomaly. However, research on anomaly multi-classification remains largely unexplored. This paper proposes a novel and valuable research task called anomaly multi-classification. Given the challenges in applying few-shot learning to this task, due to limited training data and unique characteristics of anomaly images, we introduce a baseline model that combines RelationNet and PatchCore. We propose a data generation method that creates pseudo classes and a corresponding proxy task, aiming to bridge the gap in transferring few-shot learning to industrial scenarios. Furthermore, we utilize contrastive learning to improve the vanilla baseline, achieving much better performance than directly fine-tune a ResNet. Experiments conducted on MvTec AD and MvTec3D AD demonstrate that our approach shows superior performance in this novel task. | [
"['Jie Liu' 'Yao Wu' 'Xiaotong Luo' 'Zongze Wu']"
] |
null | null | 2406.05646 | null | null | http://arxiv.org/pdf/2406.05646v1 | 2024-06-09T05:11:00Z | 2024-06-09T05:11:00Z | ICU-Sepsis: A Benchmark MDP Built from Real Medical Data | We present ICU-Sepsis, an environment that can be used in benchmarks for evaluating reinforcement learning (RL) algorithms. Sepsis management is a complex task that has been an important topic in applied RL research in recent years. Therefore, MDPs that model sepsis management can serve as part of a benchmark to evaluate RL algorithms on a challenging real-world problem. However, creating usable MDPs that simulate sepsis care in the ICU remains a challenge due to the complexities involved in acquiring and processing patient data. ICU-Sepsis is a lightweight environment that models personalized care of sepsis patients in the ICU. The environment is a tabular MDP that is widely compatible and is challenging even for state-of-the-art RL algorithms, making it a valuable tool for benchmarking their performance. However, we emphasize that while ICU-Sepsis provides a standardized environment for evaluating RL algorithms, it should not be used to draw conclusions that guide medical practice. | [
"['Kartik Choudhary' 'Dhawal Gupta' 'Philip S. Thomas']"
] |
null | null | 2406.05660 | null | null | http://arxiv.org/pdf/2406.05660v1 | 2024-06-09T06:26:21Z | 2024-06-09T06:26:21Z | Injecting Undetectable Backdoors in Deep Learning and Language Models | As ML models become increasingly complex and integral to high-stakes domains such as finance and healthcare, they also become more susceptible to sophisticated adversarial attacks. We investigate the threat posed by undetectable backdoors in models developed by insidious external expert firms. When such backdoors exist, they allow the designer of the model to sell information to the users on how to carefully perturb the least significant bits of their input to change the classification outcome to a favorable one. We develop a general strategy to plant a backdoor to neural networks while ensuring that even if the model's weights and architecture are accessible, the existence of the backdoor is still undetectable. To achieve this, we utilize techniques from cryptography such as cryptographic signatures and indistinguishability obfuscation. We further introduce the notion of undetectable backdoors to language models and extend our neural network backdoor attacks to such models based on the existence of steganographic functions. | [
"['Alkis Kalavasis' 'Amin Karbasi' 'Argyris Oikonomou' 'Katerina Sotiraki'\n 'Grigoris Velegkas' 'Manolis Zampetakis']"
] |
null | null | 2406.05666 | null | null | http://arxiv.org/pdf/2406.05666v4 | 2024-06-26T12:32:28Z | 2024-06-09T06:49:22Z | General Distribution Learning: A theoretical framework for Deep Learning | There remain numerous unanswered research questions on deep learning (DL) within the classical learning theory framework. These include the remarkable generalization capabilities of overparametrized neural networks (NNs), the efficient optimization performance despite non-convexity of objectives, the mechanism of flat minima for generalization, and the exceptional performance of deep architectures in solving physical problems. This paper introduces General Distribution Learning (GD Learning), a novel theoretical learning framework designed to address a comprehensive range of machine learning and statistical tasks, including classification, regression and parameter estimation. Departing from traditional statistical machine learning, GD Learning focuses on the true underlying distribution. In GD Learning, learning error, corresponding to the expected error in classical statistical learning framework, is divided into fitting errors due to models and algorithms, as well as sampling errors introduced by limited sampling data. The framework significantly incorporates prior knowledge, especially in scenarios characterized by data scarcity, thereby enhancing performance. Within the GD Learning framework, we demonstrate that the global optimal solutions in non-convex optimization can be approached by minimizing the gradient norm and the non-uniformity of the eigenvalues of the model's Jacobian matrix. This insight leads to the development of the gradient structure control algorithm. GD Learning also offers fresh insights into the questions on deep learning, including overparameterization and non-convex optimization, bias-variance trade-off, and the mechanism of flat minima. | [
"['Binchuan Qi' 'Li Li' 'Wei Gong']"
] |
null | null | 2406.05670 | null | null | http://arxiv.org/pdf/2406.05670v1 | 2024-06-09T06:59:46Z | 2024-06-09T06:59:46Z | Certified Robustness to Data Poisoning in Gradient-Based Training | Modern machine learning pipelines leverage large amounts of public data, making it infeasible to guarantee data quality and leaving models open to poisoning and backdoor attacks. However, provably bounding model behavior under such attacks remains an open problem. In this work, we address this challenge and develop the first framework providing provable guarantees on the behavior of models trained with potentially manipulated data. In particular, our framework certifies robustness against untargeted and targeted poisoning as well as backdoor attacks for both input and label manipulations. Our method leverages convex relaxations to over-approximate the set of all possible parameter updates for a given poisoning threat model, allowing us to bound the set of all reachable parameters for any gradient-based learning algorithm. Given this set of parameters, we provide bounds on worst-case behavior, including model performance and backdoor success rate. We demonstrate our approach on multiple real-world datasets from applications including energy consumption, medical imaging, and autonomous driving. | [
"['Philip Sosnin' 'Mark N. Müller' 'Maximilian Baader' 'Calvin Tsay'\n 'Matthew Wicker']"
] |
null | null | 2406.05682 | null | null | http://arxiv.org/pdf/2406.05682v1 | 2024-06-09T07:41:03Z | 2024-06-09T07:41:03Z | From Basic to Extra Features: Hypergraph Transformer
Pretrain-then-Finetuning for Balanced Clinical Predictions on EHR | Electronic Health Records (EHRs) contain rich patient information and are crucial for clinical research and practice. In recent years, deep learning models have been applied to EHRs, but they often rely on massive features, which may not be readily available for all patients. We propose HTP-Star, which leverages hypergraph structures with a pretrain-then-finetune framework for modeling EHR data, enabling seamless integration of additional features. Additionally, we design two techniques, namely (1) Smoothness-inducing Regularization and (2) Group-balanced Reweighting, to enhance the model's robustness during fine-tuning. Through experiments conducted on two real EHR datasets, we demonstrate that HTP-Star consistently outperforms various baselines while striking a balance between patients with basic and extra features. | [
"['Ran Xu' 'Yiwen Lu' 'Chang Liu' 'Yong Chen' 'Yan Sun' 'Xiao Hu'\n 'Joyce C Ho' 'Carl Yang']"
] |
null | null | 2406.05686 | null | null | http://arxiv.org/pdf/2406.05686v1 | 2024-06-09T08:11:12Z | 2024-06-09T08:11:12Z | Provable Optimization for Adversarial Fair Self-supervised Contrastive
Learning | This paper studies learning fair encoders in a self-supervised learning (SSL) setting, in which all data are unlabeled and only a small portion of them are annotated with sensitive attribute. Adversarial fair representation learning is well suited for this scenario by minimizing a contrastive loss over unlabeled data while maximizing an adversarial loss of predicting the sensitive attribute over the data with sensitive attribute. Nevertheless, optimizing adversarial fair representation learning presents significant challenges due to solving a non-convex non-concave minimax game. The complexity deepens when incorporating a global contrastive loss that contrasts each anchor data point against all other examples. A central question is ``{it can we design a provable yet efficient algorithm for solving adversarial fair self-supervised contrastive learning}?'' Building on advanced optimization techniques, we propose a stochastic algorithm dubbed SoFCLR with a convergence analysis under reasonable conditions without requring a large batch size. We conduct extensive experiments to demonstrate the effectiveness of the proposed approach for downstream classification with eight fairness notions. | [
"['Qi Qi' 'Quanqi Hu' 'Qihang Lin' 'Tianbao Yang']"
] |
null | null | 2406.05688 | null | null | http://arxiv.org/pdf/2406.05688v1 | 2024-06-09T08:24:17Z | 2024-06-09T08:24:17Z | Peer Review as A Multi-Turn and Long-Context Dialogue with Role-Based
Interactions | Large Language Models (LLMs) have demonstrated wide-ranging applications across various fields and have shown significant potential in the academic peer-review process. However, existing applications are primarily limited to static review generation based on submitted papers, which fail to capture the dynamic and iterative nature of real-world peer reviews. In this paper, we reformulate the peer-review process as a multi-turn, long-context dialogue, incorporating distinct roles for authors, reviewers, and decision makers. We construct a comprehensive dataset containing over 26,841 papers with 92,017 reviews collected from multiple sources, including the top-tier conference and prestigious journal. This dataset is meticulously designed to facilitate the applications of LLMs for multi-turn dialogues, effectively simulating the complete peer-review process. Furthermore, we propose a series of metrics to evaluate the performance of LLMs for each role under this reformulated peer-review setting, ensuring fair and comprehensive evaluations. We believe this work provides a promising perspective on enhancing the LLM-driven peer-review process by incorporating dynamic, role-based interactions. It aligns closely with the iterative and interactive nature of real-world academic peer review, offering a robust foundation for future research and development in this area. We open-source the dataset at https://github.com/chengtan9907/ReviewMT. | [
"['Cheng Tan' 'Dongxin Lyu' 'Siyuan Li' 'Zhangyang Gao' 'Jingxuan Wei'\n 'Siqi Ma' 'Zicheng Liu' 'Stan Z. Li']"
] |
null | null | 2406.05694 | null | null | http://arxiv.org/pdf/2406.05694v1 | 2024-06-09T08:37:11Z | 2024-06-09T08:37:11Z | A Low Rank Neural Representation of Entropy Solutions | We construct a new representation of entropy solutions to nonlinear scalar conservation laws with a smooth convex flux function in a single spatial dimension. The representation is a generalization of the method of characteristics and posseses a compositional form. While it is a nonlinear representation, the embedded dynamics of the solution in the time variable is linear. This representation is then discretized as a manifold of implicit neural representations where the feedforward neural network architecture has a low rank structure. Finally, we show that the low rank neural representation with a fixed number of layers and a small number of coefficients can approximate any entropy solution regardless of the complexity of the shock topology, while retaining the linearity of the embedded dynamics. | [
"['Donsub Rim' 'Gerrit Welper']"
] |
null | null | 2406.05709 | null | null | http://arxiv.org/pdf/2406.05709v1 | 2024-06-09T09:55:04Z | 2024-06-09T09:55:04Z | TR2MTL: LLM based framework for Metric Temporal Logic Formalization of
