categories
string | doi
string | id
string | year
float64 | venue
string | link
string | updated
string | published
string | title
string | abstract
string | authors
sequence |
|---|---|---|---|---|---|---|---|---|---|---|
null | null | 2406.06907 | null | null | http://arxiv.org/pdf/2406.06907v1 | 2024-06-11T03:00:41Z | 2024-06-11T03:00:41Z | SignMusketeers: An Efficient Multi-Stream Approach for Sign Language
Translation at Scale | A persistent challenge in sign language video processing, including the task of sign language to written language translation, is how we learn representations of sign language in an effective and efficient way that can preserve the important attributes of these languages, while remaining invariant to irrelevant visual differences. Informed by the nature and linguistics of signed languages, our proposed method focuses on just the most relevant parts in a signing video: the face, hands and body posture of the signer. However, instead of using pose estimation coordinates from off-the-shelf pose tracking models, which have inconsistent performance for hands and faces, we propose to learn the complex handshapes and rich facial expressions of sign languages in a self-supervised fashion. Our approach is based on learning from individual frames (rather than video sequences) and is therefore much more efficient than prior work on sign language pre-training. Compared to a recent model that established a new state of the art in sign language translation on the How2Sign dataset, our approach yields similar translation performance, using less than 3% of the compute. | [
"['Shester Gueuwou' 'Xiaodan Du' 'Greg Shakhnarovich' 'Karen Livescu']"
] |
null | null | 2406.06909 | null | null | http://arxiv.org/pdf/2406.06909v1 | 2024-06-11T03:07:41Z | 2024-06-11T03:07:41Z | Training Dynamics of Nonlinear Contrastive Learning Model in the High
Dimensional Limit | This letter presents a high-dimensional analysis of the training dynamics for a single-layer nonlinear contrastive learning model. The empirical distribution of the model weights converges to a deterministic measure governed by a McKean-Vlasov nonlinear partial differential equation (PDE). Under L2 regularization, this PDE reduces to a closed set of low-dimensional ordinary differential equations (ODEs), reflecting the evolution of the model performance during the training process. We analyze the fixed point locations and their stability of the ODEs unveiling several interesting findings. First, only the hidden variable's second moment affects feature learnability at the state with uninformative initialization. Second, higher moments influence the probability of feature selection by controlling the attraction region, rather than affecting local stability. Finally, independent noises added in the data argumentation degrade performance but negatively correlated noise can reduces the variance of gradient estimation yielding better performance. Despite of the simplicity of the analyzed model, it exhibits a rich phenomena of training dynamics, paving a way to understand more complex mechanism behind practical large models. | [
"['Lineghuan Meng' 'Chuang Wang']"
] |
null | null | 2406.06925 | null | null | http://arxiv.org/pdf/2406.06925v1 | 2024-06-11T03:44:17Z | 2024-06-11T03:44:17Z | Non-autoregressive Personalized Bundle Generation | The personalized bundle generation problem, which aims to create a preferred bundle for user from numerous candidate items, receives increasing attention in recommendation. However, existing works ignore the order-invariant nature of the bundle and adopt sequential modeling methods as the solution, which might introduce inductive bias and cause a large latency in prediction. To address this problem, we propose to perform the bundle generation via non-autoregressive mechanism and design a novel encoder-decoder framework named BundleNAT, which can effectively output the targeted bundle in one-shot without relying on any inherent order. In detail, instead of learning sequential dependency, we propose to adopt pre-training techniques and graph neural network to fully embed user-based preference and item-based compatibility information, and use a self-attention based encoder to further extract global dependency pattern. We then design a permutation-equivariant decoding architecture that is able to directly output the desired bundle in a one-shot manner. Experiments on three real-world datasets from Youshu and Netease show the proposed BundleNAT significantly outperforms the current state-of-the-art methods in average by up to 35.92%, 10.97% and 23.67% absolute improvements in Precision, Precision+, and Recall, respectively. | [
"['Wenchuan Yang' 'Cheng Yang' 'Jichao Li' 'Yuejin Tan' 'Xin Lu'\n 'Chuan Shi']"
] |
null | null | 2406.06954 | null | null | http://arxiv.org/pdf/2406.06954v1 | 2024-06-11T05:25:38Z | 2024-06-11T05:25:38Z | Distributional MIPLIB: a Multi-Domain Library for Advancing ML-Guided
MILP Methods | Mixed Integer Linear Programming (MILP) is a fundamental tool for modeling combinatorial optimization problems. Recently, a growing body of research has used machine learning to accelerate MILP solving. Despite the increasing popularity of this approach, there is a lack of a common repository that provides distributions of similar MILP instances across different domains, at different hardness levels, with standardized test sets. In this paper, we introduce Distributional MIPLIB, a multi-domain library of problem distributions for advancing ML-guided MILP methods. We curate MILP distributions from existing work in this area as well as real-world problems that have not been used, and classify them into different hardness levels. It will facilitate research in this area by enabling comprehensive evaluation on diverse and realistic domains. We empirically illustrate the benefits of using Distributional MIPLIB as a research vehicle in two ways. We evaluate the performance of ML-guided variable branching on previously unused distributions to identify potential areas for improvement. Moreover, we propose to learn branching policies from a mix of distributions, demonstrating that mixed distributions achieve better performance compared to homogeneous distributions when there is limited data and generalize well to larger instances. | [
"['Weimin Huang' 'Taoan Huang' 'Aaron M Ferber' 'Bistra Dilkina']"
] |
null | null | 2406.06955 | null | null | http://arxiv.org/pdf/2406.06955v1 | 2024-06-11T05:25:48Z | 2024-06-11T05:25:48Z | ElasticRec: A Microservice-based Model Serving Architecture Enabling
Elastic Resource Scaling for Recommendation Models | With the increasing popularity of recommendation systems (RecSys), the demand for compute resources in datacenters has surged. However, the model-wise resource allocation employed in current RecSys model serving architectures falls short in effectively utilizing resources, leading to sub-optimal total cost of ownership. We propose ElasticRec, a model serving architecture for RecSys providing resource elasticity and high memory efficiency. ElasticRec is based on a microservice-based software architecture for fine-grained resource allocation, tailored to the heterogeneous resource demands of RecSys. Additionally, ElasticRec achieves high memory efficiency via our utility-based resource allocation. Overall, ElasticRec achieves an average 3.3x reduction in memory allocation size and 8.1x increase in memory utility, resulting in an average 1.6x reduction in deployment cost compared to state-of-the-art RecSys inference serving system. | [
"['Yujeong Choi' 'Jiin Kim' 'Minsoo Rhu']"
] |
null | null | 2406.06959 | null | null | http://arxiv.org/pdf/2406.06959v1 | 2024-06-11T05:35:18Z | 2024-06-11T05:35:18Z | Unleashing the Denoising Capability of Diffusion Prior for Solving
Inverse Problems | The recent emergence of diffusion models has significantly advanced the precision of learnable priors, presenting innovative avenues for addressing inverse problems. Since inverse problems inherently entail maximum a posteriori estimation, previous works have endeavored to integrate diffusion priors into the optimization frameworks. However, prevailing optimization-based inverse algorithms primarily exploit the prior information within the diffusion models while neglecting their denoising capability. To bridge this gap, this work leverages the diffusion process to reframe noisy inverse problems as a two-variable constrained optimization task by introducing an auxiliary optimization variable. By employing gradient truncation, the projection gradient descent method is efficiently utilized to solve the corresponding optimization problem. The proposed algorithm, termed ProjDiff, effectively harnesses the prior information and the denoising capability of a pre-trained diffusion model within the optimization framework. Extensive experiments on the image restoration tasks and source separation and partial generation tasks demonstrate that ProjDiff exhibits superior performance across various linear and nonlinear inverse problems, highlighting its potential for practical applications. Code is available at https://github.com/weigerzan/ProjDiff/. | [
"['Jiawei Zhang' 'Jiaxin Zhuang' 'Cheng Jin' 'Gen Li' 'Yuantao Gu']"
] |
null | null | 2406.06960 | null | null | http://arxiv.org/pdf/2406.06960v1 | 2024-06-11T05:40:45Z | 2024-06-11T05:40:45Z | Low Rank Multi-Dictionary Selection at Scale | The sparse dictionary coding framework represents signals as a linear combination of a few predefined dictionary atoms. It has been employed for images, time series, graph signals and recently for 2-way (or 2D) spatio-temporal data employing jointly temporal and spatial dictionaries. Large and over-complete dictionaries enable high-quality models, but also pose scalability challenges which are exacerbated in multi-dictionary settings. Hence, an important problem that we address in this paper is: How to scale multi-dictionary coding for large dictionaries and datasets? We propose a multi-dictionary atom selection technique for low-rank sparse coding named LRMDS. To enable scalability to large dictionaries and datasets, it progressively selects groups of row-column atom pairs based on their alignment with the data and performs convex relaxation coding via the corresponding sub-dictionaries. We demonstrate both theoretically and experimentally that when the data has a low-rank encoding with a sparse subset of the atoms, LRMDS is able to select them with strong guarantees under mild assumptions. Furthermore, we demonstrate the scalability and quality of LRMDS in both synthetic and real-world datasets and for a range of coding dictionaries. It achieves 3X to 10X speed-up compared to baselines, while obtaining up to two orders of magnitude improvement in representation quality on some of the real world datasets given a fixed target number of atoms. | [
"['Boya Ma' 'Maxwell McNeil' 'Abram Magner' 'Petko Bogdanov']"
] |
null | null | 2406.06968 | null | null | http://arxiv.org/pdf/2406.06968v1 | 2024-06-11T05:51:44Z | 2024-06-11T05:51:44Z | Beyond the Norms: Detecting Prediction Errors in Regression Models | This paper tackles the challenge of detecting unreliable behavior in regression algorithms, which may arise from intrinsic variability (e.g., aleatoric uncertainty) or modeling errors (e.g., model uncertainty). First, we formally introduce the notion of unreliability in regression, i.e., when the output of the regressor exceeds a specified discrepancy (or error). Then, using powerful tools for probabilistic modeling, we estimate the discrepancy density, and we measure its statistical diversity using our proposed metric for statistical dissimilarity. In turn, this allows us to derive a data-driven score that expresses the uncertainty of the regression outcome. We show empirical improvements in error detection for multiple regression tasks, consistently outperforming popular baseline approaches, and contributing to the broader field of uncertainty quantification and safe machine learning systems. Our code is available at https://zenodo.org/records/11281964. | [
"['Andres Altieri' 'Marco Romanelli' 'Georg Pichler' 'Florence Alberge'\n 'Pablo Piantanida']"
] |
null | null | 2406.06972 | null | null | http://arxiv.org/pdf/2406.06972v1 | 2024-06-11T06:09:41Z | 2024-06-11T06:09:41Z | Generative Lifting of Multiview to 3D from Unknown Pose: Wrapping NeRF
inside Diffusion | We cast multiview reconstruction from unknown pose as a generative modeling problem. From a collection of unannotated 2D images of a scene, our approach simultaneously learns both a network to predict camera pose from 2D image input, as well as the parameters of a Neural Radiance Field (NeRF) for the 3D scene. To drive learning, we wrap both the pose prediction network and NeRF inside a Denoising Diffusion Probabilistic Model (DDPM) and train the system via the standard denoising objective. Our framework requires the system accomplish the task of denoising an input 2D image by predicting its pose and rendering the NeRF from that pose. Learning to denoise thus forces the system to concurrently learn the underlying 3D NeRF representation and a mapping from images to camera extrinsic parameters. To facilitate the latter, we design a custom network architecture to represent pose as a distribution, granting implicit capacity for discovering view correspondences when trained end-to-end for denoising alone. This technique allows our system to successfully build NeRFs, without pose knowledge, for challenging scenes where competing methods fail. At the conclusion of training, our learned NeRF can be extracted and used as a 3D scene model; our full system can be used to sample novel camera poses and generate novel-view images. | [
"['Xin Yuan' 'Rana Hanocka' 'Michael Maire']"
] |
null | null | 2406.06976 | null | null | http://arxiv.org/pdf/2406.06976v1 | 2024-06-11T06:16:33Z | 2024-06-11T06:16:33Z | Discrete Dictionary-based Decomposition Layer for Structured
Representation Learning | Neuro-symbolic neural networks have been extensively studied to integrate symbolic operations with neural networks, thereby improving systematic generalization. Specifically, Tensor Product Representation (TPR) framework enables neural networks to perform differentiable symbolic operations by encoding the symbolic structure of data within vector spaces. However, TPR-based neural networks often struggle to decompose unseen data into structured TPR representations, undermining their symbolic operations. To address this decomposition problem, we propose a Discrete Dictionary-based Decomposition (D3) layer designed to enhance the decomposition capabilities of TPR-based models. D3 employs discrete, learnable key-value dictionaries trained to capture symbolic features essential for decomposition operations. It leverages the prior knowledge acquired during training to generate structured TPR representations by mapping input data to pre-learned symbolic features within these dictionaries. D3 is a straightforward drop-in layer that can be seamlessly integrated into any TPR-based model without modifications. Our experimental results demonstrate that D3 significantly improves the systematic generalization of various TPR-based models while requiring fewer additional parameters. Notably, D3 outperforms baseline models on the synthetic task that demands the systematic decomposition of unseen combinatorial data. | [
"['Taewon Park' 'Hyun-Chul Kim' 'Minho Lee']"
] |
null | null | 2406.06977 | null | null | http://arxiv.org/pdf/2406.06977v1 | 2024-06-11T06:18:22Z | 2024-06-11T06:18:22Z | Cross-domain-aware Worker Selection with Training for Crowdsourced
Annotation | Annotation through crowdsourcing draws incremental attention, which relies on an effective selection scheme given a pool of workers. Existing methods propose to select workers based on their performance on tasks with ground truth, while two important points are missed. 1) The historical performances of workers in other tasks. In real-world scenarios, workers need to solve a new task whose correlation with previous tasks is not well-known before the training, which is called cross-domain. 2) The dynamic worker performance as workers will learn from the ground truth. In this paper, we consider both factors in designing an allocation scheme named cross-domain-aware worker selection with training approach. Our approach proposes two estimation modules to both statistically analyze the cross-domain correlation and simulate the learning gain of workers dynamically. A framework with a theoretical analysis of the worker elimination process is given. To validate the effectiveness of our methods, we collect two novel real-world datasets and generate synthetic datasets. The experiment results show that our method outperforms the baselines on both real-world and synthetic datasets. | [