Traffic Rules | Traffic rules formalization is crucial for verifying the compliance and safety of autonomous vehicles (AVs). However, manual translation of natural language traffic rules as formal specification requires domain knowledge and logic expertise, which limits its adaptation. This paper introduces TR2MTL, a framework that employs large language models (LLMs) to automatically translate traffic rules (TR) into metric temporal logic (MTL). It is envisioned as a human-in-loop system for AV rule formalization. It utilizes a chain-of-thought in-context learning approach to guide the LLM in step-by-step translation and generating valid and grammatically correct MTL formulas. It can be extended to various forms of temporal logic and rules. We evaluated the framework on a challenging dataset of traffic rules we created from various sources and compared it against LLMs using different in-context learning methods. Results show that TR2MTL is domain-agnostic, achieving high accuracy and generalization capability even with a small dataset. Moreover, the method effectively predicts formulas with varying degrees of logical and semantic structure in unstructured traffic rules. | [
"['Kumar Manas' 'Stefan Zwicklbauer' 'Adrian Paschke']"
] |
null | null | 2406.05710 | null | null | http://arxiv.org/pdf/2406.05710v1 | 2024-06-09T10:06:50Z | 2024-06-09T10:06:50Z | Data-Driven Upper Confidence Bounds with Near-Optimal Regret for
Heavy-Tailed Bandits | Stochastic multi-armed bandits (MABs) provide a fundamental reinforcement learning model to study sequential decision making in uncertain environments. The upper confidence bounds (UCB) algorithm gave birth to the renaissance of bandit algorithms, as it achieves near-optimal regret rates under various moment assumptions. Up until recently most UCB methods relied on concentration inequalities leading to confidence bounds which depend on moment parameters, such as the variance proxy, that are usually unknown in practice. In this paper, we propose a new distribution-free, data-driven UCB algorithm for symmetric reward distributions, which needs no moment information. The key idea is to combine a refined, one-sided version of the recently developed resampled median-of-means (RMM) method with UCB. We prove a near-optimal regret bound for the proposed anytime, parameter-free RMM-UCB method, even for heavy-tailed distributions. | [
"['Ambrus Tamás' 'Szabolcs Szentpéteri' 'Balázs Csanád Csáji']"
] |
null | null | 2406.05714 | null | null | http://arxiv.org/pdf/2406.05714v2 | 2024-06-20T16:47:04Z | 2024-06-09T10:12:08Z | Contextual Continuum Bandits: Static Versus Dynamic Regret | We study the contextual continuum bandits problem, where the learner sequentially receives a side information vector and has to choose an action in a convex set, minimizing a function associated to the context. The goal is to minimize all the underlying functions for the received contexts, leading to a dynamic (contextual) notion of regret, which is stronger than the standard static regret. Assuming that the objective functions are H"older with respect to the contexts, we demonstrate that any algorithm achieving a sub-linear static regret can be extended to achieve a sub-linear dynamic regret. We further study the case of strongly convex and smooth functions when the observations are noisy. Inspired by the interior point method and employing self-concordant barriers, we propose an algorithm achieving a sub-linear dynamic regret. Lastly, we present a minimax lower bound, implying two key facts. First, no algorithm can achieve sub-linear dynamic regret over functions that are not continuous with respect to the context. Second, for strongly convex and smooth functions, the algorithm that we propose achieves, up to a logarithmic factor, the minimax optimal rate of dynamic regret as a function of the number of queries. | [
"['Arya Akhavan' 'Karim Lounici' 'Massimiliano Pontil'\n 'Alexandre B. Tsybakov']"
] |
null | null | 2406.05726 | null | null | http://arxiv.org/pdf/2406.05726v1 | 2024-06-09T10:36:06Z | 2024-06-09T10:36:06Z | Region of Interest Loss for Anonymizing Learned Image Compression | The use of AI in public spaces continually raises concerns about privacy and the protection of sensitive data. An example is the deployment of detection and recognition methods on humans, where images are provided by surveillance cameras. This results in the acquisition of great amounts of sensitive data, since the capture and transmission of images taken by such cameras happens unaltered, for them to be received by a server on the network. However, many applications do not explicitly require the identity of a given person in a scene; An anonymized representation containing information of the person's position while preserving the context of them in the scene suffices. We show how using a customized loss function on region of interests (ROI) can achieve sufficient anonymization such that human faces become unrecognizable while persons are kept detectable, by training an end-to-end optimized autoencoder for learned image compression that utilizes the flexibility of the learned analysis and reconstruction transforms for the task of mutating parts of the compression result. This approach enables compression and anonymization in one step on the capture device, instead of transmitting sensitive, nonanonymized data over the network. Additionally, we evaluate how this anonymization impacts the average precision of pre-trained foundation models on detecting faces (MTCNN) and humans (YOLOv8) in comparison to non-ANN based methods, while considering compression rate and latency. | [
"['Christoph Liebender' 'Ranulfo Bezerra' 'Kazunori Ohno'\n 'Satoshi Tadokoro']"
] |
null | null | 2406.05738 | null | null | http://arxiv.org/pdf/2406.05738v1 | 2024-06-09T11:13:03Z | 2024-06-09T11:13:03Z | Smiles2Dock: an open large-scale multi-task dataset for ML-based
molecular docking | Docking is a crucial component in drug discovery aimed at predicting the binding conformation and affinity between small molecules and target proteins. ML-based docking has recently emerged as a prominent approach, outpacing traditional methods like DOCK and AutoDock Vina in handling the growing scale and complexity of molecular libraries. However, the availability of comprehensive and user-friendly datasets for training and benchmarking ML-based docking algorithms remains limited. We introduce Smiles2Dock, an open large-scale multi-task dataset for molecular docking. We created a framework combining P2Rank and AutoDock Vina to dock 1.7 million ligands from the ChEMBL database against 15 AlphaFold proteins, giving us more than 25 million protein-ligand binding scores. The dataset leverages a wide range of high-accuracy AlphaFold protein models, encompasses a diverse set of biologically relevant compounds and enables researchers to benchmark all major approaches for ML-based docking such as Graph, Transformer and CNN-based methods. We also introduce a novel Transformer-based architecture for docking scores prediction and set it as an initial benchmark for our dataset. Our dataset and code are publicly available to support the development of novel ML-based methods for molecular docking to advance scientific research in this field. | [
"['Thomas Le Menestrel' 'Manuel Rivas']"
] |
null | null | 2406.05741 | null | null | http://arxiv.org/pdf/2406.05741v1 | 2024-06-09T11:16:11Z | 2024-06-09T11:16:11Z | Digital Business Model Analysis Using a Large Language Model | Digital transformation (DX) has recently become a pressing issue for many companies as the latest digital technologies, such as artificial intelligence and the Internet of Things, can be easily utilized. However, devising new business models is not easy for compa-nies, though they can improve their operations through digital technologies. Thus, business model design support methods are needed by people who lack digital tech-nology expertise. In contrast, large language models (LLMs) represented by ChatGPT and natural language processing utilizing LLMs have been developed revolutionarily. A business model design support system that utilizes these technologies has great potential. However, research on this area is scant. Accordingly, this study proposes an LLM-based method for comparing and analyzing similar companies from different business do-mains as a first step toward business model design support utilizing LLMs. This method can support idea generation in digital business model design. | [
"['Masahiro Watanabe' 'Naoshi Uchihira']"
] |
null | null | 2406.05745 | null | null | http://arxiv.org/pdf/2406.05745v1 | 2024-06-09T11:36:36Z | 2024-06-09T11:36:36Z | Structured Learning of Compositional Sequential Interventions | We consider sequential treatment regimes where each unit is exposed to combinations of interventions over time. When interventions are described by qualitative labels, such as ``close schools for a month due to a pandemic'' or ``promote this podcast to this user during this week'', it is unclear which appropriate structural assumptions allow us to generalize behavioral predictions to previously unseen combinatorial sequences. Standard black-box approaches mapping sequences of categorical variables to outputs are applicable, but they rely on poorly understood assumptions on how reliable generalization can be obtained, and may underperform under sparse sequences, temporal variability, and large action spaces. To approach that, we pose an explicit model for emph{composition}, that is, how the effect of sequential interventions can be isolated into modules, clarifying which data conditions allow for the identification of their combined effect at different units and time steps. We show the identification properties of our compositional model, inspired by advances in causal matrix factorization methods but focusing on predictive models for novel compositions of interventions instead of matrix completion tasks and causal effect estimation. We compare our approach to flexible but generic black-box models to illustrate how structure aids prediction in sparse data conditions. | [
"['Jialin Yu' 'Andreas Koukorinis' 'Nicolò Colombo' 'Yuchen Zhu'\n 'Ricardo Silva']"
] |
null | null | 2406.05746 | null | null | http://arxiv.org/abs/2406.05746v1 | 2024-06-09T11:37:45Z | 2024-06-09T11:37:45Z | Methodology and Real-World Applications of Dynamic Uncertain Causality
Graph for Clinical Diagnosis with Explainability and Invariance | AI-aided clinical diagnosis is desired in medical care. Existing deep learning models lack explainability and mainly focus on image analysis. The recently developed Dynamic Uncertain Causality Graph (DUCG) approach is causality-driven, explainable, and invariant across different application scenarios, without problems of data collection, labeling, fitting, privacy, bias, generalization, high cost and high energy consumption. Through close collaboration between clinical experts and DUCG technicians, 46 DUCG models covering 54 chief complaints were constructed. Over 1,000 diseases can be diagnosed without triage. Before being applied in real-world, the 46 DUCG models were retrospectively verified by third-party hospitals. The verified diagnostic precisions were no less than 95%, in which the diagnostic precision for every disease including uncommon ones was no less than 80%. After verifications, the 46 DUCG models were applied in the real-world in China. Over one million real diagnosis cases have been performed, with only 17 incorrect diagnoses identified. Due to DUCG's transparency, the mistakes causing the incorrect diagnoses were found and corrected. The diagnostic abilities of the clinicians who applied DUCG frequently were improved significantly. Following the introduction to the earlier presented DUCG methodology, the recommendation algorithm for potential medical checks is presented and the key idea of DUCG is extracted. | [
"['Zhan Zhang' 'Qin Zhang' 'Yang Jiao' 'Lin Lu' 'Lin Ma' 'Aihua Liu'\n 'Xiao Liu' 'Juan Zhao' 'Yajun Xue' 'Bing Wei' 'Mingxia Zhang' 'Ru Gao'\n 'Hong Zhao' 'Jie Lu' 'Fan Li' 'Yang Zhang' 'Yiming Wang' 'Lei Zhang'\n 'Fengwei Tian' 'Jie Hu' 'Xin Gou']"
] |
null | null | 2406.05753 | null | null | http://arxiv.org/pdf/2406.05753v3 | 2024-06-17T07:28:40Z | 2024-06-09T12:16:30Z | Grounding Continuous Representations in Geometry: Equivariant Neural
Fields | Recently, Neural Fields have emerged as a powerful modelling paradigm to represent continuous signals. In a conditional neural field, a field is represented by a latent variable that conditions the NeF, whose parametrisation is otherwise shared over an entire dataset. We propose Equivariant Neural Fields based on cross attention transformers, in which NeFs are conditioned on a geometric conditioning variable, a latent point cloud, that enables an equivariant decoding from latent to field. Our equivariant approach induces a steerability property by which both field and latent are grounded in geometry and amenable to transformation laws if the field transforms, the latent represents transforms accordingly and vice versa. Crucially, the equivariance relation ensures that the latent is capable of (1) representing geometric patterns faitfhully, allowing for geometric reasoning in latent space, (2) weightsharing over spatially similar patterns, allowing for efficient learning of datasets of fields. These main properties are validated using classification experiments and a verification of the capability of fitting entire datasets, in comparison to other non-equivariant NeF approaches. We further validate the potential of ENFs by demonstrate unique local field editing properties. | [
"['David R Wessels' 'David M Knigge' 'Samuele Papa' 'Riccardo Valperga'\n 'Sharvaree Vadgama' 'Efstratios Gavves' 'Erik J Bekkers']"
] |
null | null | 2406.05754 | null | null | http://arxiv.org/pdf/2406.05754v1 | 2024-06-09T12:17:05Z | 2024-06-09T12:17:05Z | Numerical solution of a PDE arising from prediction with expert advice | This work investigates the online machine learning problem of prediction with expert advice in an adversarial setting through numerical analysis of, and experiments with, a related partial differential equation. The problem is a repeated two-person game involving decision-making at each step informed by $n$ experts in an adversarial environment. The continuum limit of this game over a large number of steps is a degenerate elliptic equation whose solution encodes the optimal strategies for both players. We develop numerical methods for approximating the solution of this equation in relatively high dimensions ($nleq 10$) by exploiting symmetries in the equation and the solution to drastically reduce the size of the computational domain. Based on our numerical results we make a number of conjectures about the optimality of various adversarial strategies, in particular about the non-optimality of the COMB strategy. | [
"['Jeff Calder' 'Nadejda Drenska' 'Drisana Mosaphir']"
] |
null | null | 2406.05757 | null | null | http://arxiv.org/pdf/2406.05757v1 | 2024-06-09T12:23:22Z | 2024-06-09T12:23:22Z | Vision Mamba: Cutting-Edge Classification of Alzheimer's Disease with 3D
MRI Scans | Classifying 3D MRI images for early detection of Alzheimer's disease is a critical task in medical imaging. Traditional approaches using Convolutional Neural Networks (CNNs) and Transformers face significant challenges in this domain. CNNs, while effective in capturing local spatial features, struggle with long-range dependencies and often require extensive computational resources for high-resolution 3D data. Transformers, on the other hand, excel in capturing global context but suffer from quadratic complexity in inference time and require substantial memory, making them less efficient for large-scale 3D MRI data. To address these limitations, we propose the use of Vision Mamba, an advanced model based on State Space Models (SSMs), for the classification of 3D MRI images to detect Alzheimer's disease. Vision Mamba leverages dynamic state representations and the selective scan algorithm, allowing it to efficiently capture and retain important spatial information across 3D volumes. By dynamically adjusting state transitions based on input features, Vision Mamba can selectively retain relevant information, leading to more accurate and computationally efficient processing of 3D MRI data. Our approach combines the parallelizable nature of convolutional operations during training with the efficient, recurrent processing of states during inference. This architecture not only improves computational efficiency but also enhances the model's ability to handle long-range dependencies within 3D medical images. Experimental results demonstrate that Vision Mamba outperforms traditional CNN and Transformer models accuracy, making it a promising tool for the early detection of Alzheimer's disease using 3D MRI data. | [
"['Muthukumar K A' 'Amit Gurung' 'Priya Ranjan']"
] |
null | null | 2406.05766 | null | null | http://arxiv.org/pdf/2406.05766v1 | 2024-06-09T12:41:14Z | 2024-06-09T12:41:14Z | Gentle-CLIP: Exploring Aligned Semantic In Low-Quality Multimodal Data
With Soft Alignment | Multimodal fusion breaks through the barriers between diverse modalities and has already yielded numerous impressive performances. However, in various specialized fields, it is struggling to obtain sufficient alignment data for the training process, which seriously limits the use of previously elegant models. Thus, semi-supervised learning attempts to achieve multimodal alignment with fewer matched pairs but traditional methods like pseudo-labeling are difficult to apply in domains with no label information. To address these problems, we transform semi-supervised multimodal alignment into a manifold matching problem and propose a new method based on CLIP, named Gentle-CLIP. Specifically, we design a novel semantic density distribution loss to explore implicit semantic alignment information from unpaired multimodal data by constraining the latent representation distribution with fine granularity, thus eliminating the need for numerous strictly matched pairs. Meanwhile, we introduce multi-kernel maximum mean discrepancy as well as self-supervised contrastive loss to pull separate modality distributions closer and enhance the stability of the representation distribution. In addition, the contrastive loss used in CLIP is employed on the supervised matched data to prevent negative optimization. Extensive experiments conducted on a range of tasks in various fields, including protein, remote sensing, and the general vision-language field, demonstrate the effectiveness of our proposed Gentle-CLIP. | [
"['Zijia Song' 'Zelin Zang' 'Yelin Wang' 'Guozheng Yang' 'Jiangbin Zheng'\n 'Kaicheng yu' 'Wanyu Chen' 'Stan Z. Li']"
] |
null | null | 2406.05784 | null | null | http://arxiv.org/pdf/2406.05784v2 | 2024-06-12T06:13:36Z | 2024-06-09T13:42:51Z | Optimizing Multi-Stuttered Speech Classification: Leveraging Whisper's
Encoder for Efficient Parameter Reduction in Automated Assessment | The automated classification of stuttered speech has significant implications for timely assessments providing assistance to speech language pathologists. Despite notable advancements in the field, the cases in which multiple disfluencies occur in speech require attention. We have taken a progressive approach to fill this gap by classifying multi-stuttered speech more efficiently. The problem has been addressed by firstly curating a dataset of multi-stuttered disfluencies from SEP-28k audio clips. Secondly, employing Whisper, a state-of-the-art speech recognition model has been leveraged by using its encoder and taking the problem as multi-label classification. Thirdly, using a 6 encoder layer Whisper and experimenting with various layer freezing strategies, a computationally efficient configuration of the model was identified. The proposed configuration achieved micro, macro, and weighted F1- scores of 0.88, 0.85, and 0.87, correspondingly on an external test dataset i.e. Fluency-Bank. In addition, through layer freezing strategies, we were able to achieve the aforementioned results by fine-tuning a single encoder layer, consequently, reducing the model's trainable parameters from 20.27 million to 3.29 million. This research study unveils the contribution of the last encoder layer in the identification of disfluencies in stuttered speech. Consequently, it has led to a computationally efficient approach which makes the model more adaptable for various dialects and languages. | [
"['Huma Ameer' 'Seemab Latif' 'Rabia Latif']"
] |
null | null | 2406.05796 | null | null | http://arxiv.org/pdf/2406.05796v1 | 2024-06-09T14:20:46Z | 2024-06-09T14:20:46Z | ProFeAT: Projected Feature Adversarial Training for Self-Supervised
Learning of Robust Representations | The need for abundant labelled data in supervised Adversarial Training (AT) has prompted the use of Self-Supervised Learning (SSL) techniques with AT. However, the direct application of existing SSL methods to adversarial training has been sub-optimal due to the increased training complexity of combining SSL with AT. A recent approach, DeACL, mitigates this by utilizing supervision from a standard SSL teacher in a distillation setting, to mimic supervised AT. However, we find that there is still a large performance gap when compared to supervised adversarial training, specifically on larger models. In this work, investigate the key reason for this gap and propose Projected Feature Adversarial Training (ProFeAT) to bridge the same. We show that the sub-optimal distillation performance is a result of mismatch in training objectives of the teacher and student, and propose to use a projection head at the student, that allows it to leverage weak supervision from the teacher while also being able to learn adversarially robust representations that are distinct from the teacher. We further propose appropriate attack and defense losses at the feature and projector, alongside a combination of weak and strong augmentations for the teacher and student respectively, to improve the training data diversity without increasing the training complexity. Through extensive experiments on several benchmark datasets and models, we demonstrate significant improvements in both clean and robust accuracy when compared to existing SSL-AT methods, setting a new state-of-the-art. We further report on-par/ improved performance when compared to TRADES, a popular supervised-AT method. | [
"['Sravanti Addepalli' 'Priyam Dey' 'R. Venkatesh Babu']"
] |
null | null | 2406.05797 | null | null | http://arxiv.org/pdf/2406.05797v1 | 2024-06-09T14:20:55Z | 2024-06-09T14:20:55Z | 3D-MolT5: Towards Unified 3D Molecule-Text Modeling with 3D Molecular
Tokenization | The integration of molecule and language has garnered increasing attention in molecular science. Recent advancements in Language Models (LMs) have demonstrated potential for the comprehensive modeling of molecule and language. However, existing works exhibit notable limitations. Most existing works overlook the modeling of 3D information, which is crucial for understanding molecular structures and also functions. While some attempts have been made to leverage external structure encoding modules to inject the 3D molecular information into LMs, there exist obvious difficulties that hinder the integration of molecular structure and language text, such as modality alignment and separate tuning. To bridge this gap, we propose 3D-MolT5, a unified framework designed to model both 1D molecular sequence and 3D molecular structure. The key innovation lies in our methodology for mapping fine-grained 3D substructure representations (based on 3D molecular fingerprints) to a specialized 3D token vocabulary for 3D-MolT5. This 3D structure token vocabulary enables the seamless combination of 1D sequence and 3D structure representations in a tokenized format, allowing 3D-MolT5 to encode molecular sequence (SELFIES), molecular structure, and text sequences within a unified architecture. Alongside, we further introduce 1D and 3D joint pre-training to enhance the model's comprehension of these diverse modalities in a joint representation space and better generalize to various tasks for our foundation model. Through instruction tuning on multiple downstream datasets, our proposed 3D-MolT5 shows superior performance than existing methods in molecular property prediction, molecule captioning, and text-based molecule generation tasks. Our code will be available on GitHub soon. | [
"['Qizhi Pei' 'Lijun Wu' 'Kaiyuan Gao' 'Jinhua Zhu' 'Rui Yan']"
] |