"['Yushi Sun' 'Jiachuan Wang' 'Peng Cheng' 'Libin Zheng' 'Lei Chen'\n 'Jian Yin']"
] |
null | null | 2406.06979 | null | null | http://arxiv.org/pdf/2406.06979v1 | 2024-06-11T06:18:29Z | 2024-06-11T06:18:29Z | AudioMarkBench: Benchmarking Robustness of Audio Watermarking | The increasing realism of synthetic speech, driven by advancements in text-to-speech models, raises ethical concerns regarding impersonation and disinformation. Audio watermarking offers a promising solution via embedding human-imperceptible watermarks into AI-generated audios. However, the robustness of audio watermarking against common/adversarial perturbations remains understudied. We present AudioMarkBench, the first systematic benchmark for evaluating the robustness of audio watermarking against watermark removal and watermark forgery. AudioMarkBench includes a new dataset created from Common-Voice across languages, biological sexes, and ages, 3 state-of-the-art watermarking methods, and 15 types of perturbations. We benchmark the robustness of these methods against the perturbations in no-box, black-box, and white-box settings. Our findings highlight the vulnerabilities of current watermarking techniques and emphasize the need for more robust and fair audio watermarking solutions. Our dataset and code are publicly available at url{https://github.com/moyangkuo/AudioMarkBench}. | [
"['Hongbin Liu' 'Moyang Guo' 'Zhengyuan Jiang' 'Lun Wang'\n 'Neil Zhenqiang Gong']"
] |
null | null | 2406.06984 | null | null | http://arxiv.org/pdf/2406.06984v1 | 2024-06-11T06:28:21Z | 2024-06-11T06:28:21Z | On the Hölder Stability of Multiset and Graph Neural Networks | Famously, multiset neural networks based on sum-pooling can separate all distinct multisets, and as a result can be used by message passing neural networks (MPNNs) to separate all pairs of graphs that can be separated by the 1-WL graph isomorphism test. However, the quality of this separation may be very weak, to the extent that the embeddings of "separable" multisets and graphs might even be considered identical when using fixed finite precision. In this work, we propose to fully analyze the separation quality of multiset models and MPNNs via a novel adaptation of Lipschitz and H"{o}lder continuity to parametric functions. We prove that common sum-based models are lower-H"{o}lder continuous, with a H"{o}lder exponent that decays rapidly with the network's depth. Our analysis leads to adversarial examples of graphs which can be separated by three 1-WL iterations, but cannot be separated in practice by standard maximally powerful MPNNs. To remedy this, we propose two novel MPNNs with improved separation quality, one of which is lower Lipschitz continuous. We show these MPNNs can easily classify our adversarial examples, and compare favorably with standard MPNNs on standard graph learning tasks. | [
"['Yair Davidson' 'Nadav Dym']"
] |
null | null | 2406.06986 | null | null | http://arxiv.org/pdf/2406.06986v1 | 2024-06-11T06:31:03Z | 2024-06-11T06:31:03Z | DNN Partitioning, Task Offloading, and Resource Allocation in Dynamic
Vehicular Networks: A Lyapunov-Guided Diffusion-Based Reinforcement Learning
Approach | The rapid advancement of Artificial Intelligence (AI) has introduced Deep Neural Network (DNN)-based tasks to the ecosystem of vehicular networks. These tasks are often computation-intensive, requiring substantial computation resources, which are beyond the capability of a single vehicle. To address this challenge, Vehicular Edge Computing (VEC) has emerged as a solution, offering computing services for DNN-based tasks through resource pooling via Vehicle-to-Vehicle/Infrastructure (V2V/V2I) communications. In this paper, we formulate the problem of joint DNN partitioning, task offloading, and resource allocation in VEC as a dynamic long-term optimization. Our objective is to minimize the DNN-based task completion time while guaranteeing the system stability over time. To this end, we first leverage a Lyapunov optimization technique to decouple the original long-term optimization with stability constraints into a per-slot deterministic problem. Afterwards, we propose a Multi-Agent Diffusion-based Deep Reinforcement Learning (MAD2RL) algorithm, incorporating the innovative use of diffusion models to determine the optimal DNN partitioning and task offloading decisions. Furthermore, we integrate convex optimization techniques into MAD2RL as a subroutine to allocate computation resources, enhancing the learning efficiency. Through simulations under real-world movement traces of vehicles, we demonstrate the superior performance of our proposed algorithm compared to existing benchmark solutions. | [
"['Zhang Liu' 'Hongyang Du' 'Junzhe Lin' 'Zhibin Gao' 'Lianfen Huang'\n 'Seyyedali Hosseinalipour' 'Dusit Niyato']"
] |
null | null | 2406.07005 | null | null | http://arxiv.org/pdf/2406.07005v1 | 2024-06-11T06:59:17Z | 2024-06-11T06:59:17Z | DecoR: Deconfounding Time Series with Robust Regression | Causal inference on time series data is a challenging problem, especially in the presence of unobserved confounders. This work focuses on estimating the causal effect between two time series, which are confounded by a third, unobserved time series. Assuming spectral sparsity of the confounder, we show how in the frequency domain this problem can be framed as an adversarial outlier problem. We introduce Deconfounding by Robust regression (DecoR), a novel approach that estimates the causal effect using robust linear regression in the frequency domain. Considering two different robust regression techniques, we first improve existing bounds on the estimation error for such techniques. Crucially, our results do not require distributional assumptions on the covariates. We can therefore use them in time series settings. Applying these results to DecoR, we prove, under suitable assumptions, upper bounds for the estimation error of DecoR that imply consistency. We show DecoR's effectiveness through experiments on synthetic data. Our experiments furthermore suggest that our method is robust with respect to model misspecification. | [
"['Felix Schur' 'Jonas Peters']"
] |
null | null | 2406.07017 | null | null | http://arxiv.org/pdf/2406.07017v1 | 2024-06-11T07:19:04Z | 2024-06-11T07:19:04Z | MoreauPruner: Robust Pruning of Large Language Models against Weight
Perturbations | Few-shot gradient methods have been extensively utilized in existing model pruning methods, where the model weights are regarded as static values and the effects of potential weight perturbations are not considered. However, the widely used large language models (LLMs) have several billion model parameters, which could increase the fragility of few-shot gradient pruning. In this work, we experimentally show that one-shot gradient pruning algorithms could lead to unstable results under perturbations to model weights. And the minor error of switching between data formats bfloat16 and float16 could result in drastically different outcomes. To address such instabilities, we leverage optimization analysis and propose an LLM structural pruning method, called MoreauPruner, with provable robustness against weight perturbations. In MoreauPruner, the model weight importance is estimated based on the neural network's Moreau envelope, which can be flexibly combined with $ell_1$-norm regularization techniques to induce the sparsity required in the pruning task. We extensively evaluate the MoreauPruner algorithm on several well-known LLMs, including LLaMA-7B, LLaMA-13B, LLaMA3-8B, and Vicuna-7B. Our numerical results suggest the robustness of MoreauPruner against weight perturbations, and indicate the MoreauPruner's successful accuracy-based scores in comparison to several existing pruning methods. We have released the code in url{https://github.com/ShiningSord/MoreauPruner}. | [
"['Zixiao Wang' 'Jingwei Zhang' 'Wenqian Zhao' 'Farzan Farnia' 'Bei Yu']"
] |
null | null | 2406.07020 | null | null | http://arxiv.org/pdf/2406.07020v1 | 2024-06-11T07:25:17Z | 2024-06-11T07:25:17Z | Learning Discrete Latent Variable Structures with Tensor Rank Conditions | Unobserved discrete data are ubiquitous in many scientific disciplines, and how to learn the causal structure of these latent variables is crucial for uncovering data patterns. Most studies focus on the linear latent variable model or impose strict constraints on latent structures, which fail to address cases in discrete data involving non-linear relationships or complex latent structures. To achieve this, we explore a tensor rank condition on contingency tables for an observed variable set $mathbf{X}_p$, showing that the rank is determined by the minimum support of a specific conditional set (not necessary in $mathbf{X}_p$) that d-separates all variables in $mathbf{X}_p$. By this, one can locate the latent variable through probing the rank on different observed variables set, and further identify the latent causal structure under some structure assumptions. We present the corresponding identification algorithm and conduct simulated experiments to verify the effectiveness of our method. In general, our results elegantly extend the identification boundary for causal discovery with discrete latent variables and expand the application scope of causal discovery with latent variables. | [
"['Zhengming Chen' 'Ruichu Cai' 'Feng Xie' 'Jie Qiao' 'Anpeng Wu'\n 'Zijian Li' 'Zhifeng Hao' 'Kun Zhang']"
] |
null | null | 2406.07025 | null | null | http://arxiv.org/pdf/2406.07025v1 | 2024-06-11T07:29:13Z | 2024-06-11T07:29:13Z | Entropy-Reinforced Planning with Large Language Models for Drug
Discovery | The objective of drug discovery is to identify chemical compounds that possess specific pharmaceutical properties toward a binding target. Existing large language models (LLMS) can achieve high token matching scores in terms of likelihood for molecule generation. However, relying solely on LLM decoding often results in the generation of molecules that are either invalid due to a single misused token, or suboptimal due to unbalanced exploration and exploitation as a consequence of the LLMs prior experience. Here we propose ERP, Entropy-Reinforced Planning for Transformer Decoding, which employs an entropy-reinforced planning algorithm to enhance the Transformer decoding process and strike a balance between exploitation and exploration. ERP aims to achieve improvements in multiple properties compared to direct sampling from the Transformer. We evaluated ERP on the SARS-CoV-2 virus (3CLPro) and human cancer cell target protein (RTCB) benchmarks and demonstrated that, in both benchmarks, ERP consistently outperforms the current state-of-the-art algorithm by 1-5 percent, and baselines by 5-10 percent, respectively. Moreover, such improvement is robust across Transformer models trained with different objectives. Finally, to further illustrate the capabilities of ERP, we tested our algorithm on three code generation benchmarks and outperformed the current state-of-the-art approach as well. Our code is publicly available at: https://github.com/xuefeng-cs/ERP. | [
"['Xuefeng Liu' 'Chih-chan Tien' 'Peng Ding' 'Songhao Jiang'\n 'Rick L. Stevens']"
] |
null | null | 2406.07028 | null | null | http://arxiv.org/pdf/2406.07028v1 | 2024-06-11T07:32:25Z | 2024-06-11T07:32:25Z | Heterogeneous Learning Rate Scheduling for Neural Architecture Search on
Long-Tailed Datasets | In this paper, we attempt to address the challenge of applying Neural Architecture Search (NAS) algorithms, specifically the Differentiable Architecture Search (DARTS), to long-tailed datasets where class distribution is highly imbalanced. We observe that traditional re-sampling and re-weighting techniques, which are effective in standard classification tasks, lead to performance degradation when combined with DARTS. To mitigate this, we propose a novel adaptive learning rate scheduling strategy tailored for the architecture parameters of DARTS when integrated with the Bilateral Branch Network (BBN) for handling imbalanced datasets. Our approach dynamically adjusts the learning rate of the architecture parameters based on the training epoch, preventing the disruption of well-trained representations in the later stages of training. Additionally, we explore the impact of branch mixing factors on the algorithm's performance. Through extensive experiments on the CIFAR-10 dataset with an artificially induced long-tailed distribution, we demonstrate that our method achieves comparable accuracy to using DARTS alone. And the experiment results suggest that re-sampling methods inherently harm the performance of the DARTS algorithm. Our findings highlight the importance of careful data augment when applying DNAS to imbalanced learning scenarios. | [
"['Chenxia Tang']"
] |
null | null | 2406.07029 | null | null | http://arxiv.org/pdf/2406.07029v1 | 2024-06-11T07:34:15Z | 2024-06-11T07:34:15Z | Fairness-Aware Meta-Learning via Nash Bargaining | To address issues of group-level fairness in machine learning, it is natural to adjust model parameters based on specific fairness objectives over a sensitive-attributed validation set. Such an adjustment procedure can be cast within a meta-learning framework. However, naive integration of fairness goals via meta-learning can cause hypergradient conflicts for subgroups, resulting in unstable convergence and compromising model performance and fairness. To navigate this issue, we frame the resolution of hypergradient conflicts as a multi-player cooperative bargaining game. We introduce a two-stage meta-learning framework in which the first stage involves the use of a Nash Bargaining Solution (NBS) to resolve hypergradient conflicts and steer the model toward the Pareto front, and the second stage optimizes with respect to specific fairness goals. Our method is supported by theoretical results, notably a proof of the NBS for gradient aggregation free from linear independence assumptions, a proof of Pareto improvement, and a proof of monotonic improvement in validation loss. We also show empirical effects across various fairness objectives in six key fairness datasets and two image classification tasks. | [
"['Yi Zeng' 'Xuelin Yang' 'Li Chen' 'Cristian Canton Ferrer' 'Ming Jin'\n 'Michael I. Jordan' 'Ruoxi Jia']"
] |
null | null | 2406.07041 | null | null | http://arxiv.org/pdf/2406.07041v1 | 2024-06-11T07:59:17Z | 2024-06-11T07:59:17Z | Integrating Domain Knowledge for handling Limited Data in Offline RL | With the ability to learn from static datasets, Offline Reinforcement Learning (RL) emerges as a compelling avenue for real-world applications. However, state-of-the-art offline RL algorithms perform sub-optimally when confronted with limited data confined to specific regions within the state space. The performance degradation is attributed to the inability of offline RL algorithms to learn appropriate actions for rare or unseen observations. This paper proposes a novel domain knowledge-based regularization technique and adaptively refines the initial domain knowledge to considerably boost performance in limited data with partially omitted states. The key insight is that the regularization term mitigates erroneous actions for sparse samples and unobserved states covered by domain knowledge. Empirical evaluations on standard discrete environment datasets demonstrate a substantial average performance increase of at least 27% compared to existing offline RL algorithms operating on limited data. | [
"['Briti Gangopadhyay' 'Zhao Wang' 'Jia-Fong Yeh' 'Shingo Takamatsu']"
] |
null | null | 2406.07049 | null | null | http://arxiv.org/pdf/2406.07049v1 | 2024-06-11T08:25:11Z | 2024-06-11T08:25:11Z | GridPE: Unifying Positional Encoding in Transformers with a Grid
Cell-Inspired Framework | Understanding spatial location and relationships is a fundamental capability for modern artificial intelligence systems. Insights from human spatial cognition provide valuable guidance in this domain. Recent neuroscientific discoveries have highlighted the role of grid cells as a fundamental neural component for spatial representation, including distance computation, path integration, and scale discernment. In this paper, we introduce a novel positional encoding scheme inspired by Fourier analysis and the latest findings in computational neuroscience regarding grid cells. Assuming that grid cells encode spatial position through a summation of Fourier basis functions, we demonstrate the translational invariance of the grid representation during inner product calculations. Additionally, we derive an optimal grid scale ratio for multi-dimensional Euclidean spaces based on principles of biological efficiency. Utilizing these computational principles, we have developed a **Grid**-cell inspired **Positional Encoding** technique, termed **GridPE**, for encoding locations within high-dimensional spaces. We integrated GridPE into the Pyramid Vision Transformer architecture. Our theoretical analysis shows that GridPE provides a unifying framework for positional encoding in arbitrary high-dimensional spaces. Experimental results demonstrate that GridPE significantly enhances the performance of transformers, underscoring the importance of incorporating neuroscientific insights into the design of artificial intelligence systems. | [
"['Boyang Li' 'Yulin Wu' 'Nuoxian Huang']"
] |
null | null | 2406.07053 | null | null | http://arxiv.org/pdf/2406.07053v1 | 2024-06-11T08:35:23Z | 2024-06-11T08:35:23Z | TelecomRAG: Taming Telecom Standards with Retrieval Augmented Generation
and LLMs | Large Language Models (LLMs) have immense potential to transform the telecommunications industry. They could help professionals understand complex standards, generate code, and accelerate development. However, traditional LLMs struggle with the precision and source verification essential for telecom work. To address this, specialized LLM-based solutions tailored to telecommunication standards are needed. Retrieval-augmented generation (RAG) offers a way to create precise, fact-based answers. This paper proposes TelecomRAG, a framework for a Telecommunication Standards Assistant that provides accurate, detailed, and verifiable responses. Our implementation, using a knowledge base built from 3GPP Release 16 and Release 18 specification documents, demonstrates how this assistant surpasses generic LLMs, offering superior accuracy, technical depth, and verifiability, and thus significant value to the telecommunications field. | [
"['Girma M. Yilma' 'Jose A. Ayala-Romero' 'Andres Garcia-Saavedra'\n 'Xavier Costa-Perez']"
] |
null | null | 2406.07057 | null | null | http://arxiv.org/pdf/2406.07057v1 | 2024-06-11T08:38:13Z | 2024-06-11T08:38:13Z | Benchmarking Trustworthiness of Multimodal Large Language Models: A
Comprehensive Study | Despite the superior capabilities of Multimodal Large Language Models (MLLMs) across diverse tasks, they still face significant trustworthiness challenges. Yet, current literature on the assessment of trustworthy MLLMs remains limited, lacking a holistic evaluation to offer thorough insights into future improvements. In this work, we establish MultiTrust, the first comprehensive and unified benchmark on the trustworthiness of MLLMs across five primary aspects: truthfulness, safety, robustness, fairness, and privacy. Our benchmark employs a rigorous evaluation strategy that addresses both multimodal risks and cross-modal impacts, encompassing 32 diverse tasks with self-curated datasets. Extensive experiments with 21 modern MLLMs reveal some previously unexplored trustworthiness issues and risks, highlighting the complexities introduced by the multimodality and underscoring the necessity for advanced methodologies to enhance their reliability. For instance, typical proprietary models still struggle with the perception of visually confusing images and are vulnerable to multimodal jailbreaking and adversarial attacks; MLLMs are more inclined to disclose privacy in text and reveal ideological and cultural biases even when paired with irrelevant images in inference, indicating that the multimodality amplifies the internal risks from base LLMs. Additionally, we release a scalable toolbox for standardized trustworthiness research, aiming to facilitate future advancements in this important field. Code and resources are publicly available at: https://multi-trust.github.io/. | [
"['Yichi Zhang' 'Yao Huang' 'Yitong Sun' 'Chang Liu' 'Zhe Zhao'\n 'Zhengwei Fang' 'Yifan Wang' 'Huanran Chen' 'Xiao Yang' 'Xingxing Wei'\n 'Hang Su' 'Yinpeng Dong' 'Jun Zhu']"
] |
null | null | 2406.07060 | null | null | http://arxiv.org/pdf/2406.07060v1 | 2024-06-11T08:41:21Z | 2024-06-11T08:41:21Z | Reading Miscue Detection in Primary School through Automatic Speech
Recognition | Automatic reading diagnosis systems can benefit both teachers for more efficient scoring of reading exercises and students for accessing reading exercises with feedback more easily. However, there are limited studies on Automatic Speech Recognition (ASR) for child speech in languages other than English, and limited research on ASR-based reading diagnosis systems. This study investigates how efficiently state-of-the-art (SOTA) pretrained ASR models recognize Dutch native children speech and manage to detect reading miscues. We found that Hubert Large finetuned on Dutch speech achieves SOTA phoneme-level child speech recognition (PER at 23.1%), while Whisper (Faster Whisper Large-v2) achieves SOTA word-level performance (WER at 9.8%). Our findings suggest that Wav2Vec2 Large and Whisper are the two best ASR models for reading miscue detection. Specifically, Wav2Vec2 Large shows the highest recall at 0.83, whereas Whisper exhibits the highest precision at 0.52 and an F1 score of 0.52. | [
"['Lingyun Gao' 'Cristian Tejedor-Garcia' 'Helmer Strik'\n 'Catia Cucchiarini']"
] |
null | null | 2406.07072 | null | null | http://arxiv.org/pdf/2406.07072v1 | 2024-06-11T08:59:20Z | 2024-06-11T08:59:20Z | On the relation between trainability and dequantization of variational
quantum learning models | The quest for successful variational quantum machine learning (QML) relies on the design of suitable parametrized quantum circuits (PQCs), as analogues to neural networks in classical machine learning. Successful QML models must fulfill the properties of trainability and non-dequantization, among others. Recent works have highlighted an intricate interplay between trainability and dequantization of such models, which is still unresolved. In this work we contribute to this debate from the perspective of machine learning, proving a number of results identifying, among others when trainability and non-dequantization are not mutually exclusive. We begin by providing a number of new somewhat broader definitions of the relevant concepts, compared to what is found in other literature, which are operationally motivated, and consistent with prior art. With these precise definitions given and motivated, we then study the relation between trainability and dequantization of variational QML. Next, we also discuss the degrees of "variationalness" of QML models, where we distinguish between models like the hardware efficient ansatz and quantum kernel methods. Finally, we introduce recipes for building PQC-based QML models which are both trainable and nondequantizable, and corresponding to different degrees of variationalness. We do not address the practical utility for such models. Our work however does point toward a way forward for finding more general constructions, for which finding applications may become feasible. | [
"['Elies Gil-Fuster' 'Casper Gyurik' 'Adrián Pérez-Salinas' 'Vedran Dunjko']"
] |
null | null | 2406.07083 | null | null | http://arxiv.org/pdf/2406.07083v1 | 2024-06-11T09:16:43Z | 2024-06-11T09:16:43Z | Efficient Mixture Learning in Black-Box Variational Inference | Mixture variational distributions in black box variational inference (BBVI) have demonstrated impressive results in challenging density estimation tasks. However, currently scaling the number of mixture components can lead to a linear increase in the number of learnable parameters and a quadratic increase in inference time due to the evaluation of the evidence lower bound (ELBO). Our two key contributions address these limitations. First, we introduce the novel Multiple Importance Sampling Variational Autoencoder (MISVAE), which amortizes the mapping from input to mixture-parameter space using one-hot encodings. Fortunately, with MISVAE, each additional mixture component incurs a negligible increase in network parameters. Second, we construct two new estimators of the ELBO for mixtures in BBVI, enabling a tremendous reduction in inference time with marginal or even improved impact on performance. Collectively, our contributions enable scalability to hundreds of mixture components and provide superior estimation performance in shorter time, with fewer network parameters compared to previous Mixture VAEs. Experimenting with MISVAE, we achieve astonishing, SOTA results on MNIST. Furthermore, we empirically validate our estimators in other BBVI settings, including Bayesian phylogenetic inference, where we improve inference times for the SOTA mixture model on eight data sets. | [
"['Alexandra Hotti' 'Oskar Kviman' 'Ricky Molén' 'Víctor Elvira'\n 'Jens Lagergren']"
] |
null | null | 2406.07084 | null | null | http://arxiv.org/pdf/2406.07084v1 | 2024-06-11T09:21:50Z | 2024-06-11T09:21:50Z | Leveraging Large Language Models for Efficient Failure Analysis in Game
Development | In games, and more generally in the field of software development, early detection of bugs is vital to maintain a high quality of the final product. Automated tests are a powerful tool that can catch a problem earlier in development by executing periodically. As an example, when new code is submitted to the code base, a new automated test verifies these changes. However, identifying the specific change responsible for a test failure becomes harder when dealing with batches of changes -- especially in the case of a large-scale project such as a AAA game, where thousands of people contribute to a single code base. This paper proposes a new approach to automatically identify which change in the code caused a test to fail. The method leverages Large Language Models (LLMs) to associate error messages with the corresponding code changes causing the failure. We investigate the effectiveness of our approach with quantitative and qualitative evaluations. Our approach reaches an accuracy of 71% in our newly created dataset, which comprises issues reported by developers at EA over a period of one year. We further evaluated our model through a user study to assess the utility and usability of the tool from a developer perspective, resulting in a significant reduction in time -- up to 60% -- spent investigating issues. | [
"['Leonardo Marini' 'Linus Gisslén' 'Alessandro Sestini']"
] |
null | null | 2406.07096 | null | null | http://arxiv.org/pdf/2406.07096v1 | 2024-06-11T09:37:52Z | 2024-06-11T09:37:52Z | Fast Context-Biasing for CTC and Transducer ASR models with CTC-based
Word Spotter | Accurate recognition of rare and new words remains a pressing problem for contextualized Automatic Speech Recognition (ASR) systems. Most context-biasing methods involve modification of the ASR model or the beam-search decoding algorithm, complicating model reuse and slowing down inference. This work presents a new approach to fast context-biasing with CTC-based Word Spotter (CTC-WS) for CTC and Transducer (RNN-T) ASR models. The proposed method matches CTC log-probabilities against a compact context graph to detect potential context-biasing candidates. The valid candidates then replace their greedy recognition counterparts in corresponding frame intervals. A Hybrid Transducer-CTC model enables the CTC-WS application for the Transducer model. The results demonstrate a significant acceleration of the context-biasing recognition with a simultaneous improvement in F-score and WER compared to baseline methods. The proposed method is publicly available in the NVIDIA NeMo toolkit. | [
"['Andrei Andrusenko' 'Aleksandr Laptev' 'Vladimir Bataev'\n 'Vitaly Lavrukhin' 'Boris Ginsburg']"
] |
null | null | 2406.07100 | null | null | http://arxiv.org/pdf/2406.07100v2 | 2024-06-27T13:00:34Z | 2024-06-11T09:42:03Z | D-GRIL: End-to-End Topological Learning with 2-parameter Persistence | End-to-end topological learning using 1-parameter persistence is well-known. We show that the framework can be enhanced using 2-parameter persistence by adopting a recently introduced 2-parameter persistence based vectorization technique called GRIL. We establish a theoretical foundation of differentiating GRIL producing D-GRIL. We show that D-GRIL can be used to learn a bifiltration function on standard benchmark graph datasets. Further, we exhibit that this framework can be applied in the context of bio-activity prediction in drug discovery. | [
"['Soham Mukherjee' 'Shreyas N. Samaga' 'Cheng Xin' 'Steve Oudot'\n 'Tamal K. Dey']"
] |
null | null | 2406.07107 | null | null | http://arxiv.org/pdf/2406.07107v2 | 2024-06-12T01:39:24Z | 2024-06-11T09:49:00Z | Agnostic Sharpness-Aware Minimization | Sharpness-aware minimization (SAM) has been instrumental in improving deep neural network training by minimizing both the training loss and the sharpness of the loss landscape, leading the model into flatter minima that are associated with better generalization properties. In another aspect, Model-Agnostic Meta-Learning (MAML) is a framework designed to improve the adaptability of models. MAML optimizes a set of meta-models that are specifically tailored for quick adaptation to multiple tasks with minimal fine-tuning steps and can generalize well with limited data. In this work, we explore the connection between SAM and MAML, particularly in terms of enhancing model generalization. We introduce Agnostic-SAM, a novel approach that combines the principles of both SAM and MAML. Agnostic-SAM adapts the core idea of SAM by optimizing the model towards wider local minima using training data, while concurrently maintaining low loss values on validation data. By doing so, it seeks flatter minima that are not only robust to small perturbations but also less vulnerable to data distributional shift problems. Our experimental results demonstrate that Agnostic-SAM significantly improves generalization over baselines across a range of datasets and under challenging conditions such as noisy labels and data limitation. | [
"['Van-Anh Nguyen' 'Quyen Tran' 'Tuan Truong' 'Thanh-Toan Do' 'Dinh Phung'\n 'Trung Le']"
] |
null | null | 2406.07115 | null | null | http://arxiv.org/pdf/2406.07115v1 | 2024-06-11T10:00:18Z | 2024-06-11T10:00:18Z | Advancing Tool-Augmented Large Language Models: Integrating Insights
from Errors in Inference Trees | Tool-augmented large language models (LLMs) leverage tools, often in the form of APIs, to enhance their reasoning capabilities on complex tasks, thus taking on the role of intelligent agents interacting with the real world. The recently introduced ToolLLaMA model by Qin et al. [2024] utilizes the depth-first search-based decision tree (DFSDT) method for reasoning with $16000+$ real-world APIs, which effectively improves the planning and inferencing performance of tool-augmented LLMs compared to traditional chain reasoning approaches. However, their approach only employs successful paths from decision trees (also called inference trees) for supervised fine-tuning (SFT) during training, which does not fully exploit the advantages of the tree of thought. In this study, we propose an inference trajectory optimization framework based on the preference data extracted from decision trees to address this limitation. We first introduce a novel method for constructing preference data from the tree of thought, capitalizing on the failed explorations previously overlooked in the trees. Specifically, we generate an effective step-wise preference dataset, named ToolPreference, for tool use based on the ToolBench dataset. In the subsequent training phase, we first fine-tune the LLM with tool-usage expert trajectories and then use these step-wise preference pairs for direct preference optimization (DPO) to update the policy of the LLM, resulting in our ToolPrefer-LLaMA (TP-LLaMA) model. Our experiments demonstrate that by obtaining insights from errors in inference trees, TP-LLaMA significantly outperforms the baselines across almost all test scenarios by a large margin and exhibits better generalization capabilities with unseen APIs. At the same time, TP-LLaMA has also demonstrated superior reasoning efficiency compared to the baselines, making it more suitable for complex tool-usage reasoning tasks. | [
"['Sijia Chen' 'Yibo Wang' 'Yi-Feng Wu' 'Qing-Guo Chen' 'Zhao Xu'\n 'Weihua Luo' 'Kaifu Zhang' 'Lijun Zhang']"