null | null | 2406.05814 | null | null | http://arxiv.org/pdf/2406.05814v1 | 2024-06-09T15:00:28Z | 2024-06-09T15:00:28Z | Unified Text-to-Image Generation and Retrieval | How humans can efficiently and effectively acquire images has always been a perennial question. A typical solution is text-to-image retrieval from an existing database given the text query; however, the limited database typically lacks creativity. By contrast, recent breakthroughs in text-to-image generation have made it possible to produce fancy and diverse visual content, but it faces challenges in synthesizing knowledge-intensive images. In this work, we rethink the relationship between text-to-image generation and retrieval and propose a unified framework in the context of Multimodal Large Language Models (MLLMs). Specifically, we first explore the intrinsic discriminative abilities of MLLMs and introduce a generative retrieval method to perform retrieval in a training-free manner. Subsequently, we unify generation and retrieval in an autoregressive generation way and propose an autonomous decision module to choose the best-matched one between generated and retrieved images as the response to the text query. Additionally, we construct a benchmark called TIGeR-Bench, including creative and knowledge-intensive domains, to standardize the evaluation of unified text-to-image generation and retrieval. Extensive experimental results on TIGeR-Bench and two retrieval benchmarks, i.e., Flickr30K and MS-COCO, demonstrate the superiority and effectiveness of our proposed method. | [
"['Leigang Qu' 'Haochuan Li' 'Tan Wang' 'Wenjie Wang' 'Yongqi Li'\n 'Liqiang Nie' 'Tat-Seng Chua']"
] |
null | null | 2406.05815 | null | null | http://arxiv.org/pdf/2406.05815v1 | 2024-06-09T15:03:36Z | 2024-06-09T15:03:36Z | What Can We Learn from State Space Models for Machine Learning on
Graphs? | Machine learning on graphs has recently found extensive applications across domains. However, the commonly used Message Passing Neural Networks (MPNNs) suffer from limited expressive power and struggle to capture long-range dependencies. Graph transformers offer a strong alternative due to their global attention mechanism, but they come with great computational overheads, especially for large graphs. In recent years, State Space Models (SSMs) have emerged as a compelling approach to replace full attention in transformers to model sequential data. It blends the strengths of RNNs and CNNs, offering a) efficient computation, b) the ability to capture long-range dependencies, and c) good generalization across sequences of various lengths. However, extending SSMs to graph-structured data presents unique challenges due to the lack of canonical node ordering in graphs. In this work, we propose Graph State Space Convolution (GSSC) as a principled extension of SSMs to graph-structured data. By leveraging global permutation-equivariant set aggregation and factorizable graph kernels that rely on relative node distances as the convolution kernels, GSSC preserves all three advantages of SSMs. We demonstrate the provably stronger expressiveness of GSSC than MPNNs in counting graph substructures and show its effectiveness across 10 real-world, widely used benchmark datasets, where GSSC achieves best results on 7 out of 10 datasets with all significant improvements compared to the state-of-the-art baselines and second-best results on the other 3 datasets. Our findings highlight the potential of GSSC as a powerful and scalable model for graph machine learning. Our code is available at https://github.com/Graph-COM/GSSC. | [
"['Yinan Huang' 'Siqi Miao' 'Pan Li']"
] |
null | null | 2406.05816 | null | null | http://arxiv.org/pdf/2406.05816v2 | 2024-06-21T13:09:43Z | 2024-06-09T15:08:00Z | Attention as a Hypernetwork | Transformers can under some circumstances generalize to novel problem instances whose constituent parts might have been encountered during training but whose compositions have not. What mechanisms underlie this ability for compositional generalization? By reformulating multi-head attention as a hypernetwork, we reveal that a low-dimensional latent code specifies key-query specific operations. We find empirically that this latent code is highly structured, capturing information about the subtasks performed by the network. Using the framework of attention as a hypernetwork we further propose a simple modification of multi-head linear attention that strengthens the ability for compositional generalization on a range of abstract reasoning tasks. In particular, we introduce a symbolic version of the Raven Progressive Matrices human intelligence test on which we demonstrate how scaling model size and data enables compositional generalization and gives rise to a functionally structured latent code in the transformer. | [
"['Simon Schug' 'Seijin Kobayashi' 'Yassir Akram' 'João Sacramento'\n 'Razvan Pascanu']"
] |
null | null | 2406.05822 | null | null | http://arxiv.org/pdf/2406.05822v1 | 2024-06-09T15:14:53Z | 2024-06-09T15:14:53Z | Symmetric Matrix Completion with ReLU Sampling | We study the problem of symmetric positive semi-definite low-rank matrix completion (MC) with deterministic entry-dependent sampling. In particular, we consider rectified linear unit (ReLU) sampling, where only positive entries are observed, as well as a generalization to threshold-based sampling. We first empirically demonstrate that the landscape of this MC problem is not globally benign: Gradient descent (GD) with random initialization will generally converge to stationary points that are not globally optimal. Nevertheless, we prove that when the matrix factor with a small rank satisfies mild assumptions, the nonconvex objective function is geodesically strongly convex on the quotient manifold in a neighborhood of a planted low-rank matrix. Moreover, we show that our assumptions are satisfied by a matrix factor with i.i.d. Gaussian entries. Finally, we develop a tailor-designed initialization for GD to solve our studied formulation, which empirically always achieves convergence to the global minima. We also conduct extensive experiments and compare MC methods, investigating convergence and completion performance with respect to initialization, noise level, dimension, and rank. | [
"['Huikang Liu' 'Peng Wang' 'Longxiu Huang' 'Qing Qu' 'Laura Balzano']"
] |
null | null | 2406.05826 | null | null | http://arxiv.org/pdf/2406.05826v1 | 2024-06-09T15:31:00Z | 2024-06-09T15:31:00Z | PSBD: Prediction Shift Uncertainty Unlocks Backdoor Detection | Deep neural networks are susceptible to backdoor attacks, where adversaries manipulate model predictions by inserting malicious samples into the training data. Currently, there is still a lack of direct filtering methods for identifying suspicious training data to unveil potential backdoor samples. In this paper, we propose a novel method, Prediction Shift Backdoor Detection (PSBD), leveraging an uncertainty-based approach requiring minimal unlabeled clean validation data. PSBD is motivated by an intriguing Prediction Shift (PS) phenomenon, where poisoned models' predictions on clean data often shift away from true labels towards certain other labels with dropout applied during inference, while backdoor samples exhibit less PS. We hypothesize PS results from neuron bias effect, making neurons favor features of certain classes. PSBD identifies backdoor training samples by computing the Prediction Shift Uncertainty (PSU), the variance in probability values when dropout layers are toggled on and off during model inference. Extensive experiments have been conducted to verify the effectiveness and efficiency of PSBD, which achieves state-of-the-art results among mainstream detection methods. | [
"['Wei Li' 'Pin-Yu Chen' 'Sijia Liu' 'Ren Wang']"
] |
null | null | 2406.05830 | null | null | http://arxiv.org/pdf/2406.05830v1 | 2024-06-09T15:37:28Z | 2024-06-09T15:37:28Z | Probabilistic Approach to Black-Box Binary Optimization with Budget
Constraints: Application to Sensor Placement | We present a fully probabilistic approach for solving binary optimization problems with black-box objective functions and with budget constraints. In the probabilistic approach, the optimization variable is viewed as a random variable and is associated with a parametric probability distribution. The original optimization problem is replaced with an optimization over the expected value of the original objective, which is then optimized over the probability distribution parameters. The resulting optimal parameter (optimal policy) is used to sample the binary space to produce estimates of the optimal solution(s) of the original binary optimization problem. The probability distribution is chosen from the family of Bernoulli models because the optimization variable is binary. The optimization constraints generally restrict the feasibility region. This can be achieved by modeling the random variable with a conditional distribution given satisfiability of the constraints. Thus, in this work we develop conditional Bernoulli distributions to model the random variable conditioned by the total number of nonzero entries, that is, the budget constraint. This approach (a) is generally applicable to binary optimization problems with nonstochastic black-box objective functions and budget constraints; (b) accounts for budget constraints by employing conditional probabilities that sample only the feasible region and thus considerably reduces the computational cost compared with employing soft constraints; and (c) does not employ soft constraints and thus does not require tuning of a regularization parameter, for example to promote sparsity, which is challenging in sensor placement optimization problems. The proposed approach is verified numerically by using an idealized bilinear binary optimization problem and is validated by using a sensor placement experiment in a parameter identification setup. | [
"['Ahmed Attia']"
] |
null | null | 2406.05832 | null | null | http://arxiv.org/pdf/2406.05832v1 | 2024-06-09T15:50:35Z | 2024-06-09T15:50:35Z | Improving Antibody Design with Force-Guided Sampling in Diffusion Models | Antibodies, crucial for immune defense, primarily rely on complementarity-determining regions (CDRs) to bind and neutralize antigens, such as viruses. The design of these CDRs determines the antibody's affinity and specificity towards its target. Generative models, particularly denoising diffusion probabilistic models (DDPMs), have shown potential to advance the structure-based design of CDR regions. However, only a limited dataset of bound antibody-antigen structures is available, and generalization to out-of-distribution interfaces remains a challenge. Physics based force-fields, which approximate atomic interactions, offer a coarse but universal source of information to better mold designs to target interfaces. Integrating this foundational information into diffusion models is, therefore, highly desirable. Here, we propose a novel approach to enhance the sampling process of diffusion models by integrating force field energy-based feedback. Our model, DiffForce, employs forces to guide the diffusion sampling process, effectively blending the two distributions. Through extensive experiments, we demonstrate that our method guides the model to sample CDRs with lower energy, enhancing both the structure and sequence of the generated antibodies. | [
"['Paulina Kulytė' 'Francisco Vargas' 'Simon Valentin Mathis'\n 'Yu Guang Wang' 'José Miguel Hernández-Lobato' 'Pietro Liò']"
] |
null | null | 2406.05850 | null | null | http://arxiv.org/pdf/2406.05850v1 | 2024-06-09T16:49:19Z | 2024-06-09T16:49:19Z | Scaling Graph Convolutions for Mobile Vision | To compete with existing mobile architectures, MobileViG introduces Sparse Vision Graph Attention (SVGA), a fast token-mixing operator based on the principles of GNNs. However, MobileViG scales poorly with model size, falling at most 1% behind models with similar latency. This paper introduces Mobile Graph Convolution (MGC), a new vision graph neural network (ViG) module that solves this scaling problem. Our proposed mobile vision architecture, MobileViGv2, uses MGC to demonstrate the effectiveness of our approach. MGC improves on SVGA by increasing graph sparsity and introducing conditional positional encodings to the graph operation. Our smallest model, MobileViGv2-Ti, achieves a 77.7% top-1 accuracy on ImageNet-1K, 2% higher than MobileViG-Ti, with 0.9 ms inference latency on the iPhone 13 Mini NPU. Our largest model, MobileViGv2-B, achieves an 83.4% top-1 accuracy, 0.8% higher than MobileViG-B, with 2.7 ms inference latency. Besides image classification, we show that MobileViGv2 generalizes well to other tasks. For object detection and instance segmentation on MS COCO 2017, MobileViGv2-M outperforms MobileViG-M by 1.2 $AP^{box}$ and 0.7 $AP^{mask}$, and MobileViGv2-B outperforms MobileViG-B by 1.0 $AP^{box}$ and 0.7 $AP^{mask}$. For semantic segmentation on ADE20K, MobileViGv2-M achieves 42.9% $mIoU$ and MobileViGv2-B achieves 44.3% $mIoU$. Our code can be found at url{https://github.com/SLDGroup/MobileViGv2}. | [
"['William Avery' 'Mustafa Munir' 'Radu Marculescu']"