] |
null | null | 2406.07117 | null | null | http://arxiv.org/pdf/2406.07117v1 | 2024-06-11T10:02:07Z | 2024-06-11T10:02:07Z | Augmenting Offline RL with Unlabeled Data | Recent advancements in offline Reinforcement Learning (Offline RL) have led to an increased focus on methods based on conservative policy updates to address the Out-of-Distribution (OOD) issue. These methods typically involve adding behavior regularization or modifying the critic learning objective, focusing primarily on states or actions with substantial dataset support. However, we challenge this prevailing notion by asserting that the absence of an action or state from a dataset does not necessarily imply its suboptimality. In this paper, we propose a novel approach to tackle the OOD problem. We introduce an offline RL teacher-student framework, complemented by a policy similarity measure. This framework enables the student policy to gain insights not only from the offline RL dataset but also from the knowledge transferred by a teacher policy. The teacher policy is trained using another dataset consisting of state-action pairs, which can be viewed as practical domain knowledge acquired without direct interaction with the environment. We believe this additional knowledge is key to effectively solving the OOD issue. This research represents a significant advancement in integrating a teacher-student network into the actor-critic framework, opening new avenues for studies on knowledge transfer in offline RL and effectively addressing the OOD challenge. | [
"['Zhao Wang' 'Briti Gangopadhyay' 'Jia-Fong Yeh' 'Shingo Takamatsu']"
] |
null | null | 2406.07124 | null | null | http://arxiv.org/pdf/2406.07124v1 | 2024-06-11T10:12:10Z | 2024-06-11T10:12:10Z | CHARME: A chain-based reinforcement learning approach for the minor
embedding problem | Quantum Annealing (QA) holds great potential for solving combinatorial optimization problems efficiently. However, the effectiveness of QA algorithms heavily relies on the embedding of problem instances, represented as logical graphs, into the quantum unit processing (QPU) whose topology is in form of a limited connectivity graph, known as the minor embedding Problem. Existing methods for the minor embedding problem suffer from scalability issues when confronted with larger problem sizes. In this paper, we propose a novel approach utilizing Reinforcement Learning (RL) techniques to address the minor embedding problem, named CHARME. CHARME includes three key components: a Graph Neural Network (GNN) architecture for policy modeling, a state transition algorithm ensuring solution validity, and an order exploration strategy for effective training. Through comprehensive experiments on synthetic and real-world instances, we demonstrate that the efficiency of our proposed order exploration strategy as well as our proposed RL framework, CHARME. In details, CHARME yields superior solutions compared to fast embedding methods such as Minorminer and ATOM. Moreover, our method surpasses the OCT-based approach, known for its slower runtime but high-quality solutions, in several cases. In addition, our proposed exploration enhances the efficiency of the training of the CHARME framework by providing better solutions compared to the greedy strategy. | [
"['Hoang M. Ngo' 'Nguyen H K. Do' 'Minh N. Vu' 'Tamer Kahveci' 'My T. Thai']"
] |
null | null | 2406.07125 | null | null | http://arxiv.org/pdf/2406.07125v1 | 2024-06-11T10:16:55Z | 2024-06-11T10:16:55Z | CARACAS: vehiCular ArchitectuRe for detAiled Can Attacks Simulation | Modern vehicles are increasingly vulnerable to attacks that exploit network infrastructures, particularly the Controller Area Network (CAN) networks. To effectively counter such threats using contemporary tools like Intrusion Detection Systems (IDSs) based on data analysis and classification, large datasets of CAN messages become imperative. This paper delves into the feasibility of generating synthetic datasets by harnessing the modeling capabilities of simulation frameworks such as Simulink coupled with a robust representation of attack models to present CARACAS, a vehicular model, including component control via CAN messages and attack injection capabilities. CARACAS showcases the efficacy of this methodology, including a Battery Electric Vehicle (BEV) model, and focuses on attacks targeting torque control in two distinct scenarios. | [
"['Sadek Misto Kirdi' 'Nicola Scarano' 'Franco Oberti' 'Luca Mannella'\n 'Stefano Di Carlo' 'Alessandro Savino']"
] |
null | null | 2406.07126 | null | null | http://arxiv.org/pdf/2406.07126v2 | 2024-07-14T16:19:30Z | 2024-06-11T10:18:58Z | Logical Distillation of Graph Neural Networks | We present a logic based interpretable model for learning on graphs and an algorithm to distill this model from a Graph Neural Network (GNN). Recent results have shown connections between the expressivity of GNNs and the two-variable fragment of first-order logic with counting quantifiers (C2). We introduce a decision-tree based model which leverages an extension of C2 to distill interpretable logical classifiers from GNNs. We test our approach on multiple GNN architectures. The distilled models are interpretable, succinct, and attain similar accuracy to the underlying GNN. Furthermore, when the ground truth is expressible in C2, our approach outperforms the GNN. | [
"['Alexander Pluska' 'Pascal Welke' 'Thomas Gärtner' 'Sagar Malhotra']"
] |
null | null | 2406.07141 | null | null | http://arxiv.org/pdf/2406.07141v1 | 2024-06-11T10:40:54Z | 2024-06-11T10:40:54Z | Identifiable Object-Centric Representation Learning via Probabilistic
Slot Attention | Learning modular object-centric representations is crucial for systematic generalization. Existing methods show promising object-binding capabilities empirically, but theoretical identifiability guarantees remain relatively underdeveloped. Understanding when object-centric representations can theoretically be identified is crucial for scaling slot-based methods to high-dimensional images with correctness guarantees. To that end, we propose a probabilistic slot-attention algorithm that imposes an aggregate mixture prior over object-centric slot representations, thereby providing slot identifiability guarantees without supervision, up to an equivalence relation. We provide empirical verification of our theoretical identifiability result using both simple 2-dimensional data and high-resolution imaging datasets. | [
"['Avinash Kori' 'Francesco Locatello' 'Ainkaran Santhirasekaram'\n 'Francesca Toni' 'Ben Glocker' 'Fabio De Sousa Ribeiro']"
] |
null | null | 2406.07145 | null | null | http://arxiv.org/pdf/2406.07145v2 | 2024-06-13T03:58:32Z | 2024-06-11T10:45:41Z | Failures Are Fated, But Can Be Faded: Characterizing and Mitigating
Unwanted Behaviors in Large-Scale Vision and Language Models | In large deep neural networks that seem to perform surprisingly well on many tasks, we also observe a few failures related to accuracy, social biases, and alignment with human values, among others. Therefore, before deploying these models, it is crucial to characterize this failure landscape for engineers to debug and legislative bodies to audit models. Nevertheless, it is infeasible to exhaustively test for all possible combinations of factors that could lead to a model's failure. In this paper, we introduce a post-hoc method that utilizes emph{deep reinforcement learning} to explore and construct the landscape of failure modes in pre-trained discriminative and generative models. With the aid of limited human feedback, we then demonstrate how to restructure the failure landscape to be more desirable by moving away from the discovered failure modes. We empirically show the effectiveness of the proposed method across common Computer Vision, Natural Language Processing, and Vision-Language tasks. | [
"['Som Sagar' 'Aditya Taparia' 'Ransalu Senanayake']"
] |
null | null | 2406.07160 | null | null | http://arxiv.org/pdf/2406.07160v1 | 2024-06-11T11:08:33Z | 2024-06-11T11:08:33Z | Deep Learning-Based Approach for User Activity Detection with Grant-Free
Random Access in Cell-Free Massive MIMO | Modern wireless networks must reliably support a wide array of connectivity demands, encompassing various user needs across diverse scenarios. Machine-Type Communication (mMTC) is pivotal in these networks, particularly given the challenges posed by massive connectivity and sporadic device activation patterns. Traditional grant-based random access (GB-RA) protocols face limitations due to constrained orthogonal preamble resources. In response, the adoption of grant-free random access (GF-RA) protocols offers a promising solution. This paper explores the application of supervised machine learning models to tackle activity detection issues in scenarios where non-orthogonal preamble design is considered. We introduce a data-driven algorithm specifically designed for user activity detection in Cell-Free Massive Multiple-Input Multiple-Output (CF-mMIMO) networks operating under GF-RA protocols. Additionally, this study presents a novel clustering strategy that simplifies and enhances activity detection accuracy, assesses the resilience of the algorithm to input perturbations, and investigates the effects of adopting floating-to-fixed-point conversion on algorithm performance. Simulations conducted adhere to 3GPP standards, ensuring accurate channel modeling, and employ a deep learning approach to boost the detection capabilities of mMTC GF-RA devices. The results are compelling: the algorithm achieves an exceptional 99% accuracy rate, confirming its efficacy in real-world applications. | [
"['Ali Elkeshawy' 'HaÏfa Farès' 'Amor Nafkha']"
] |
null | null | 2406.07177 | null | null | http://arxiv.org/pdf/2406.07177v1 | 2024-06-11T11:40:12Z | 2024-06-11T11:40:12Z | TernaryLLM: Ternarized Large Language Model | Large language models (LLMs) have achieved remarkable performance on Natural Language Processing (NLP) tasks, but they are hindered by high computational costs and memory requirements. Ternarization, an extreme form of quantization, offers a solution by reducing memory usage and enabling energy-efficient floating-point additions. However, applying ternarization to LLMs faces challenges stemming from outliers in both weights and activations. In this work, observing asymmetric outliers and non-zero means in weights, we introduce Dual Learnable Ternarization (DLT), which enables both scales and shifts to be learnable. We also propose Outlier-Friendly Feature Knowledge Distillation (OFF) to recover the information lost in extremely low-bit quantization. The proposed OFF can incorporate semantic information and is insensitive to outliers. At the core of OFF is maximizing the mutual information between features in ternarized and floating-point models using cosine similarity. Extensive experiments demonstrate that our TernaryLLM surpasses previous low-bit quantization methods on the standard text generation and zero-shot benchmarks for different LLM families. Specifically, for one of the most powerful open-source models, LLaMA-3, our approach (W1.58A16) outperforms the previous state-of-the-art method (W2A16) by 5.8 in terms of perplexity on C4 and by 8.2% in terms of average accuracy on zero-shot tasks. | [
"['Tianqi Chen' 'Zhe Li' 'Weixiang Xu' 'Zeyu Zhu' 'Dong Li' 'Lu Tian'\n 'Emad Barsoum' 'Peisong Wang' 'Jian Cheng']"
] |
null | null | 2406.07213 | null | null | http://arxiv.org/pdf/2406.07213v3 | 2024-06-17T08:51:31Z | 2024-06-11T12:42:41Z | Semantic-Aware Spectrum Sharing in Internet of Vehicles Based on Deep
Reinforcement Learning | This work aims to investigate semantic communication in high-speed mobile Internet of vehicles (IoV) environments, with a focus on the spectrum sharing between vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications. We specifically address spectrum scarcity and network traffic and then propose a semantic-aware spectrum sharing algorithm (SSS) based on the deep reinforcement learning (DRL) soft actor-critic (SAC) approach. Firstly, we delve into the extraction of semantic information. Secondly, we redefine metrics for semantic information in V2V and V2I spectrum sharing in IoV environments, introducing high-speed semantic spectrum efficiency (HSSE) and semantic transmission rate (HSR). Finally, we employ the SAC algorithm for decision optimization in V2V and V2I spectrum sharing based on semantic information. This optimization encompasses the optimal link of V2V and V2I sharing strategies, the transmission power for vehicles sending semantic information and the length of transmitted semantic symbols, aiming at maximizing HSSE of V2I and enhancing success rate of effective semantic information transmission (SRS) of V2V. Experimental results demonstrate that the SSS algorithm outperforms other baseline algorithms, including other traditional-communication-based spectrum sharing algorithms and spectrum sharing algorithm using other reinforcement learning approaches. The SSS algorithm exhibits a 15% increase in HSSE and approximately a 7% increase in SRS. | [
"['Zhiyu Shao' 'Qiong Wu' 'Pingyi Fan' 'Nan Cheng' 'Wen Chen'\n 'Jiangzhou Wang' 'Khaled B. Letaief']"
] |
null | null | 2406.07217 | null | null | http://arxiv.org/pdf/2406.07217v1 | 2024-06-11T12:50:53Z | 2024-06-11T12:50:53Z | A Synthetic Dataset for Personal Attribute Inference | Recently, powerful Large Language Models (LLMs) have become easily accessible to hundreds of millions of users worldwide. However, their strong capabilities and vast world knowledge do not come without associated privacy risks. In this work, we focus on the emerging privacy threat LLMs pose - the ability to accurately infer personal information from online texts. Despite the growing importance of LLM-based author profiling, research in this area has been hampered by a lack of suitable public datasets, largely due to ethical and privacy concerns associated with real personal data. In this work, we take two steps to address this problem: (i) we construct a simulation framework for the popular social media platform Reddit using LLM agents seeded with synthetic personal profiles; (ii) using this framework, we generate SynthPAI, a diverse synthetic dataset of over 7800 comments manually labeled for personal attributes. We validate our dataset with a human study showing that humans barely outperform random guessing on the task of distinguishing our synthetic comments from real ones. Further, we verify that our dataset enables meaningful personal attribute inference research by showing across 18 state-of-the-art LLMs that our synthetic comments allow us to draw the same conclusions as real-world data. Together, this indicates that our dataset and pipeline provide a strong and privacy-preserving basis for future research toward understanding and mitigating the inference-based privacy threats LLMs pose. | [
"['Hanna Yukhymenko' 'Robin Staab' 'Mark Vero' 'Martin Vechev']"
] |
null | null | 2406.07222 | null | null | http://arxiv.org/pdf/2406.07222v1 | 2024-06-11T13:01:50Z | 2024-06-11T13:01:50Z | Improving Autoformalization using Type Checking | Large language models show promise for autoformalization, the task of automatically translating natural language into formal languages. However, current autoformalization methods remain limited. The last reported state-of-the-art performance on the ProofNet formalization benchmark for the Lean proof assistant, achieved using Codex for Lean 3, only showed successful formalization of 16.1% of informal statements. Similarly, our evaluation of GPT-4o for Lean 4 only produces successful translations 34.9% of the time. Our analysis shows that the performance of these models is largely limited by their inability to generate formal statements that successfully type-check (i.e., are syntactically correct and consistent with types) - with a whopping 86.6% of GPT-4o errors starting from a type-check failure. In this work, we propose a method to fix this issue through decoding with type-check filtering, where we initially sample a diverse set of candidate formalizations for an informal statement, then use the Lean proof assistant to filter out candidates that do not type-check. Using GPT-4o as a base model, and combining our method with self-consistency, we obtain a +18.3% absolute increase in formalization accuracy, and achieve a new state-of-the-art of 53.2% on ProofNet with Lean 4. | [
"['Auguste Poiroux' 'Gail Weiss' 'Viktor Kunčak' 'Antoine Bosselut']"
] |