] |
null | null | 2406.05855 | null | null | http://arxiv.org/pdf/2406.05855v2 | 2024-06-14T06:30:22Z | 2024-06-09T16:58:19Z | Self-Distilled Disentangled Learning for Counterfactual Prediction | The advancements in disentangled representation learning significantly enhance the accuracy of counterfactual predictions by granting precise control over instrumental variables, confounders, and adjustable variables. An appealing method for achieving the independent separation of these factors is mutual information minimization, a task that presents challenges in numerous machine learning scenarios, especially within high-dimensional spaces. To circumvent this challenge, we propose the Self-Distilled Disentanglement framework, referred to as $SD^2$. Grounded in information theory, it ensures theoretically sound independent disentangled representations without intricate mutual information estimator designs for high-dimensional representations. Our comprehensive experiments, conducted on both synthetic and real-world datasets, confirms the effectiveness of our approach in facilitating counterfactual inference in the presence of both observed and unobserved confounders. | [
"['Xinshu Li' 'Mingming Gong' 'Lina Yao']"
] |
null | null | 2406.05863 | null | null | http://arxiv.org/pdf/2406.05863v1 | 2024-06-09T17:27:20Z | 2024-06-09T17:27:20Z | Source -Free Domain Adaptation for Speaker Verification in Data-Scarce
Languages and Noisy Channels | Domain adaptation is often hampered by exceedingly small target datasets and inaccessible source data. These conditions are prevalent in speech verification, where privacy policies and/or languages with scarce speech resources limit the availability of sufficient data. This paper explored techniques of sourcefree domain adaptation unto a limited target speech dataset for speaker verificationin data-scarce languages. Both language and channel mis-match between source and target were investigated. Fine-tuning methods were evaluated and compared across different sizes of labeled target data. A novel iterative cluster-learn algorithm was studied for unlabeled target datasets. | [
"['Shlomo Salo Elia' 'Aviad Malachi' 'Vered Aharonson' 'Gadi Pinkas']"
] |
null | null | 2406.05870 | null | null | http://arxiv.org/pdf/2406.05870v1 | 2024-06-09T17:55:55Z | 2024-06-09T17:55:55Z | Machine Against the RAG: Jamming Retrieval-Augmented Generation with
Blocker Documents | Retrieval-augmented generation (RAG) systems respond to queries by retrieving relevant documents from a knowledge database, then generating an answer by applying an LLM to the retrieved documents. We demonstrate that RAG systems that operate on databases with potentially untrusted content are vulnerable to a new class of denial-of-service attacks we call jamming. An adversary can add a single ``blocker'' document to the database that will be retrieved in response to a specific query and, furthermore, result in the RAG system not answering the query - ostensibly because it lacks the information or because the answer is unsafe. We describe and analyze several methods for generating blocker documents, including a new method based on black-box optimization that does not require the adversary to know the embedding or LLM used by the target RAG system, nor access to an auxiliary LLM to generate blocker documents. We measure the efficacy of the considered methods against several LLMs and embeddings, and demonstrate that the existing safety metrics for LLMs do not capture their vulnerability to jamming. We then discuss defenses against blocker documents. | [
"['Avital Shafran' 'Roei Schuster' 'Vitaly Shmatikov']"
] |
null | null | 2406.05871 | null | null | http://arxiv.org/pdf/2406.05871v1 | 2024-06-09T18:03:47Z | 2024-06-09T18:03:47Z | OmniControlNet: Dual-stage Integration for Conditional Image Generation | We provide a two-way integration for the widely adopted ControlNet by integrating external condition generation algorithms into a single dense prediction method and incorporating its individually trained image generation processes into a single model. Despite its tremendous success, the ControlNet of a two-stage pipeline bears limitations in being not self-contained (e.g. calls the external condition generation algorithms) with a large model redundancy (separately trained models for different types of conditioning inputs). Our proposed OmniControlNet consolidates 1) the condition generation (e.g., HED edges, depth maps, user scribble, and animal pose) by a single multi-tasking dense prediction algorithm under the task embedding guidance and 2) the image generation process for different conditioning types under the textual embedding guidance. OmniControlNet achieves significantly reduced model complexity and redundancy while capable of producing images of comparable quality for conditioned text-to-image generation. | [
"['Yilin Wang' 'Haiyang Xu' 'Xiang Zhang' 'Zeyuan Chen' 'Zhizhou Sha'\n 'Zirui Wang' 'Zhuowen Tu']"
] |
null | null | 2406.05872 | null | null | http://arxiv.org/pdf/2406.05872v1 | 2024-06-09T18:07:47Z | 2024-06-09T18:07:47Z | STARLING: Self-supervised Training of Text-based Reinforcement Learning
Agent with Large Language Models | Interactive fiction games have emerged as an important application to improve the generalization capabilities of language-based reinforcement learning (RL) agents. Existing environments for interactive fiction games are domain-specific or time-consuming to generate and do not train the RL agents to master a specific set of skills. In this work, we introduce an interactive environment for self-supervised RL, STARLING, for text-based games that bootstraps the text-based RL agents with automatically generated games (based on the seed set of game ideas) to boost the performance and generalization capabilities to reach a goal of the target environment. These games let the agent hone their skills on a predefined set of tasks. We create and test an environment with 100 games, generated using this automated framework that uses large language models (GPT-3) and an interactive fiction game engine (based on Inform7) to provide the user with the ability to generate more games under minimal human supervision. Experimental results based on both the human participants and baseline text-based RL agents reveal that current state-of-the-art text-based RL agents cannot use previously learned skills in new situations at the level humans can. These results enforce STARLING's potential to serve as a sandbox environment for further research in self-supervised text-based RL. | [
"['Shreyas Basavatia' 'Keerthiram Murugesan' 'Shivam Ratnakar']"
] |
null | null | 2406.05876 | null | null | http://arxiv.org/pdf/2406.05876v1 | 2024-06-09T18:13:36Z | 2024-06-09T18:13:36Z | Zero-Shot End-To-End Spoken Question Answering In Medical Domain | In the rapidly evolving landscape of spoken question-answering (SQA), the integration of large language models (LLMs) has emerged as a transformative development. Conventional approaches often entail the use of separate models for question audio transcription and answer selection, resulting in significant resource utilization and error accumulation. To tackle these challenges, we explore the effectiveness of end-to-end (E2E) methodologies for SQA in the medical domain. Our study introduces a novel zero-shot SQA approach, compared to traditional cascade systems. Through a comprehensive evaluation conducted on a new open benchmark of 8 medical tasks and 48 hours of synthetic audio, we demonstrate that our approach requires up to 14.7 times fewer resources than a combined 1.3B parameters LLM with a 1.55B parameters ASR model while improving average accuracy by 0.5%. These findings underscore the potential of E2E methodologies for SQA in resource-constrained contexts. | [
"['Yanis Labrak' 'Adel Moumen' 'Richard Dufour' 'Mickael Rouvier']"
] |
null | null | 2406.05881 | null | null | http://arxiv.org/pdf/2406.05881v2 | 2024-06-16T10:28:45Z | 2024-06-09T18:40:24Z | LGR2: Language Guided Reward Relabeling for Accelerating Hierarchical
Reinforcement Learning | Developing interactive systems that leverage natural language instructions to solve complex robotic control tasks has been a long-desired goal in the robotics community. Large Language Models (LLMs) have demonstrated exceptional abilities in handling complex tasks, including logical reasoning, in-context learning, and code generation. However, predicting low-level robotic actions using LLMs poses significant challenges. Additionally, the complexity of such tasks usually demands the acquisition of policies to execute diverse subtasks and combine them to attain the ultimate objective. Hierarchical Reinforcement Learning (HRL) is an elegant approach for solving such tasks, which provides the intuitive benefits of temporal abstraction and improved exploration. However, HRL faces the recurring issue of non-stationarity due to unstable lower primitive behaviour. In this work, we propose LGR2, a novel HRL framework that leverages language instructions to generate a stationary reward function for the higher-level policy. Since the language-guided reward is unaffected by the lower primitive behaviour, LGR2 mitigates non-stationarity and is thus an elegant method for leveraging language instructions to solve robotic control tasks. To analyze the efficacy of our approach, we perform empirical analysis and demonstrate that LGR2 effectively alleviates non-stationarity in HRL. Our approach attains success rates exceeding 70$%$ in challenging, sparse-reward robotic navigation and manipulation environments where the baselines fail to achieve any significant progress. Additionally, we conduct real-world robotic manipulation experiments and demonstrate that CRISP shows impressive generalization in real-world scenarios. | [
"['Utsav Singh' 'Pramit Bhattacharyya' 'Vinay P. Namboodiri']"
] |
null | null | 2406.05882 | null | null | http://arxiv.org/pdf/2406.05882v1 | 2024-06-09T18:41:05Z | 2024-06-09T18:41:05Z | Distributional Preference Alignment of LLMs via Optimal Transport | Current LLM alignment techniques use pairwise human preferences at a sample level, and as such, they do not imply an alignment on the distributional level. We propose in this paper Alignment via Optimal Transport (AOT), a novel method for distributional preference alignment of LLMs. AOT aligns LLMs on unpaired preference data by making the reward distribution of the positive samples stochastically dominant in the first order on the distribution of negative samples. We introduce a convex relaxation of this first-order stochastic dominance and cast it as an optimal transport problem with a smooth and convex cost. Thanks to the one-dimensional nature of the resulting optimal transport problem and the convexity of the cost, it has a closed-form solution via sorting on empirical measures. We fine-tune LLMs with this AOT objective, which enables alignment by penalizing the violation of the stochastic dominance of the reward distribution of the positive samples on the reward distribution of the negative samples. We analyze the sample complexity of AOT by considering the dual of the OT problem and show that it converges at the parametric rate. Empirically, we show on a diverse set of alignment datasets and LLMs that AOT leads to state-of-the-art models in the 7B family of models when evaluated with Open LLM Benchmarks and AlpacaEval. | [
"['Igor Melnyk' 'Youssef Mroueh' 'Brian Belgodere' 'Mattia Rigotti'\n 'Apoorva Nitsure' 'Mikhail Yurochkin' 'Kristjan Greenewald'\n 'Jiri Navratil' 'Jerret Ross']"
] |
null | null | 2406.05883 | null | null | http://arxiv.org/pdf/2406.05883v1 | 2024-06-09T18:41:50Z | 2024-06-09T18:41:50Z | Information Theoretic Guarantees For Policy Alignment In Large Language
Models | Policy alignment of large language models refers to constrained policy optimization, where the policy is optimized to maximize a reward while staying close to a reference policy with respect to an $f$-divergence such as the $mathsf{KL}$ divergence. The best of $n$ alignment policy selects a sample from the reference policy that has the maximum reward among $n$ independent samples. For both cases (policy alignment and best of $n$), recent works showed empirically that the reward improvement of the aligned policy on the reference one scales like $sqrt{mathsf{KL}}$, with an explicit bound in $n$ on the $mathsf{KL}$ for the best of $n$ policy. We show in this paper that the $sqrt{mathsf{KL}}$ information theoretic upper bound holds if the reward under the reference policy has sub-gaussian tails. Moreover, we prove for the best of $n$ policy, that the $mathsf{KL}$ upper bound can be obtained for any $f$-divergence via a reduction to exponential order statistics owing to the R'enyi representation of order statistics, and a data processing inequality. If additional information is known on the tails of the aligned policy we show that tighter control on the reward improvement can be obtained via the R'enyi divergence. Finally we demonstrate how these upper bounds transfer from proxy rewards to golden rewards which results in a decrease in the golden reward improvement due to overestimation and approximation errors of the proxy reward. | [
"['Youssef Mroueh']"
] |
null | null | 2406.05887 | null | null | http://arxiv.org/pdf/2406.05887v1 | 2024-06-09T18:59:08Z | 2024-06-09T18:59:08Z | Few-Shot Load Forecasting Under Data Scarcity in Smart Grids: A
Meta-Learning Approach | Despite the rapid expansion of smart grids and large volumes of data at the individual consumer level, there are still various cases where adequate data collection to train accurate load forecasting models is challenging or even impossible. This paper proposes adapting an established model-agnostic meta-learning algorithm for short-term load forecasting in the context of few-shot learning. Specifically, the proposed method can rapidly adapt and generalize within any unknown load time series of arbitrary length using only minimal training samples. In this context, the meta-learning model learns an optimal set of initial parameters for a base-level learner recurrent neural network. The proposed model is evaluated using a dataset of historical load consumption data from real-world consumers. Despite the examined load series' short length, it produces accurate forecasts outperforming transfer learning and task-specific machine learning methods by $12.5%$. To enhance robustness and fairness during model evaluation, a novel metric, mean average log percentage error, is proposed that alleviates the bias introduced by the commonly used MAPE metric. Finally, a series of studies to evaluate the model's robustness under different hyperparameters and time series lengths is also conducted, demonstrating that the proposed approach consistently outperforms all other models. | [
"['Georgios Tsoumplekas' 'Christos L. Athanasiadis' 'Dimitrios I. Doukas'\n 'Antonios Chrysopoulos' 'Pericles A. Mitkas']"
] |
null | null | 2406.05891 | null | null | http://arxiv.org/pdf/2406.05891v1 | 2024-06-09T19:17:14Z | 2024-06-09T19:17:14Z | GCtx-UNet: Efficient Network for Medical Image Segmentation | Medical image segmentation is crucial for disease diagnosis and monitoring. Though effective, the current segmentation networks such as UNet struggle with capturing long-range features. More accurate models such as TransUNet, Swin-UNet, and CS-UNet have higher computation complexity. To address this problem, we propose GCtx-UNet, a lightweight segmentation architecture that can capture global and local image features with accuracy better or comparable to the state-of-the-art approaches. GCtx-UNet uses vision transformer that leverages global context self-attention modules joined with local self-attention to model long and short range spatial dependencies. GCtx-UNet is evaluated on the Synapse multi-organ abdominal CT dataset, the ACDC cardiac MRI dataset, and several polyp segmentation datasets. In terms of Dice Similarity Coefficient (DSC) and Hausdorff Distance (HD) metrics, GCtx-UNet outperformed CNN-based and Transformer-based approaches, with notable gains in the segmentation of complex and small anatomical structures. Moreover, GCtx-UNet is much more efficient than the state-of-the-art approaches with smaller model size, lower computation workload, and faster training and inference speed, making it a practical choice for clinical applications. | [
"['Khaled Alrfou' 'Tian Zhao']"
] |
null | null | 2406.05892 | null | null | http://arxiv.org/pdf/2406.05892v1 | 2024-06-09T19:18:05Z | 2024-06-09T19:18:05Z | Security Vulnerability Detection with Multitask Self-Instructed
Fine-Tuning of Large Language Models | Software security vulnerabilities allow attackers to perform malicious activities to disrupt software operations. Recent Transformer-based language models have significantly advanced vulnerability detection, surpassing the capabilities of static analysis based deep learning models. However, language models trained solely on code tokens do not capture either the explanation of vulnerability type or the data flow structure information of code, both of which are crucial for vulnerability detection. We propose a novel technique that integrates a multitask sequence-to-sequence LLM with pro-gram control flow graphs encoded as a graph neural network to achieve sequence-to-classification vulnerability detection. We introduce MSIVD, multitask self-instructed fine-tuning for vulnerability detection, inspired by chain-of-thought prompting and LLM self-instruction. Our experiments demonstrate that MSIVD achieves superior performance, outperforming the highest LLM-based vulnerability detector baseline (LineVul), with a F1 score of 0.92 on the BigVul dataset, and 0.48 on the PreciseBugs dataset. By training LLMs and GNNs simultaneously using a combination of code and explanatory metrics of a vulnerable program, MSIVD represents a promising direction for advancing LLM-based vulnerability detection that generalizes to unseen data. Based on our findings, we further discuss the necessity for new labelled security vulnerability datasets, as recent LLMs have seen or memorized prior datasets' held-out evaluation data. | [
"['Aidan Z. H. Yang' 'Haoye Tian' 'He Ye' 'Ruben Martins' 'Claire Le Goues']"
] |
null | null | 2406.05893 | null | null | http://arxiv.org/pdf/2406.05893v1 | 2024-06-09T19:23:20Z | 2024-06-09T19:23:20Z | Event prediction and causality inference despite incomplete information | We explored the challenge of predicting and explaining the occurrence of events within sequences of data points. Our focus was particularly on scenarios in which unknown triggers causing the occurrence of events may consist of non-consecutive, masked, noisy data points. This scenario is akin to an agent tasked with learning to predict and explain the occurrence of events without understanding the underlying processes or having access to crucial information. Such scenarios are encountered across various fields, such as genomics, hardware and software verification, and financial time series prediction. We combined analytical, simulation, and machine learning (ML) approaches to investigate, quantify, and provide solutions to this challenge. We deduced and validated equations generally applicable to any variation of the underlying challenge. Using these equations, we (1) described how the level of complexity changes with various parameters (e.g., number of apparent and hidden states, trigger length, confidence, etc.) and (2) quantified the data needed to successfully train an ML model. We then (3) proved our ML solution learns and subsequently identifies unknown triggers and predicts the occurrence of events. If the complexity of the challenge is too high, our ML solution can identify trigger candidates to be used to interactively probe the system under investigation to determine the true trigger in a way considerably more efficient than brute force methods. By sharing our findings, we aim to assist others grappling with similar challenges, enabling estimates on the complexity of their problem, the data required and a solution to solve it. | [
"['Harrison Lam' 'Yuanjie Chen' 'Noboru Kanazawa' 'Mohammad Chowdhury'\n 'Anna Battista' 'Stephan Waldert']"
] |
null | null | 2406.05898 | null | null | http://arxiv.org/pdf/2406.05898v2 | 2024-06-23T05:43:41Z | 2024-06-09T19:35:20Z | Async Learned User Embeddings for Ads Delivery Optimization | In recommendation systems, high-quality user embeddings can capture subtle preferences, enable precise similarity calculations, and adapt to changing preferences over time to maintain relevance. The effectiveness of recommendation systems depends on the quality of user embedding. We propose to asynchronously learn high fidelity user embeddings for billions of users each day from sequence based multimodal user activities through a Transformer-like large scale feature learning module. The async learned user representations embeddings (ALURE) are further converted to user similarity graphs through graph learning and then combined with user realtime activities to retrieval highly related ads candidates for the ads delivery system. Our method shows significant gains in both offline and online experiments. | [
"['Mingwei Tang' 'Meng Liu' 'Hong Li' 'Junjie Yang' 'Chenglin Wei'\n 'Boyang Li' 'Dai Li' 'Rengan Xu' 'Yifan Xu' 'Zehua Zhang' 'Xiangyu Wang'\n 'Linfeng Liu' 'Yuelei Xie' 'Chengye Liu' 'Labib Fawaz' 'Li Li'\n 'Hongnan Wang' 'Bill Zhu' 'Sri Reddy']"
] |
null | null | 2406.05900 | null | null | http://arxiv.org/pdf/2406.05900v1 | 2024-06-09T19:38:27Z | 2024-06-09T19:38:27Z | Large Language Models Memorize Sensor Datasets! Implications on Human
Activity Recognition Research | The astonishing success of Large Language Models (LLMs) in Natural Language Processing (NLP) has spurred their use in many application domains beyond text analysis, including wearable sensor-based Human Activity Recognition (HAR). In such scenarios, often sensor data are directly fed into an LLM along with text instructions for the model to perform activity classification. Seemingly remarkable results have been reported for such LLM-based HAR systems when they are evaluated on standard benchmarks from the field. Yet, we argue, care has to be taken when evaluating LLM-based HAR systems in such a traditional way. Most contemporary LLMs are trained on virtually the entire (accessible) internet -- potentially including standard HAR datasets. With that, it is not unlikely that LLMs actually had access to the test data used in such benchmark experiments.The resulting contamination of training data would render these experimental evaluations meaningless. In this paper we investigate whether LLMs indeed have had access to standard HAR datasets during training. We apply memorization tests to LLMs, which involves instructing the models to extend given snippets of data. When comparing the LLM-generated output to the original data we found a non-negligible amount of matches which suggests that the LLM under investigation seems to indeed have seen wearable sensor data from the benchmark datasets during training. For the Daphnet dataset in particular, GPT-4 is able to reproduce blocks of sensor readings. We report on our investigations and discuss potential implications on HAR research, especially with regards to reporting results on experimental evaluation | [
"['Harish Haresamudram' 'Hrudhai Rajasekhar' 'Nikhil Murlidhar Shanbhogue'\n 'Thomas Ploetz']"
] |
null | null | 2406.05902 | null | null | http://arxiv.org/pdf/2406.05902v1 | 2024-06-09T19:42:25Z | 2024-06-09T19:42:25Z | Whose Preferences? Differences in Fairness Preferences and Their Impact
on the Fairness of AI Utilizing Human Feedback | There is a growing body of work on learning from human feedback to align various aspects of machine learning systems with human values and preferences. We consider the setting of fairness in content moderation, in which human feedback is used to determine how two comments -- referencing different sensitive attribute groups -- should be treated in comparison to one another. With a novel dataset collected from Prolific and MTurk, we find significant gaps in fairness preferences depending on the race, age, political stance, educational level, and LGBTQ+ identity of annotators. We also demonstrate that demographics mentioned in text have a strong influence on how users perceive individual fairness in moderation. Further, we find that differences also exist in downstream classifiers trained to predict human preferences. Finally, we observe that an ensemble, giving equal weight to classifiers trained on annotations from different demographics, performs better for different demographic intersections; compared to a single classifier that gives equal weight to each annotation. | [
"['Emilia Agis Lerner' 'Florian E. Dorner' 'Elliott Ash' 'Naman Goel']"
] |