null | null | 2406.07234 | null | null | http://arxiv.org/pdf/2406.07234v2 | 2024-06-18T16:46:21Z | 2024-06-11T13:12:39Z | OPFData: Large-scale datasets for AC optimal power flow with topological
perturbations | Solving the AC optimal power flow problem (AC-OPF) is critical to the efficient and safe planning and operation of power grids. Small efficiency improvements in this domain have the potential to lead to billions of dollars of cost savings, and significant reductions in emissions from fossil fuel generators. Recent work on data-driven solution methods for AC-OPF shows the potential for large speed improvements compared to traditional solvers; however, no large-scale open datasets for this problem exist. We present the largest readily-available collection of solved AC-OPF problems to date. This collection is orders of magnitude larger than existing readily-available datasets, allowing training of high-capacity data-driven models. Uniquely, it includes topological perturbations - a critical requirement for usage in realistic power grid operations. We hope this resource will spur the community to scale research to larger grid sizes with variable topology. | [
"['Sean Lovett' 'Miha Zgubic' 'Sofia Liguori' 'Sephora Madjiheurem'\n 'Hamish Tomlinson' 'Sophie Elster' 'Chris Apps' 'Sims Witherspoon'\n 'Luis Piloto']"
] |
null | null | 2406.07236 | null | null | http://arxiv.org/pdf/2406.07236v1 | 2024-06-11T13:14:04Z | 2024-06-11T13:14:04Z | Let Go of Your Labels with Unsupervised Transfer | Foundation vision-language models have enabled remarkable zero-shot transferability of the pre-trained representations to a wide range of downstream tasks. However, to solve a new task, zero-shot transfer still necessitates human guidance to define visual categories that appear in the data. Here, we show that fully unsupervised transfer emerges when searching for the labeling of a dataset that induces maximal margin classifiers in representation spaces of different foundation models. We present TURTLE, a fully unsupervised method that effectively employs this guiding principle to uncover the underlying labeling of a downstream dataset without any supervision and task-specific representation learning. We evaluate TURTLE on a diverse benchmark suite of 26 datasets and show that it achieves new state-of-the-art unsupervised performance. Furthermore, TURTLE, although being fully unsupervised, outperforms zero-shot transfer baselines on a wide range of datasets. In particular, TURTLE matches the average performance of CLIP zero-shot on 26 datasets by employing the same representation space, spanning a wide range of architectures and model sizes. By guiding the search for the underlying labeling using the representation spaces of two foundation models, TURTLE surpasses zero-shot transfer and unsupervised prompt tuning baselines, demonstrating the surprising power and effectiveness of unsupervised transfer. | [
"['Artyom Gadetsky' 'Yulun Jiang' 'Maria Brbic']"
] |
null | null | 2406.07246 | null | null | http://arxiv.org/pdf/2406.07246v1 | 2024-06-11T13:28:43Z | 2024-06-11T13:28:43Z | Marginalization Consistent Mixture of Separable Flows for Probabilistic
Irregular Time Series Forecasting | Probabilistic forecasting models for joint distributions of targets in irregular time series are a heavily under-researched area in machine learning with, to the best of our knowledge, only three models researched so far: GPR, the Gaussian Process Regression model~citep{Durichen2015.Multitask}, TACTiS, the Transformer-Attentional Copulas for Time Series~cite{Drouin2022.Tactis, ashok2024tactis} and ProFITi citep{Yalavarthi2024.Probabilistica}, a multivariate normalizing flow model based on invertible attention layers. While ProFITi, thanks to using multivariate normalizing flows, is the more expressive model with better predictive performance, we will show that it suffers from marginalization inconsistency: it does not guarantee that the marginal distributions of a subset of variables in its predictive distributions coincide with the directly predicted distributions of these variables. Also, TACTiS does not provide any guarantees for marginalization consistency. We develop a novel probabilistic irregular time series forecasting model, Marginalization Consistent Mixtures of Separable Flows (moses), that mixes several normalizing flows with (i) Gaussian Processes with full covariance matrix as source distributions and (ii) a separable invertible transformation, aiming to combine the expressivity of normalizing flows with the marginalization consistency of Gaussians. In experiments on four different datasets we show that moses outperforms other state-of-the-art marginalization consistent models, performs on par with ProFITi, but different from ProFITi, guarantee marginalization consistency. | [
"['Vijaya Krishna Yalavarthi' 'Randolf Scholz' 'Kiran Madhusudhanan'\n 'Stefan Born' 'Lars Schmidt-Thieme']"
] |
null | null | 2406.07247 | null | null | http://arxiv.org/pdf/2406.07247v1 | 2024-06-11T13:29:34Z | 2024-06-11T13:29:34Z | Dynamical Mean-Field Theory of Self-Attention Neural Networks | Transformer-based models have demonstrated exceptional performance across diverse domains, becoming the state-of-the-art solution for addressing sequential machine learning problems. Even though we have a general understanding of the fundamental components in the transformer architecture, little is known about how they operate or what are their expected dynamics. Recently, there has been an increasing interest in exploring the relationship between attention mechanisms and Hopfield networks, promising to shed light on the statistical physics of transformer networks. However, to date, the dynamical regimes of transformer-like models have not been studied in depth. In this paper, we address this gap by using methods for the study of asymmetric Hopfield networks in nonequilibrium regimes --namely path integral methods over generating functionals, yielding dynamics governed by concurrent mean-field variables. Assuming 1-bit tokens and weights, we derive analytical approximations for the behavior of large self-attention neural networks coupled to a softmax output, which become exact in the large limit size. Our findings reveal nontrivial dynamical phenomena, including nonequilibrium phase transitions associated with chaotic bifurcations, even for very simple configurations with a few encoded features and a very short context window. Finally, we discuss the potential of our analytic approach to improve our understanding of the inner workings of transformer models, potentially reducing computational training costs and enhancing model interpretability. | [
"['Ángel Poc-López' 'Miguel Aguilera']"
] |
null | null | 2406.07250 | null | null | http://arxiv.org/pdf/2406.07250v1 | 2024-06-11T13:32:40Z | 2024-06-11T13:32:40Z | Description and Discussion on DCASE 2024 Challenge Task 2: First-Shot
Unsupervised Anomalous Sound Detection for Machine Condition Monitoring | We present the task description of the Detection and Classification of Acoustic Scenes and Events (DCASE) 2024 Challenge Task 2: First-shot unsupervised anomalous sound detection (ASD) for machine condition monitoring. Continuing from last year's DCASE 2023 Challenge Task 2, we organize the task as a first-shot problem under domain generalization required settings. The main goal of the first-shot problem is to enable rapid deployment of ASD systems for new kinds of machines without the need for machine-specific hyperparameter tunings. This problem setting was realized by (1) giving only one section for each machine type and (2) having completely different machine types for the development and evaluation datasets. For the DCASE 2024 Challenge Task 2, data of completely new machine types were newly collected and provided as the evaluation dataset. In addition, attribute information such as the machine operation conditions were concealed for several machine types to mimic situations where such information are unavailable. We will add challenge results and analysis of the submissions after the challenge submission deadline. | [
"['Tomoya Nishida' 'Noboru Harada' 'Daisuke Niizumi' 'Davide Albertini'\n 'Roberto Sannino' 'Simone Pradolini' 'Filippo Augusti' 'Keisuke Imoto'\n 'Kota Dohi' 'Harsh Purohit' 'Takashi Endo' 'Yohei Kawaguchi']"
] |
null | null | 2406.07253 | null | null | http://arxiv.org/pdf/2406.07253v1 | 2024-06-11T13:34:05Z | 2024-06-11T13:34:05Z | Hybrid Reinforcement Learning from Offline Observation Alone | We consider the hybrid reinforcement learning setting where the agent has access to both offline data and online interactive access. While Reinforcement Learning (RL) research typically assumes offline data contains complete action, reward and transition information, datasets with only state information (also known as observation-only datasets) are more general, abundant and practical. This motivates our study of the hybrid RL with observation-only offline dataset framework. While the task of competing with the best policy "covered" by the offline data can be solved if a reset model of the environment is provided (i.e., one that can be reset to any state), we show evidence of hardness when only given the weaker trace model (i.e., one can only reset to the initial states and must produce full traces through the environment), without further assumption of admissibility of the offline data. Under the admissibility assumptions -- that the offline data could actually be produced by the policy class we consider -- we propose the first algorithm in the trace model setting that provably matches the performance of algorithms that leverage a reset model. We also perform proof-of-concept experiments that suggest the effectiveness of our algorithm in practice. | [
"['Yuda Song' 'J. Andrew Bagnell' 'Aarti Singh']"
] |
null | null | 2406.07259 | null | null | http://arxiv.org/pdf/2406.07259v1 | 2024-06-11T13:39:07Z | 2024-06-11T13:39:07Z | Scientific Computing with Large Language Models | We provide an overview of the emergence of large language models for scientific computing applications. We highlight use cases that involve natural language processing of scientific documents and specialized languages designed to describe physical systems. For the former, chatbot style applications appear in medicine, mathematics and physics and can be used iteratively with domain experts for problem solving. We also review specialized languages within molecular biology, the languages of molecules, proteins, and DNA where language models are being used to predict properties and even create novel physical systems at much faster rates than traditional computing methods. | [
"['Christopher Culver' 'Peter Hicks' 'Mihailo Milenkovic'\n 'Sanjif Shanmugavelu' 'Tobias Becker']"
] |
null | null | 2406.07263 | null | null | http://arxiv.org/pdf/2406.07263v1 | 2024-06-11T13:42:49Z | 2024-06-11T13:42:49Z | Active learning for affinity prediction of antibodies | The primary objective of most lead optimization campaigns is to enhance the binding affinity of ligands. For large molecules such as antibodies, identifying mutations that enhance antibody affinity is particularly challenging due to the combinatorial explosion of potential mutations. When the structure of the antibody-antigen complex is available, relative binding free energy (RBFE) methods can offer valuable insights into how different mutations will impact the potency and selectivity of a drug candidate, thereby reducing the reliance on costly and time-consuming wet-lab experiments. However, accurately simulating the physics of large molecules is computationally intensive. We present an active learning framework that iteratively proposes promising sequences for simulators to evaluate, thereby accelerating the search for improved binders. We explore different modeling approaches to identify the most effective surrogate model for this task, and evaluate our framework both using pre-computed pools of data and in a realistic full-loop setting. | [
"['Alexandra Gessner' 'Sebastian W. Ober' 'Owen Vickery' 'Dino Oglić'\n 'Talip Uçar']"
] |
null | null | 2406.07266 | null | null | http://arxiv.org/pdf/2406.07266v2 | 2024-06-25T11:42:09Z | 2024-06-11T13:51:51Z | Efficient 3D Molecular Generation with Flow Matching and Scale Optimal
Transport | Generative models for 3D drug design have gained prominence recently for their potential to design ligands directly within protein pockets. Current approaches, however, often suffer from very slow sampling times or generate molecules with poor chemical validity. Addressing these limitations, we propose Semla, a scalable E(3)-equivariant message passing architecture. We further introduce a molecular generation model, SemlaFlow, which is trained using flow matching along with scale optimal transport, a novel extension of equivariant optimal transport. Our model produces state-of-the-art results on benchmark datasets with just 100 sampling steps. Crucially, SemlaFlow samples high quality molecules with as few as 20 steps, corresponding to a two order-of-magnitude speed-up compared to state-of-the-art, without sacrificing performance. Furthermore, we highlight limitations of current evaluation methods for 3D generation and propose new benchmark metrics for unconditional molecular generators. Finally, using these new metrics, we compare our model's ability to generate high quality samples against current approaches and further demonstrate SemlaFlow's strong performance. | [
"['Ross Irwin' 'Alessandro Tibo' 'Jon Paul Janet' 'Simon Olsson']"
] |
null | null | 2406.07291 | null | null | http://arxiv.org/pdf/2406.07291v1 | 2024-06-11T14:22:37Z | 2024-06-11T14:22:37Z | Joint Learning of Context and Feedback Embeddings in Spoken Dialogue | Short feedback responses, such as backchannels, play an important role in spoken dialogue. So far, most of the modeling of feedback responses has focused on their timing, often neglecting how their lexical and prosodic form influence their contextual appropriateness and conversational function. In this paper, we investigate the possibility of embedding short dialogue contexts and feedback responses in the same representation space using a contrastive learning objective. In our evaluation, we primarily focus on how such embeddings can be used as a context-feedback appropriateness metric and thus for feedback response ranking in U.S. English dialogues. Our results show that the model outperforms humans given the same ranking task and that the learned embeddings carry information about the conversational function of feedback responses. | [
"['Livia Qian' 'Gabriel Skantze']"
] |
null | null | 2406.07295 | null | null | http://arxiv.org/pdf/2406.07295v2 | 2024-06-12T13:55:30Z | 2024-06-11T14:24:00Z | Multi-objective Reinforcement learning from AI Feedback | This paper presents Multi-Objective Reinforcement Learning from AI Feedback (MORLAIF), a novel approach to improving the alignment and performance of language models trained using reinforcement learning from AI feedback (RLAIF). In contrast to standard approaches that train a single preference model to represent all human preferences, MORLAIF decomposes this task into multiple simpler principles, such as toxicity, factuality, and sycophancy. Separate preference models are trained for each principle using feedback from GPT-3.5-Turbo. These preference model scores are then combined using different scalarization functions to provide a reward signal for Proximal Policy Optimization (PPO) training of the target language model. Our experiments indicate that MORLAIF outperforms the standard RLAIF baselines and that MORLAIF can be used to align larger language models using smaller ones. Surprisingly, the choice of scalarization function does not appear to significantly impact the results. | [
"['Marcus Williams']"
] |
null | null | 2406.07302 | null | null | http://arxiv.org/pdf/2406.07302v1 | 2024-06-11T14:30:34Z | 2024-06-11T14:30:34Z | BertaQA: How Much Do Language Models Know About Local Culture? | Large Language Models (LLMs) exhibit extensive knowledge about the world, but most evaluations have been limited to global or anglocentric subjects. This raises the question of how well these models perform on topics relevant to other cultures, whose presence on the web is not that prominent. To address this gap, we introduce BertaQA, a multiple-choice trivia dataset that is parallel in English and Basque. The dataset consists of a local subset with questions pertinent to the Basque culture, and a global subset with questions of broader interest. We find that state-of-the-art LLMs struggle with local cultural knowledge, even as they excel on global topics. However, we show that continued pre-training in Basque significantly improves the models' performance on Basque culture, even when queried in English. To our knowledge, this is the first solid evidence of knowledge transfer from a low-resource to a high-resource language. Our analysis sheds light on the complex interplay between language and knowledge, and reveals that some prior findings do not fully hold when reassessed on local topics. Our dataset and evaluation code are available under open licenses at https://github.com/juletx/BertaQA. | [