null | null | 2406.05918 | null | null | http://arxiv.org/pdf/2406.05918v1 | 2024-06-09T21:12:15Z | 2024-06-09T21:12:15Z | Why Don't Prompt-Based Fairness Metrics Correlate? | The widespread use of large language models has brought up essential questions about the potential biases these models might learn. This led to the development of several metrics aimed at evaluating and mitigating these biases. In this paper, we first demonstrate that prompt-based fairness metrics exhibit poor agreement, as measured by correlation, raising important questions about the reliability of fairness assessment using prompts. Then, we outline six relevant reasons why such a low correlation is observed across existing metrics. Based on these insights, we propose a method called Correlated Fairness Output (CAIRO) to enhance the correlation between fairness metrics. CAIRO augments the original prompts of a given fairness metric by using several pre-trained language models and then selects the combination of the augmented prompts that achieves the highest correlation across metrics. We show a significant improvement in Pearson correlation from 0.3 and 0.18 to 0.90 and 0.98 across metrics for gender and religion biases, respectively. Our code is available at https://github.com/chandar-lab/CAIRO. | [
"['Abdelrahman Zayed' 'Goncalo Mordido' 'Ioana Baldini' 'Sarath Chandar']"
] |
null | null | 2406.05923 | null | null | http://arxiv.org/pdf/2406.05923v1 | 2024-06-09T21:44:06Z | 2024-06-09T21:44:06Z | Contrastive Learning from Synthetic Audio Doppelgangers | Learning robust audio representations currently demands extensive datasets of real-world sound recordings. By applying artificial transformations to these recordings, models can learn to recognize similarities despite subtle variations through techniques like contrastive learning. However, these transformations are only approximations of the true diversity found in real-world sounds, which are generated by complex interactions of physical processes, from vocal cord vibrations to the resonance of musical instruments. We propose a solution to both the data scale and transformation limitations, leveraging synthetic audio. By randomly perturbing the parameters of a sound synthesizer, we generate audio doppelg"angers-synthetic positive pairs with causally manipulated variations in timbre, pitch, and temporal envelopes. These variations, difficult to achieve through transformations of existing audio, provide a rich source of contrastive information. Despite the shift to randomly generated synthetic data, our method produces strong representations, competitive with real data on standard audio classification benchmarks. Notably, our approach is lightweight, requires no data storage, and has only a single hyperparameter, which we extensively analyze. We offer this method as a complement to existing strategies for contrastive learning in audio, using synthesized sounds to reduce the data burden on practitioners. | [
"['Manuel Cherep' 'Nikhil Singh']"
] |
null | null | 2406.05927 | null | null | http://arxiv.org/pdf/2406.05927v1 | 2024-06-09T22:14:55Z | 2024-06-09T22:14:55Z | MeanSparse: Post-Training Robustness Enhancement Through Mean-Centered
Feature Sparsification | We present a simple yet effective method to improve the robustness of Convolutional Neural Networks (CNNs) against adversarial examples by post-processing an adversarially trained model. Our technique, MeanSparse, cascades the activation functions of a trained model with novel operators that sparsify mean-centered feature vectors. This is equivalent to reducing feature variations around the mean, and we show that such reduced variations merely affect the model's utility, yet they strongly attenuate the adversarial perturbations and decrease the attacker's success rate. Our experiments show that, when applied to the top models in the RobustBench leaderboard, it achieves a new robustness record of 72.08% (from 71.07%) and 59.64% (from 59.56%) on CIFAR-10 and ImageNet, respectively, in term of AutoAttack accuracy. Code is available at https://github.com/SPIN-UMass/MeanSparse | [
"['Sajjad Amini' 'Mohammadreza Teymoorianfard' 'Shiqing Ma'\n 'Amir Houmansadr']"
] |
null | null | 2406.05937 | null | null | http://arxiv.org/pdf/2406.05937v1 | 2024-06-09T23:56:49Z | 2024-06-09T23:56:49Z | Linear Causal Representation Learning from Unknown Multi-node
Interventions | Despite the multifaceted recent advances in interventional causal representation learning (CRL), they primarily focus on the stylized assumption of single-node interventions. This assumption is not valid in a wide range of applications, and generally, the subset of nodes intervened in an interventional environment is fully unknown. This paper focuses on interventional CRL under unknown multi-node (UMN) interventional environments and establishes the first identifiability results for general latent causal models (parametric or nonparametric) under stochastic interventions (soft or hard) and linear transformation from the latent to observed space. Specifically, it is established that given sufficiently diverse interventional environments, (i) identifiability up to ancestors is possible using only soft interventions, and (ii) perfect identifiability is possible using hard interventions. Remarkably, these guarantees match the best-known results for more restrictive single-node interventions. Furthermore, CRL algorithms are also provided that achieve the identifiability guarantees. A central step in designing these algorithms is establishing the relationships between UMN interventional CRL and score functions associated with the statistical models of different interventional environments. Establishing these relationships also serves as constructive proof of the identifiability guarantees. | [
"['Burak Varıcı' 'Emre Acartürk' 'Karthikeyan Shanmugam' 'Ali Tajer']"
] |
null | null | 2406.05938 | null | null | http://arxiv.org/pdf/2406.05938v1 | 2024-06-09T23:57:47Z | 2024-06-09T23:57:47Z | Expressive Power of Graph Neural Networks for (Mixed-Integer) Quadratic
Programs | Quadratic programming (QP) is the most widely applied category of problems in nonlinear programming. Many applications require real-time/fast solutions, though not necessarily with high precision. Existing methods either involve matrix decomposition or use the preconditioned conjugate gradient method. For relatively large instances, these methods cannot achieve the real-time requirement unless there is an effective precondition. Recently, graph neural networks (GNNs) opened new possibilities for QP. Some promising empirical studies of applying GNNs for QP tasks show that GNNs can capture key characteristics of an optimization instance and provide adaptive guidance accordingly to crucial configurations during the solving process, or directly provide an approximate solution. Despite notable empirical observations, theoretical foundations are still lacking. In this work, we investigate the expressive or representative power of GNNs, a crucial aspect of neural network theory, specifically in the context of QP tasks, with both continuous and mixed-integer settings. We prove the existence of message-passing GNNs that can reliably represent key properties of quadratic programs, including feasibility, optimal objective value, and optimal solution. Our theory is validated by numerical results. | [
"['Ziang Chen' 'Xiaohan Chen' 'Jialin Liu' 'Xinshang Wang' 'Wotao Yin']"
] |
null | null | 2406.05953 | null | null | http://arxiv.org/pdf/2406.05953v1 | 2024-06-10T01:20:31Z | 2024-06-10T01:20:31Z | Decoupling regularization from the action space | Regularized reinforcement learning (RL), particularly the entropy-regularized kind, has gained traction in optimal control and inverse RL. While standard unregularized RL methods remain unaffected by changes in the number of actions, we show that it can severely impact their regularized counterparts. This paper demonstrates the importance of decoupling the regularizer from the action space: that is, to maintain a consistent level of regularization regardless of how many actions are involved to avoid over-regularization. Whereas the problem can be avoided by introducing a task-specific temperature parameter, it is often undesirable and cannot solve the problem when action spaces are state-dependent. In the state-dependent action context, different states with varying action spaces are regularized inconsistently. We introduce two solutions: a static temperature selection approach and a dynamic counterpart, universally applicable where this problem arises. Implementing these changes improves performance on the DeepMind control suite in static and dynamic temperature regimes and a biological sequence design task. | [
"['Sobhan Mohammadpour' 'Emma Frejinger' 'Pierre-Luc Bacon']"
] |
null | null | 2406.05954 | null | null | http://arxiv.org/pdf/2406.05954v2 | 2024-06-11T21:18:24Z | 2024-06-10T01:21:31Z | Aligning Large Language Models with Representation Editing: A Control
Perspective | Aligning large language models (LLMs) with human objectives is crucial for real-world applications. However, fine-tuning LLMs for alignment often suffers from unstable training and requires substantial computing resources. Test-time alignment techniques, such as prompting and guided decoding, do not modify the underlying model, and their performance remains dependent on the original model's capabilities. To address these challenges, we propose aligning LLMs through representation editing. The core of our method is to view a pre-trained autoregressive LLM as a discrete-time stochastic dynamical system. To achieve alignment for specific objectives, we introduce external control signals into the state space of this language dynamical system. We train a value function directly on the hidden states according to the Bellman equation, enabling gradient-based optimization to obtain the optimal control signals at test time. Our experiments demonstrate that our method outperforms existing test-time alignment techniques while requiring significantly fewer resources compared to fine-tuning methods. | [
"['Lingkai Kong' 'Haorui Wang' 'Wenhao Mu' 'Yuanqi Du' 'Yuchen Zhuang'\n 'Yifei Zhou' 'Yue Song' 'Rongzhi Zhang' 'Kai Wang' 'Chao Zhang']"
] |
null | null | 2406.05955 | null | null | http://arxiv.org/pdf/2406.05955v2 | 2024-06-11T02:15:47Z | 2024-06-10T01:21:59Z | Turbo Sparse: Achieving LLM SOTA Performance with Minimal Activated
Parameters | Exploiting activation sparsity is a promising approach to significantly accelerating the inference process of large language models (LLMs) without compromising performance. However, activation sparsity is determined by activation functions, and commonly used ones like SwiGLU and GeGLU exhibit limited sparsity. Simply replacing these functions with ReLU fails to achieve sufficient sparsity. Moreover, inadequate training data can further increase the risk of performance degradation. To address these challenges, we propose a novel dReLU function, which is designed to improve LLM activation sparsity, along with a high-quality training data mixture ratio to facilitate effective sparsification. Additionally, we leverage sparse activation patterns within the Feed-Forward Network (FFN) experts of Mixture-of-Experts (MoE) models to further boost efficiency. By applying our neuron sparsification method to the Mistral and Mixtral models, only 2.5 billion and 4.3 billion parameters are activated per inference iteration, respectively, while achieving even more powerful model performance. Evaluation results demonstrate that this sparsity achieves a 2-5x decoding speedup. Remarkably, on mobile phones, our TurboSparse-Mixtral-47B achieves an inference speed of 11 tokens per second. Our models are available at url{https://huggingface.co/PowerInfer} | [
"['Yixin Song' 'Haotong Xie' 'Zhengyan Zhang' 'Bo Wen' 'Li Ma' 'Zeyu Mi'\n 'Haibo Chen']"
] |
null | null | 2406.05959 | null | null | http://arxiv.org/pdf/2406.05959v2 | 2024-06-18T19:06:04Z | 2024-06-10T01:39:04Z | MAGNOLIA: Matching Algorithms via GNNs for Online Value-to-go
Approximation | Online Bayesian bipartite matching is a central problem in digital marketplaces and exchanges, including advertising, crowdsourcing, ridesharing, and kidney exchange. We introduce a graph neural network (GNN) approach that emulates the problem's combinatorially-complex optimal online algorithm, which selects actions (e.g., which nodes to match) by computing each action's value-to-go (VTG) -- the expected weight of the final matching if the algorithm takes that action, then acts optimally in the future. We train a GNN to estimate VTG and show empirically that this GNN returns high-weight matchings across a variety of tasks. Moreover, we identify a common family of graph distributions in spatial crowdsourcing applications, such as rideshare, under which VTG can be efficiently approximated by aggregating information within local neighborhoods in the graphs. This structure matches the local behavior of GNNs, providing theoretical justification for our approach. | [