"['Julen Etxaniz' 'Gorka Azkune' 'Aitor Soroa' 'Oier Lopez de Lacalle'\n 'Mikel Artetxe']"
] |
null | null | 2406.07314 | null | null | http://arxiv.org/pdf/2406.07314v1 | 2024-06-11T14:44:37Z | 2024-06-11T14:44:37Z | Rethinking the impact of noisy labels in graph classification: A utility
and privacy perspective | Graph neural networks based on message-passing mechanisms have achieved advanced results in graph classification tasks. However, their generalization performance degrades when noisy labels are present in the training data. Most existing noisy labeling approaches focus on the visual domain or graph node classification tasks and analyze the impact of noisy labels only from a utility perspective. Unlike existing work, in this paper, we measure the effects of noise labels on graph classification from data privacy and model utility perspectives. We find that noise labels degrade the model's generalization performance and enhance the ability of membership inference attacks on graph data privacy. To this end, we propose the robust graph neural network approach with noisy labeled graph classification. Specifically, we first accurately filter the noisy samples by high-confidence samples and the first feature principal component vector of each class. Then, the robust principal component vectors and the model output under data augmentation are utilized to achieve noise label correction guided by dual spatial information. Finally, supervised graph contrastive learning is introduced to enhance the embedding quality of the model and protect the privacy of the training graph data. The utility and privacy of the proposed method are validated by comparing twelve different methods on eight real graph classification datasets. Compared with the state-of-the-art methods, the RGLC method achieves at most and at least 7.8% and 0.8% performance gain at 30% noisy labeling rate, respectively, and reduces the accuracy of privacy attacks to below 60%. | [
"['De Li' 'Xianxian Li' 'Zeming Gan' 'Qiyu Li' 'Bin Qu' 'Jinyan Wang']"
] |
null | null | 2406.07325 | null | null | http://arxiv.org/pdf/2406.07325v1 | 2024-06-11T14:59:18Z | 2024-06-11T14:59:18Z | Beyond Training: Optimizing Reinforcement Learning Based Job Shop
Scheduling Through Adaptive Action Sampling | Learned construction heuristics for scheduling problems have become increasingly competitive with established solvers and heuristics in recent years. In particular, significant improvements have been observed in solution approaches using deep reinforcement learning (DRL). While much attention has been paid to the design of network architectures and training algorithms to achieve state-of-the-art results, little research has investigated the optimal use of trained DRL agents during inference. Our work is based on the hypothesis that, similar to search algorithms, the utilization of trained DRL agents should be dependent on the acceptable computational budget. We propose a simple yet effective parameterization, called $delta$-sampling that manipulates the trained action vector to bias agent behavior towards exploration or exploitation during solution construction. By following this approach, we can achieve a more comprehensive coverage of the search space while still generating an acceptable number of solutions. In addition, we propose an algorithm for obtaining the optimal parameterization for such a given number of solutions and any given trained agent. Experiments extending existing training protocols for job shop scheduling problems with our inference method validate our hypothesis and result in the expected improvements of the generated solutions. | [
"['Constantin Waubert de Puiseau' 'Christian Dörpelkus' 'Jannik Peters'\n 'Hasan Tercan' 'Tobias Meisen']"
] |
null | null | 2406.07327 | null | null | http://arxiv.org/pdf/2406.07327v1 | 2024-06-11T14:59:24Z | 2024-06-11T14:59:24Z | 3D-Properties: Identifying Challenges in DPO and Charting a Path Forward | Aligning large language models (LLMs) with human preference has recently gained tremendous attention, with the canonical yet costly RLHF-PPO and the simple and straightforward Direct Preference Optimization (DPO) as two examples. Despite the efficiency, DPO has rarely be used in the state-of-the-art production-level LLMs, implying its potential pathologies. In this work, we revisit DPO with a comprehensive examination of its empirical efficacy and a systematic comparison with RLHF-PPO. We identify the textbf{3D}-properties of DPO's learning outcomes: the textbf{D}rastic drop in the likelihood of rejected responses, the textbf{D}egradation into LLM unlearning, and the textbf{D}ispersion effect on unseen responses through experiments with both a carefully designed toy model and practical LLMs on tasks including mathematical problem-solving and instruction following. These findings inherently connect to some observations made by related works and we additionally contribute a plausible theoretical explanation for them. Accordingly, we propose easy regularization methods to mitigate the issues caused by textbf{3D}-properties, improving the training stability and final performance of DPO. Our contributions also include an investigation into how the distribution of the paired preference data impacts the effectiveness of DPO. We hope this work could offer research directions to narrow the gap between reward-free preference learning methods and reward-based ones. | [
"['Yuzi Yan' 'Yibo Miao' 'Jialian Li' 'Yipin Zhang' 'Jian Xie'\n 'Zhijie Deng' 'Dong Yan']"
] |
null | null | 2406.07328 | null | null | http://arxiv.org/pdf/2406.07328v1 | 2024-06-11T14:59:29Z | 2024-06-11T14:59:29Z | Realistic Data Generation for 6D Pose Estimation of Surgical Instruments | Automation in surgical robotics has the potential to improve patient safety and surgical efficiency, but it is difficult to achieve due to the need for robust perception algorithms. In particular, 6D pose estimation of surgical instruments is critical to enable the automatic execution of surgical maneuvers based on visual feedback. In recent years, supervised deep learning algorithms have shown increasingly better performance at 6D pose estimation tasks; yet, their success depends on the availability of large amounts of annotated data. In household and industrial settings, synthetic data, generated with 3D computer graphics software, has been shown as an alternative to minimize annotation costs of 6D pose datasets. However, this strategy does not translate well to surgical domains as commercial graphics software have limited tools to generate images depicting realistic instrument-tissue interactions. To address these limitations, we propose an improved simulation environment for surgical robotics that enables the automatic generation of large and diverse datasets for 6D pose estimation of surgical instruments. Among the improvements, we developed an automated data generation pipeline and an improved surgical scene. To show the applicability of our system, we generated a dataset of 7.5k images with pose annotations of a surgical needle that was used to evaluate a state-of-the-art pose estimation network. The trained model obtained a mean translational error of 2.59mm on a challenging dataset that presented varying levels of occlusion. These results highlight our pipeline's success in training and evaluating novel vision algorithms for surgical robotics applications. | [
"['Juan Antonio Barragan' 'Jintan Zhang' 'Haoying Zhou' 'Adnan Munawar'\n 'Peter Kazanzides']"
] |
null | null | 2406.07337 | null | null | http://arxiv.org/pdf/2406.07337v1 | 2024-06-11T15:06:15Z | 2024-06-11T15:06:15Z | Transferring Knowledge from Large Foundation Models to Small Downstream
Models | How do we transfer the relevant knowledge from ever larger foundation models into small, task-specific downstream models that can run at much lower costs? Standard transfer learning using pre-trained weights as the initialization transfers limited information and commits us to often massive pre-trained architectures. This procedure also precludes combining multiple pre-trained models that learn complementary information. To address these shortcomings, we introduce Adaptive Feature Transfer (AFT). Instead of transferring weights, AFT operates purely on features, thereby decoupling the choice of the pre-trained model from the smaller downstream model. Rather than indiscriminately compressing all pre-trained features, AFT adaptively transfers pre-trained features that are most useful for performing the downstream task, using a simple regularization that adds minimal overhead. Across multiple vision, language, and multi-modal datasets, AFT achieves significantly better downstream performance compared to alternatives with a similar computational cost. Furthermore, AFT reliably translates improvement in pre-trained models into improvement in downstream performance, even if the downstream model is over $50times$ smaller, and can effectively transfer complementary information learned by multiple pre-trained models. | [
"['Shikai Qiu' 'Boran Han' 'Danielle C. Maddix' 'Shuai Zhang' 'Yuyang Wang'\n 'Andrew Gordon Wilson']"
] |
null | null | 2406.07348 | null | null | http://arxiv.org/pdf/2406.07348v3 | 2024-06-16T04:33:17Z | 2024-06-11T15:15:33Z | DR-RAG: Applying Dynamic Document Relevance to Retrieval-Augmented
Generation for Question-Answering | Retrieval-Augmented Generation (RAG) has recently demonstrated the performance of Large Language Models (LLMs) in the knowledge-intensive tasks such as Question-Answering (QA). RAG expands the query context by incorporating external knowledge bases to enhance the response accuracy. However, it would be inefficient to access LLMs multiple times for each query and unreliable to retrieve all the relevant documents by a single query. We have found that even though there is low relevance between some critical documents and query, it is possible to retrieve the remaining documents by combining parts of the documents with the query. To mine the relevance, a two-stage retrieval framework called Dynamic-Relevant Retrieval-Augmented Generation (DR-RAG) is proposed to improve document retrieval recall and the accuracy of answers while maintaining efficiency. Additionally, a compact classifier is applied to two different selection strategies to determine the contribution of the retrieved documents to answering the query and retrieve the relatively relevant documents. Meanwhile, DR-RAG call the LLMs only once, which significantly improves the efficiency of the experiment. The experimental results on multi-hop QA datasets show that DR-RAG can significantly improve the accuracy of the answers and achieve new progress in QA systems. | [
"['Zijian Hei' 'Weiling Liu' 'Wenjie Ou' 'Juyi Qiao' 'Junming Jiao'\n 'Guowen Song' 'Ting Tian' 'Yi Lin']"
] |
null | null | 2406.07358 | null | null | http://arxiv.org/pdf/2406.07358v3 | 2024-06-14T22:24:40Z | 2024-06-11T15:26:57Z | AI Sandbagging: Language Models can Strategically Underperform on
Evaluations | Trustworthy capability evaluations are crucial for ensuring the safety of AI systems, and are becoming a key component of AI regulation. However, the developers of an AI system, or the AI system itself, may have incentives for evaluations to understate the AI's actual capability. These conflicting interests lead to the problem of sandbagging $unicode{x2013}$ which we define as "strategic underperformance on an evaluation". In this paper we assess sandbagging capabilities in contemporary language models (LMs). We prompt frontier LMs, like GPT-4 and Claude 3 Opus, to selectively underperform on dangerous capability evaluations, while maintaining performance on general (harmless) capability evaluations. Moreover, we find that models can be fine-tuned, on a synthetic dataset, to hide specific capabilities unless given a password. This behaviour generalizes to high-quality, held-out benchmarks such as WMDP. In addition, we show that both frontier and smaller models can be prompted, or password-locked, to target specific scores on a capability evaluation. Even more, we found that a capable password-locked model (Llama 3 70b) is reasonably able to emulate a less capable model (Llama 2 7b). Overall, our results suggest that capability evaluations are vulnerable to sandbagging. This vulnerability decreases the trustworthiness of evaluations, and thereby undermines important safety decisions regarding the development and deployment of advanced AI systems. | [
"['Teun van der Weij' 'Felix Hofstätter' 'Ollie Jaffe' 'Samuel F. Brown'\n 'Francis Rhys Ward']"
] |
null | null | 2406.07361 | null | null | http://arxiv.org/pdf/2406.07361v1 | 2024-06-11T15:28:48Z | 2024-06-11T15:28:48Z | Deep Implicit Optimization for Robust and Flexible Image Registration | Deep Learning in Image Registration (DLIR) methods have been tremendously successful in image registration due to their speed and ability to incorporate weak label supervision at training time. However, DLIR methods forego many of the benefits of classical optimization-based methods. The functional nature of deep networks do not guarantee that the predicted transformation is a local minima of the registration objective, the representation of the transformation (displacement/velocity field/affine) is fixed, and the networks are not robust to domain shift. Our method aims to bridge this gap between classical and learning methods by incorporating optimization as a layer in a deep network. A deep network is trained to predict multi-scale dense feature images that are registered using a black box iterative optimization solver. This optimal warp is then used to minimize image and label alignment errors. By implicitly differentiating end-to-end through an iterative optimization solver, our learned features are registration and label-aware, and the warp functions are guaranteed to be local minima of the registration objective in the feature space. Our framework shows excellent performance on in-domain datasets, and is agnostic to domain shift such as anisotropy and varying intensity profiles. For the first time, our method allows switching between arbitrary transformation representations (free-form to diffeomorphic) at test time with zero retraining. End-to-end feature learning also facilitates interpretability of features, and out-of-the-box promptability using additional label-fidelity terms at inference. | [
"['Rohit Jena' 'Pratik Chaudhari' 'James C. Gee']"
] |
null | null | 2406.07368 | null | null | http://arxiv.org/pdf/2406.07368v1 | 2024-06-11T15:34:43Z | 2024-06-11T15:34:43Z | When Linear Attention Meets Autoregressive Decoding: Towards More
Effective and Efficient Linearized Large Language Models | Autoregressive Large Language Models (LLMs) have achieved impressive performance in language tasks but face two significant bottlenecks: (1) quadratic complexity in the attention module as the number of tokens increases, and (2) limited efficiency due to the sequential processing nature of autoregressive LLMs during generation. While linear attention and speculative decoding offer potential solutions, their applicability and synergistic potential for enhancing autoregressive LLMs remain uncertain. We conduct the first comprehensive study on the efficacy of existing linear attention methods for autoregressive LLMs, integrating them with speculative decoding. We introduce an augmentation technique for linear attention that ensures compatibility with speculative decoding, enabling more efficient training and serving of LLMs. Extensive experiments and ablation studies involving seven existing linear attention models and five encoder/decoder-based LLMs consistently validate the effectiveness of our augmented linearized LLMs. Notably, our approach achieves up to a 6.67 reduction in perplexity on the LLaMA model and up to a 2$times$ speedup during generation compared to prior linear attention methods. Codes and models are available at https://github.com/GATECH-EIC/Linearized-LLM. | [
"['Haoran You' 'Yichao Fu' 'Zheng Wang' 'Amir Yazdanbakhsh' 'Yingyan' 'Lin']"
] |
null | null | 2406.07373 | null | null | http://arxiv.org/pdf/2406.07373v1 | 2024-06-11T15:41:48Z | 2024-06-11T15:41:48Z | Closing the Computational-Query Depth Gap in Parallel Stochastic Convex
Optimization | We develop a new parallel algorithm for minimizing Lipschitz, convex functions with a stochastic subgradient oracle. The total number of queries made and the query depth, i.e., the number of parallel rounds of queries, match the prior state-of-the-art, [CJJLLST23], while improving upon the computational depth by a polynomial factor for sufficiently small accuracy. When combined with previous state-of-the-art methods our result closes a gap between the best-known query depth and the best-known computational depth of parallel algorithms. Our method starts with a ball acceleration framework of previous parallel methods, i.e., [CJJJLST20, ACJJS21], which reduce the problem to minimizing a regularized Gaussian convolution of the function constrained to Euclidean balls. By developing and leveraging new stability properties of the Hessian of this induced function, we depart from prior parallel algorithms and reduce these ball-constrained optimization problems to stochastic unconstrained quadratic minimization problems. Although we are unable to prove concentration of the asymmetric matrices that we use to approximate this Hessian, we nevertheless develop an efficient parallel method for solving these quadratics. Interestingly, our algorithms can be improved using fast matrix multiplication and use nearly-linear work if the matrix multiplication exponent is 2. | [
"['Arun Jambulapati' 'Aaron Sidford' 'Kevin Tian']"
] |
null | null | 2406.07375 | null | null | http://arxiv.org/pdf/2406.07375v1 | 2024-06-11T15:41:56Z | 2024-06-11T15:41:56Z | Improving the realism of robotic surgery simulation through injection of
learning-based estimated errors | The development of algorithms for automation of subtasks during robotic surgery can be accelerated by the availability of realistic simulation environments. In this work, we focus on one aspect of the realism of a surgical simulator, which is the positional accuracy of the robot. In current simulators, robots have perfect or near-perfect accuracy, which is not representative of their physical counterparts. We therefore propose a pair of neural networks, trained by data collected from a physical robot, to estimate both the controller error and the kinematic and non-kinematic error. These error estimates are then injected within the simulator to produce a simulated robot that has the characteristic performance of the physical robot. In this scenario, we believe it is sufficient for the estimated error used in the simulation to have a statistically similar distribution to the actual error of the physical robot. This is less stringent, and therefore more tenable, than the requirement for error compensation of a physical robot, where the estimated error should equal the actual error. Our results demonstrate that error injection reduces the mean position and orientation differences between the simulated and physical robots from 5.0 mm / 3.6 deg to 1.3 mm / 1.7 deg, respectively, which represents reductions by factors of 3.8 and 2.1. | [
"['Juan Antonio Barragan' 'Hisashi Ishida' 'Adnan Munawar'\n 'Peter Kazanzides']"
] |
null | null | 2406.07381 | null | null | http://arxiv.org/pdf/2406.07381v1 | 2024-06-11T15:49:08Z | 2024-06-11T15:49:08Z | World Models with Hints of Large Language Models for Goal Achieving | Reinforcement learning struggles in the face of long-horizon tasks and sparse goals due to the difficulty in manual reward specification. While existing methods address this by adding intrinsic rewards, they may fail to provide meaningful guidance in long-horizon decision-making tasks with large state and action spaces, lacking purposeful exploration. Inspired by human cognition, we propose a new multi-modal model-based RL approach named Dreaming with Large Language Models (DLLM). DLLM integrates the proposed hinting subgoals from the LLMs into the model rollouts to encourage goal discovery and reaching in challenging tasks. By assigning higher intrinsic rewards to samples that align with the hints outlined by the language model during model rollouts, DLLM guides the agent toward meaningful and efficient exploration. Extensive experiments demonstrate that the DLLM outperforms recent methods in various challenging, sparse-reward environments such as HomeGrid, Crafter, and Minecraft by 27.7%, 21.1%, and 9.9%, respectively. | [
"['Zeyuan Liu' 'Ziyu Huan' 'Xiyao Wang' 'Jiafei Lyu' 'Jian Tao' 'Xiu Li'\n 'Furong Huang' 'Huazhe Xu']"
] |
null | null | 2406.07387 | null | null | http://arxiv.org/pdf/2406.07387v1 | 2024-05-09T19:45:49Z | 2024-05-09T19:45:49Z | Machine Learning-Based Channel Prediction for RIS-assisted MIMO Systems
With Channel Aging | Reconfigurable intelligent surfaces (RISs) have emerged as a promising technology to enhance the performance of sixth-generation (6G) and beyond communication systems. The passive nature of RISs and their large number of reflecting elements pose challenges to the channel estimation process. The associated complexity further escalates when the channel coefficients are fast-varying as in scenarios with user mobility. In this paper, we propose an extended channel estimation framework for RIS-assisted multiple-input multiple-output (MIMO) systems based on a convolutional neural network (CNN) integrated with an autoregressive (AR) predictor. The implemented framework is designed for identifying the aging pattern and predicting enhanced estimates of the wireless channels in correlated fast-fading environments. Insightful simulation results demonstrate that our proposed CNN-AR approach is robust to channel aging, exhibiting a high-precision estimation accuracy. The results also show that our approach can achieve high spectral efficiency and low pilot overhead compared to traditional methods. | [
"['Nipuni Ginige' 'Arthur Sousa de Sena' 'Nurul Huda Mahmood'\n 'Nandana Rajatheva' 'Matti Latva-aho']"
] |
null | null | 2406.07389 | null | null | http://arxiv.org/pdf/2406.07389v1 | 2024-05-30T08:23:17Z | 2024-05-30T08:23:17Z | Robust Image Semantic Coding with Learnable CSI Fusion Masking over MIMO
Fading Channels | Though achieving marvelous progress in various scenarios, existing semantic communication frameworks mainly consider single-input single-output Gaussian channels or Rayleigh fading channels, neglecting the widely-used multiple-input multiple-output (MIMO) channels, which hinders the application into practical systems. One common solution to combat MIMO fading is to utilize feedback MIMO channel state information (CSI). In this paper, we incorporate MIMO CSI into system designs from a new perspective and propose the learnable CSI fusion semantic communication (LCFSC) framework, where CSI is treated as side information by the semantic extractor to enhance the semantic coding. To avoid feature fusion due to abrupt combination of CSI with features, we present a non-invasive CSI fusion multi-head attention module inside the Swin Transformer. With the learned attention masking map determined by both source and channel states, more robust attention distribution could be generated. Furthermore, the percentage of mask elements could be flexibly adjusted by the learnable mask ratio, which is produced based on the conditional variational interference in an unsupervised manner. In this way, CSI-aware semantic coding is achieved through learnable CSI fusion masking. Experiment results testify the superiority of LCFSC over traditional schemes and state-of-the-art Swin Transformer-based semantic communication frameworks in MIMO fading channels. | [
"['Bingyan Xie' 'Yongpeng Wu' 'Yuxuan Shi' 'Wenjun Zhang' 'Shuguang Cui'\n 'Merouane Debbah']"
] |
null | null | 2406.07398 | null | null | http://arxiv.org/pdf/2406.07398v1 | 2024-06-11T16:05:15Z | 2024-06-11T16:05:15Z | Visual Representation Learning with Stochastic Frame Prediction | Self-supervised learning of image representations by predicting future frames is a promising direction but still remains a challenge. This is because of the under-determined nature of frame prediction; multiple potential futures can arise from a single current frame. To tackle this challenge, in this paper, we revisit the idea of stochastic video generation that learns to capture uncertainty in frame prediction and explore its effectiveness for representation learning. Specifically, we design a framework that trains a stochastic frame prediction model to learn temporal information between frames. Moreover, to learn dense information within each frame, we introduce an auxiliary masked image modeling objective along with a shared decoder architecture. We find this architecture allows for combining both objectives in a synergistic and compute-efficient manner. We demonstrate the effectiveness of our framework on a variety of tasks from video label propagation and vision-based robot learning domains, such as video segmentation, pose tracking, vision-based robotic locomotion, and manipulation tasks. Code is available on the project webpage: https://sites.google.com/view/2024rsp. | [
"['Huiwon Jang' 'Dongyoung Kim' 'Junsu Kim' 'Jinwoo Shin' 'Pieter Abbeel'\n 'Younggyo Seo']"
] |
null | null | 2406.07399 | null | null | http://arxiv.org/pdf/2406.07399v1 | 2024-06-11T16:07:08Z | 2024-06-11T16:07:08Z | Redefining Automotive Radar Imaging: A Domain-Informed 1D Deep Learning
Approach for High-Resolution and Efficient Performance | Millimeter-wave (mmWave) radars are indispensable for perception tasks of autonomous vehicles, thanks to their resilience in challenging weather conditions. Yet, their deployment is often limited by insufficient spatial resolution for precise semantic scene interpretation. Classical super-resolution techniques adapted from optical imaging inadequately address the distinct characteristics of radar signal data. In response, our study redefines radar imaging super-resolution as a one-dimensional (1D) signal super-resolution spectra estimation problem by harnessing the radar signal processing domain knowledge, introducing innovative data normalization and a domain-informed signal-to-noise ratio (SNR)-guided loss function. Our tailored deep learning network for automotive radar imaging exhibits remarkable scalability, parameter efficiency and fast inference speed, alongside enhanced performance in terms of radar imaging quality and resolution. Extensive testing confirms that our SR-SPECNet sets a new benchmark in producing high-resolution radar range-azimuth images, outperforming existing methods across varied antenna configurations and dataset sizes. Source code and new radar dataset will be made publicly available online. | [
"['Ruxin Zheng' 'Shunqiao Sun' 'Holger Caesar' 'Honglei Chen' 'Jian Li']"
] |
null | null | 2406.07400 | null | null | http://arxiv.org/pdf/2406.07400v1 | 2024-06-11T16:07:24Z | 2024-06-11T16:07:24Z | Guiding LLM Temporal Logic Generation with Explicit Separation of Data
and Control | Temporal logics are powerful tools that are widely used for the synthesis and verification of reactive systems. The recent progress on Large Language Models (LLMs) has the potential to make the process of writing such specifications more accessible. However, writing specifications in temporal logics remains challenging for all but the most expert users. A key question in using LLMs for temporal logic specification engineering is to understand what kind of guidance is most helpful to the LLM and the users to easily produce specifications. Looking specifically at the problem of reactive program synthesis, we explore the impact of providing an LLM with guidance on the separation of control and data--making explicit for the LLM what functionality is relevant for the specification, and treating the remaining functionality as an implementation detail for a series of pre-defined functions and predicates. We present a benchmark set and find that this separation of concerns improves specification generation. Our benchmark provides a test set against which to verify future work in LLM generation of temporal logic specifications. | [
"['William Murphy' 'Nikolaus Holzer' 'Nathan Koenig' 'Leyi Cui'\n 'Raven Rothkopf' 'Feitong Qiao' 'Mark Santolucito']"
] |
null | null | 2406.07402 | null | null | http://arxiv.org/pdf/2406.07402v1 | 2024-06-11T16:08:39Z | 2024-06-11T16:08:39Z | A Survey on Recent Random Walk-based Methods for Embedding Knowledge
Graphs | Machine learning, deep learning, and NLP methods on knowledge graphs are present in different fields and have important roles in various domains from self-driving cars to friend recommendations on social media platforms. However, to apply these methods to knowledge graphs, the data usually needs to be in an acceptable size and format. In fact, knowledge graphs normally have high dimensions and therefore we need to transform them to a low-dimensional vector space. An embedding is a low-dimensional space into which you can translate high dimensional vectors in a way that intrinsic features of the input data are preserved. In this review, we first explain knowledge graphs and their embedding and then review some of the random walk-based embedding methods that have been developed recently. | [
"['Elika Bozorgi' 'Sakher Khalil Alqaiidi' 'Afsaneh Shams'\n 'Hamid Reza Arabnia' 'Krzysztof Kochut']"
] |
null | null | 2406.07404 | null | null | http://arxiv.org/pdf/2406.07404v1 | 2024-06-11T16:10:37Z | 2024-06-11T16:10:37Z | Enhancing Tabular Data Optimization with a Flexible Graph-based
Reinforced Exploration Strategy | Tabular data optimization methods aim to automatically find an optimal feature transformation process that generates high-value features and improves the performance of downstream machine learning tasks. Current frameworks for automated feature transformation rely on iterative sequence generation tasks, optimizing decision strategies through performance feedback from downstream tasks. However, these approaches fail to effectively utilize historical decision-making experiences and overlook potential relationships among generated features, thus limiting the depth of knowledge extraction. Moreover, the granularity of the decision-making process lacks dynamic backtracking capabilities for individual features, leading to insufficient adaptability when encountering inefficient pathways, adversely affecting overall robustness and exploration efficiency. To address the limitations observed in current automatic feature engineering frameworks, we introduce a novel method that utilizes a feature-state transformation graph to effectively preserve the entire feature transformation journey, where each node represents a specific transformation state. During exploration, three cascading agents iteratively select nodes and idea mathematical operations to generate new transformation states. This strategy leverages the inherent properties of the graph structure, allowing for the preservation and reuse of valuable transformations. It also enables backtracking capabilities through graph pruning techniques, which can rectify inefficient transformation paths. To validate the efficacy and flexibility of our approach, we conducted comprehensive experiments and detailed case studies, demonstrating superior performance in diverse scenarios. | [
"['Xiaohan Huang' 'Dongjie Wang' 'Zhiyuan Ning' 'Ziyue Qiao'\n 'Qingqing Long' 'Haowei Zhu' 'Min Wu' 'Yuanchun Zhou' 'Meng Xiao']"
] |
null | null | 2406.07407 | null | null | http://arxiv.org/pdf/2406.07407v1 | 2024-06-11T16:13:09Z | 2024-06-11T16:13:09Z | Private Geometric Median | In this paper, we study differentially private (DP) algorithms for computing the geometric median (GM) of a dataset: Given $n$ points, $x_1,dots,x_n$ in $mathbb{R}^d$, the goal is to find a point $theta$ that minimizes the sum of the Euclidean distances to these points, i.e., $sum_{i=1}^{n} |theta - x_i|_2$. Off-the-shelf methods, such as DP-GD, require strong a priori knowledge locating the data within a ball of radius $R$, and the excess risk of the algorithm depends linearly on $R$. In this paper, we ask: can we design an efficient and private algorithm with an excess error guarantee that scales with the (unknown) radius containing the majority of the datapoints? Our main contribution is a pair of polynomial-time DP algorithms for the task of private GM with an excess error guarantee that scales with the effective diameter of the datapoints. Additionally, we propose an inefficient algorithm based on the inverse smooth sensitivity mechanism, which satisfies the more restrictive notion of pure DP. We complement our results with a lower bound and demonstrate the optimality of our polynomial-time algorithms in terms of sample complexity. | [