"['Alexandre Hayderi' 'Amin Saberi' 'Ellen Vitercik' 'Anders Wikum']"
] |
null | null | 2406.05964 | null | null | http://arxiv.org/pdf/2406.05964v1 | 2024-06-10T01:46:42Z | 2024-06-10T01:46:42Z | Distributionally Robust Safe Sample Screening | In this study, we propose a machine learning method called Distributionally Robust Safe Sample Screening (DRSSS). DRSSS aims to identify unnecessary training samples, even when the distribution of the training samples changes in the future. To achieve this, we effectively combine the distributionally robust (DR) paradigm, which aims to enhance model robustness against variations in data distribution, with the safe sample screening (SSS), which identifies unnecessary training samples prior to model training. Since we need to consider an infinite number of scenarios regarding changes in the distribution, we applied SSS because it does not require model training after the change of the distribution. In this paper, we employed the covariate shift framework to represent the distribution of training samples and reformulated the DR covariate-shift problem as a weighted empirical risk minimization problem, where the weights are subject to uncertainty within a predetermined range. By extending the existing SSS technique to accommodate this weight uncertainty, the DRSSS method is capable of reliably identifying unnecessary samples under any future distribution within a specified range. We provide a theoretical guarantee for the DRSSS method and validate its performance through numerical experiments on both synthetic and real-world datasets. | [
"['Hiroyuki Hanada' 'Aoyama Tatsuya' 'Akahane Satoshi' 'Tomonari Tanaka'\n 'Yoshito Okura' 'Yu Inatsu' 'Noriaki Hashimoto' 'Shion Takeno'\n 'Taro Murayama' 'Hanju Lee' 'Shinya Kojima' 'Ichiro Takeuchi']"
] |
null | null | 2406.05967 | null | null | http://arxiv.org/pdf/2406.05967v1 | 2024-06-10T01:59:00Z | 2024-06-10T01:59:00Z | CVQA: Culturally-diverse Multilingual Visual Question Answering
Benchmark | Visual Question Answering (VQA) is an important task in multimodal AI, and it is often used to test the ability of vision-language models to understand and reason on knowledge present in both visual and textual data. However, most of the current VQA models use datasets that are primarily focused on English and a few major world languages, with images that are typically Western-centric. While recent efforts have tried to increase the number of languages covered on VQA datasets, they still lack diversity in low-resource languages. More importantly, although these datasets often extend their linguistic range via translation or some other approaches, they usually keep images the same, resulting in narrow cultural representation. To address these limitations, we construct CVQA, a new Culturally-diverse multilingual Visual Question Answering benchmark, designed to cover a rich set of languages and cultures, where we engage native speakers and cultural experts in the data collection process. As a result, CVQA includes culturally-driven images and questions from across 28 countries on four continents, covering 26 languages with 11 scripts, providing a total of 9k questions. We then benchmark several Multimodal Large Language Models (MLLMs) on CVQA, and show that the dataset is challenging for the current state-of-the-art models. This benchmark can serve as a probing evaluation suite for assessing the cultural capability and bias of multimodal models and hopefully encourage more research efforts toward increasing cultural awareness and linguistic diversity in this field. | [
"['David Romero' 'Chenyang Lyu' 'Haryo Akbarianto Wibowo' 'Teresa Lynn'\n 'Injy Hamed' 'Aditya Nanda Kishore' 'Aishik Mandal' 'Alina Dragonetti'\n 'Artem Abzaliev' 'Atnafu Lambebo Tonja' 'Bontu Fufa Balcha'\n 'Chenxi Whitehouse' 'Christian Salamea' 'Dan John Velasco'\n 'David Ifeoluwa Adelani' 'David Le Meur' 'Emilio Villa-Cueva'\n 'Fajri Koto' 'Fauzan Farooqui' 'Frederico Belcavello' 'Ganzorig Batnasan'\n 'Gisela Vallejo' 'Grainne Caulfield' 'Guido Ivetta' 'Haiyue Song'\n 'Henok Biadglign Ademtew' 'Hernán Maina' 'Holy Lovenia'\n 'Israel Abebe Azime' 'Jan Christian Blaise Cruz' 'Jay Gala' 'Jiahui Geng'\n 'Jesus-German Ortiz-Barajas' 'Jinheon Baek' 'Jocelyn Dunstan'\n 'Laura Alonso Alemany' 'Kumaranage Ravindu Yasas Nagasinghe'\n 'Luciana Benotti' \"Luis Fernando D'Haro\" 'Marcelo Viridiano'\n 'Marcos Estecha-Garitagoitia' 'Maria Camila Buitrago Cabrera'\n 'Mario Rodríguez-Cantelar' 'Mélanie Jouitteau' 'Mihail Mihaylov'\n 'Mohamed Fazli Mohamed Imam' 'Muhammad Farid Adilazuarda'\n 'Munkhjargal Gochoo' 'Munkh-Erdene Otgonbold' 'Naome Etori'\n 'Olivier Niyomugisha' 'Paula Mónica Silva' 'Pranjal Chitale' 'Raj Dabre'\n 'Rendi Chevi' 'Ruochen Zhang' 'Ryandito Diandaru' 'Samuel Cahyawijaya'\n 'Santiago Góngora' 'Soyeong Jeong' 'Sukannya Purkayastha'\n 'Tatsuki Kuribayashi' 'Thanmay Jayakumar' 'Tiago Timponi Torrent'\n 'Toqeer Ehsan' 'Vladimir Araujo' 'Yova Kementchedjhieva' 'Zara Burzo'\n 'Zheng Wei Lim' 'Zheng Xin Yong' 'Oana Ignat' 'Joan Nwatu'\n 'Rada Mihalcea' 'Thamar Solorio' 'Alham Fikri Aji']"
] |
null | null | 2406.05972 | null | null | http://arxiv.org/pdf/2406.05972v1 | 2024-06-10T02:14:19Z | 2024-06-10T02:14:19Z | Decision-Making Behavior Evaluation Framework for LLMs under Uncertain
Context | When making decisions under uncertainty, individuals often deviate from rational behavior, which can be evaluated across three dimensions: risk preference, probability weighting, and loss aversion. Given the widespread use of large language models (LLMs) in decision-making processes, it is crucial to assess whether their behavior aligns with human norms and ethical expectations or exhibits potential biases. Several empirical studies have investigated the rationality and social behavior performance of LLMs, yet their internal decision-making tendencies and capabilities remain inadequately understood. This paper proposes a framework, grounded in behavioral economics, to evaluate the decision-making behaviors of LLMs. Through a multiple-choice-list experiment, we estimate the degree of risk preference, probability weighting, and loss aversion in a context-free setting for three commercial LLMs: ChatGPT-4.0-Turbo, Claude-3-Opus, and Gemini-1.0-pro. Our results reveal that LLMs generally exhibit patterns similar to humans, such as risk aversion and loss aversion, with a tendency to overweight small probabilities. However, there are significant variations in the degree to which these behaviors are expressed across different LLMs. We also explore their behavior when embedded with socio-demographic features, uncovering significant disparities. For instance, when modeled with attributes of sexual minority groups or physical disabilities, Claude-3-Opus displays increased risk aversion, leading to more conservative choices. These findings underscore the need for careful consideration of the ethical implications and potential biases in deploying LLMs in decision-making scenarios. Therefore, this study advocates for developing standards and guidelines to ensure that LLMs operate within ethical boundaries while enhancing their utility in complex decision-making environments. | [
"['Jingru Jia' 'Zehua Yuan' 'Junhao Pan' 'Paul McNamara' 'Deming Chen']"
] |
null | null | 2406.05981 | null | null | http://arxiv.org/pdf/2406.05981v2 | 2024-06-11T15:14:30Z | 2024-06-10T02:47:55Z | ShiftAddLLM: Accelerating Pretrained LLMs via Post-Training
Multiplication-Less Reparameterization | Large language models (LLMs) have shown impressive performance on language tasks but face challenges when deployed on resource-constrained devices due to their extensive parameters and reliance on dense multiplications, resulting in high memory demands and latency bottlenecks. Shift-and-add reparameterization offers a promising solution by replacing costly multiplications with hardware-friendly primitives in both the attention and multi-layer perceptron (MLP) layers of an LLM. However, current reparameterization techniques require training from scratch or full parameter fine-tuning to restore accuracy, which is resource-intensive for LLMs. To address this, we propose accelerating pretrained LLMs through post-training shift-and-add reparameterization, creating efficient multiplication-free models, dubbed ShiftAddLLM. Specifically, we quantize each weight matrix into binary matrices paired with group-wise scaling factors. The associated multiplications are reparameterized into (1) shifts between activations and scaling factors and (2) queries and adds according to the binary matrices. To reduce accuracy loss, we present a multi-objective optimization method to minimize both weight and output activation reparameterization errors. Additionally, based on varying sensitivity across layers to reparameterization, we develop an automated bit allocation strategy to further reduce memory usage and latency. Experiments on five LLM families and eight tasks consistently validate the effectiveness of ShiftAddLLM, achieving average perplexity improvements of 5.6 and 22.7 points at comparable or lower latency compared to the most competitive quantized LLMs at 3 and 2 bits, respectively, and more than 80% memory and energy reductions over the original LLMs. Codes and models are available at https://github.com/GATECH-EIC/ShiftAddLLM. | [
"['Haoran You' 'Yipin Guo' 'Yichao Fu' 'Wei Zhou' 'Huihong Shi'\n 'Xiaofan Zhang' 'Souvik Kundu' 'Amir Yazdanbakhsh' 'Yingyan' 'Lin']"
] |
null | null | 2406.05982 | null | null | http://arxiv.org/abs/2406.05982v1 | 2024-06-10T02:50:33Z | 2024-06-10T02:50:33Z | Artificial Intelligence for Neuro MRI Acquisition: A Review | Magnetic resonance imaging (MRI) has significantly benefited from the resurgence of artificial intelligence (AI). By leveraging AI's capabilities in large-scale optimization and pattern recognition, innovative methods are transforming the MRI acquisition workflow, including planning, sequence design, and correction of acquisition artifacts. These emerging algorithms demonstrate substantial potential in enhancing the efficiency and throughput of acquisition steps. This review discusses several pivotal AI-based methods in neuro MRI acquisition, focusing on their technological advances, impact on clinical practice, and potential risks. | [
"['Hongjia Yang' 'Guanhua Wang' 'Ziyu Li' 'Haoxiang Li' 'Jialan Zheng'\n 'Yuxin Hu' 'Xiaozhi Cao' 'Congyu Liao' 'Huihui Ye' 'Qiyuan Tian']"
] |
null | null | 2406.05984 | null | null | http://arxiv.org/pdf/2406.05984v1 | 2024-06-10T02:51:16Z | 2024-06-10T02:51:16Z | Explainable AI for Mental Disorder Detection via Social Media: A survey
and outlook | Mental health constitutes a complex and pervasive global challenge, affecting millions of lives and often leading to severe consequences. In this paper, we conduct a thorough survey to explore the intersection of data science, artificial intelligence, and mental healthcare, focusing on the recent developments of mental disorder detection through online social media (OSM). A significant portion of the population actively engages in OSM platforms, creating a vast repository of personal data that holds immense potential for mental health analytics. The paper navigates through traditional diagnostic methods, state-of-the-art data- and AI-driven research studies, and the emergence of explainable AI (XAI) models for mental healthcare. We review state-of-the-art machine learning methods, particularly those based on modern deep learning, while emphasising the need for explainability in healthcare AI models. The experimental design section provides insights into prevalent practices, including available datasets and evaluation approaches. We also identify key issues and challenges in the field and propose promising future research directions. As mental health decisions demand transparency, interpretability, and ethical considerations, this paper contributes to the ongoing discourse on advancing XAI in mental healthcare through social media. The comprehensive overview presented here aims to guide researchers, practitioners, and policymakers in developing the area of mental disorder detection. | [
"['Yusif Ibrahimov' 'Tarique Anwar' 'Tommy Yuan']"
] |
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