"['Mahdi Haghifam' 'Thomas Steinke' 'Jonathan Ullman']"
] |
null | null | 2406.07409 | null | null | http://arxiv.org/pdf/2406.07409v1 | 2024-06-11T16:14:30Z | 2024-06-11T16:14:30Z | Accelerating Ill-conditioned Hankel Matrix Recovery via Structured
Newton-like Descent | This paper studies the robust Hankel recovery problem, which simultaneously removes the sparse outliers and fulfills missing entries from the partial observation. We propose a novel non-convex algorithm, coined Hankel Structured Newton-Like Descent (HSNLD), to tackle the robust Hankel recovery problem. HSNLD is highly efficient with linear convergence, and its convergence rate is independent of the condition number of the underlying Hankel matrix. The recovery guarantee has been established under some mild conditions. Numerical experiments on both synthetic and real datasets show the superior performance of HSNLD against state-of-the-art algorithms. | [
"['HanQin Cai' 'Longxiu Huang' 'Xiliang Lu' 'Juntao You']"
] |
null | null | 2406.07413 | null | null | http://arxiv.org/pdf/2406.07413v1 | 2024-06-11T16:18:15Z | 2024-06-11T16:18:15Z | Holistic Memory Diversification for Incremental Learning in Growing
Graphs | This paper addresses the challenge of incremental learning in growing graphs with increasingly complex tasks. The goal is to continually train a graph model to handle new tasks while retaining its inference ability on previous tasks. Existing methods usually neglect the importance of memory diversity, limiting in effectively selecting high-quality memory from previous tasks and remembering broad previous knowledge within the scarce memory on graphs. To address that, we introduce a novel holistic Diversified Memory Selection and Generation (DMSG) framework for incremental learning in graphs, which first introduces a buffer selection strategy that considers both intra-class and inter-class diversities, employing an efficient greedy algorithm for sampling representative training nodes from graphs into memory buffers after learning each new task. Then, to adequately rememorize the knowledge preserved in the memory buffer when learning new tasks, we propose a diversified memory generation replay method. This method first utilizes a variational layer to generate the distribution of buffer node embeddings and sample synthesized ones for replaying. Furthermore, an adversarial variational embedding learning method and a reconstruction-based decoder are proposed to maintain the integrity and consolidate the generalization of the synthesized node embeddings, respectively. Finally, we evaluate our model on node classification tasks involving increasing class numbers. Extensive experimental results on publicly accessible datasets demonstrate the superiority of DMSG over state-of-the-art methods. | [
"['Ziyue Qiao' 'Junren Xiao' 'Qingqiang Sun' 'Meng Xiao' 'Hui Xiong']"
] |
null | null | 2406.07418 | null | null | http://arxiv.org/pdf/2406.07418v1 | 2024-06-11T16:21:33Z | 2024-06-11T16:21:33Z | Enhanced Gene Selection in Single-Cell Genomics: Pre-Filtering Synergy
and Reinforced Optimization | Recent advancements in single-cell genomics necessitate precision in gene panel selection to interpret complex biological data effectively. Those methods aim to streamline the analysis of scRNA-seq data by focusing on the most informative genes that contribute significantly to the specific analysis task. Traditional selection methods, which often rely on expert domain knowledge, embedded machine learning models, or heuristic-based iterative optimization, are prone to biases and inefficiencies that may obscure critical genomic signals. Recognizing the limitations of traditional methods, we aim to transcend these constraints with a refined strategy. In this study, we introduce an iterative gene panel selection strategy that is applicable to clustering tasks in single-cell genomics. Our method uniquely integrates results from other gene selection algorithms, providing valuable preliminary boundaries or prior knowledge as initial guides in the search space to enhance the efficiency of our framework. Furthermore, we incorporate the stochastic nature of the exploration process in reinforcement learning (RL) and its capability for continuous optimization through reward-based feedback. This combination mitigates the biases inherent in the initial boundaries and harnesses RL's adaptability to refine and target gene panel selection dynamically. To illustrate the effectiveness of our method, we conducted detailed comparative experiments, case studies, and visualization analysis. | [
"['Weiliang Zhang' 'Zhen Meng' 'Dongjie Wang' 'Min Wu' 'Kunpeng Liu'\n 'Yuanchun Zhou' 'Meng Xiao']"
] |
null | null | 2406.07423 | null | null | http://arxiv.org/pdf/2406.07423v1 | 2024-06-11T16:23:33Z | 2024-06-11T16:23:33Z | Beyond ELBOs: A Large-Scale Evaluation of Variational Methods for
Sampling | Monte Carlo methods, Variational Inference, and their combinations play a pivotal role in sampling from intractable probability distributions. However, current studies lack a unified evaluation framework, relying on disparate performance measures and limited method comparisons across diverse tasks, complicating the assessment of progress and hindering the decision-making of practitioners. In response to these challenges, our work introduces a benchmark that evaluates sampling methods using a standardized task suite and a broad range of performance criteria. Moreover, we study existing metrics for quantifying mode collapse and introduce novel metrics for this purpose. Our findings provide insights into strengths and weaknesses of existing sampling methods, serving as a valuable reference for future developments. The code is publicly available here. | [
"['Denis Blessing' 'Xiaogang Jia' 'Johannes Esslinger' 'Francisco Vargas'\n 'Gerhard Neumann']"
] |
null | null | 2406.07428 | null | null | http://arxiv.org/pdf/2406.07428v1 | 2024-06-11T16:30:30Z | 2024-06-11T16:30:30Z | GemNet: Menu-Based, Strategy-Proof Multi-Bidder Auctions Through Deep
Learning | Differentiable economics uses deep learning for automated mechanism design. Despite strong progress, it has remained an open problem to learn multi-bidder, general, and fully strategy-proof (SP) auctions. We introduce GEneral Menu-based NETwork (GemNet), which significantly extends the menu-based approach of RochetNet [D"utting et al., 2023] to the multi-bidder setting. The challenge in achieving SP is to learn bidder-independent menus that are feasible, so that the optimal menu choices for each bidder do not over-allocate items when taken together (we call this menu compatibility). GemNet penalizes the failure of menu compatibility during training, and transforms learned menus after training through price changes, by considering a set of discretized bidder values and reasoning about Lipschitz smoothness to guarantee menu compatibility on the entire value space. This approach is general, leaving undisturbed trained menus that already satisfy menu compatibility and reducing to RochetNet for a single bidder. Mixed-integer linear programs are used for menu transforms and through a number of optimizations, including adaptive grids and methods to skip menu elements, we scale to large auction design problems. GemNet learns auctions with better revenue than affine maximization methods, achieves exact SP whereas previous general multi-bidder methods are approximately SP, and offers greatly enhanced interpretability. | [
"['Tonghan Wang' 'Yanchen Jiang' 'David C. Parkes']"
] |
null | null | 2406.07435 | null | null | http://arxiv.org/pdf/2406.07435v1 | 2024-06-11T16:42:17Z | 2024-06-11T16:42:17Z | Beware of Aliases -- Signal Preservation is Crucial for Robust Image
Restoration | Image restoration networks are usually comprised of an encoder and a decoder, responsible for aggregating image content from noisy, distorted data and to restore clean, undistorted images, respectively. Data aggregation as well as high-resolution image generation both usually come at the risk of involving aliases, i.e.~standard architectures put their ability to reconstruct the model input in jeopardy to reach high PSNR values on validation data. The price to be paid is low model robustness. In this work, we show that simply providing alias-free paths in state-of-the-art reconstruction transformers supports improved model robustness at low costs on the restoration performance. We do so by proposing BOA-Restormer, a transformer-based image restoration model that executes downsampling and upsampling operations partly in the frequency domain to ensure alias-free paths along the entire model while potentially preserving all relevant high-frequency information. | [
"['Shashank Agnihotri' 'Julia Grabinski' 'Janis Keuper' 'Margret Keuper']"
] |
null | null | 2406.07438 | null | null | http://arxiv.org/pdf/2406.07438v2 | 2024-06-18T17:42:52Z | 2024-06-11T16:45:48Z | DeformTime: Capturing Variable Dependencies with Deformable Attention
for Time Series Forecasting | In multivariate time series (MTS) forecasting, existing state-of-the-art deep learning approaches tend to focus on autoregressive formulations and overlook the information within exogenous indicators. To address this limitation, we present DeformTime, a neural network architecture that attempts to capture correlated temporal patterns from the input space, and hence, improve forecasting accuracy. It deploys two core operations performed by deformable attention blocks (DABs): learning dependencies across variables from different time steps (variable DAB), and preserving temporal dependencies in data from previous time steps (temporal DAB). Input data transformation is explicitly designed to enhance learning from the deformed series of information while passing through a DAB. We conduct extensive experiments on 6 MTS data sets, using previously established benchmarks as well as challenging infectious disease modelling tasks with more exogenous variables. The results demonstrate that DeformTime improves accuracy against previous competitive methods across the vast majority of MTS forecasting tasks, reducing the mean absolute error by 10% on average. Notably, performance gains remain consistent across longer forecasting horizons. | [
"['Yuxuan Shu' 'Vasileios Lampos']"
] |
null | null | 2406.07450 | null | null | http://arxiv.org/pdf/2406.07450v1 | 2024-06-11T16:55:38Z | 2024-06-11T16:55:38Z | Benchmarking Vision-Language Contrastive Methods for Medical
Representation Learning | We perform a comprehensive benchmarking of contrastive frameworks for learning multimodal representations in the medical domain. Through this study, we aim to answer the following research questions: (i) How transferable are general-domain representations to the medical domain? (ii) Is multimodal contrastive training sufficient, or does it benefit from unimodal training as well? (iii) What is the impact of feature granularity on the effectiveness of multimodal medical representation learning? To answer these questions, we investigate eight contrastive learning approaches under identical training setups, and train them on 2.8 million image-text pairs from four datasets, and evaluate them on 25 downstream tasks, including classification (zero-shot and linear probing), image-to-text and text-to-image retrieval, and visual question-answering. Our findings suggest a positive answer to the first question, a negative answer to the second question, and the benefit of learning fine-grained features. Finally, we make our code publicly available. | [
"['Shuvendu Roy' 'Yasaman Parhizkar' 'Franklin Ogidi' 'Vahid Reza Khazaie'\n 'Michael Colacci' 'Ali Etemad' 'Elham Dolatabadi' 'Arash Afkanpour']"
] |
null | null | 2406.07451 | null | null | http://arxiv.org/pdf/2406.07451v1 | 2024-06-11T16:57:48Z | 2024-06-11T16:57:48Z | An Optimism-based Approach to Online Evaluation of Generative Models | Existing frameworks for evaluating and comparing generative models typically target an offline setting, where the evaluator has access to full batches of data produced by the models. However, in many practical scenarios, the goal is to identify the best model using the fewest generated samples to minimize the costs of querying data from the models. Such an online comparison is challenging with current offline assessment methods. In this work, we propose an online evaluation framework to find the generative model that maximizes a standard assessment score among a group of available models. Our method uses an optimism-based multi-armed bandit framework to identify the model producing data with the highest evaluation score, quantifying the quality and diversity of generated data. Specifically, we study the online assessment of generative models based on the Fr'echet Inception Distance (FID) and Inception Score (IS) metrics and propose the FID-UCB and IS-UCB algorithms leveraging the upper confidence bound approach in online learning. We prove sub-linear regret bounds for these algorithms and present numerical results on standard image datasets, demonstrating their effectiveness in identifying the score-maximizing generative model. | [
"['Xiaoyan Hu' 'Ho-fung Leung' 'Farzan Farnia']"
] |
null | null | 2406.07455 | null | null | http://arxiv.org/pdf/2406.07455v1 | 2024-06-11T17:01:41Z | 2024-06-11T17:01:41Z | Reinforcement Learning from Human Feedback without Reward Inference:
Model-Free Algorithm and Instance-Dependent Analysis | In this paper, we study reinforcement learning from human feedback (RLHF) under an episodic Markov decision process with a general trajectory-wise reward model. We developed a model-free RLHF best policy identification algorithm, called $mathsf{BSAD}$, without explicit reward model inference, which is a critical intermediate step in the contemporary RLHF paradigms for training large language models (LLM). The algorithm identifies the optimal policy directly from human preference information in a backward manner, employing a dueling bandit sub-routine that constantly duels actions to identify the superior one. $mathsf{BSAD}$ adopts a reward-free exploration and best-arm-identification-like adaptive stopping criteria to equalize the visitation among all states in the same decision step while moving to the previous step as soon as the optimal action is identifiable, leading to a provable, instance-dependent sample complexity $tilde{mathcal{O}}(c_{mathcal{M}}SA^3H^3Mlogfrac{1}{delta})$ which resembles the result in classic RL, where $c_{mathcal{M}}$ is the instance-dependent constant and $M$ is the batch size. Moreover, $mathsf{BSAD}$ can be transformed into an explore-then-commit algorithm with logarithmic regret and generalized to discounted MDPs using a frame-based approach. Our results show: (i) sample-complexity-wise, RLHF is not significantly harder than classic RL and (ii) end-to-end RLHF may deliver improved performance by avoiding pitfalls in reward inferring such as overfit and distribution shift. | [
"['Qining Zhang' 'Honghao Wei' 'Lei Ying']"
] |
null | null | 2406.07456 | null | null | http://arxiv.org/pdf/2406.07456v1 | 2024-06-11T17:01:45Z | 2024-06-11T17:01:45Z | fKAN: Fractional Kolmogorov-Arnold Networks with trainable Jacobi basis
functions | Recent advancements in neural network design have given rise to the development of Kolmogorov-Arnold Networks (KANs), which enhance speed, interpretability, and precision. This paper presents the Fractional Kolmogorov-Arnold Network (fKAN), a novel neural network architecture that incorporates the distinctive attributes of KANs with a trainable adaptive fractional-orthogonal Jacobi function as its basis function. By leveraging the unique mathematical properties of fractional Jacobi functions, including simple derivative formulas, non-polynomial behavior, and activity for both positive and negative input values, this approach ensures efficient learning and enhanced accuracy. The proposed architecture is evaluated across a range of tasks in deep learning and physics-informed deep learning. Precision is tested on synthetic regression data, image classification, image denoising, and sentiment analysis. Additionally, the performance is measured on various differential equations, including ordinary, partial, and fractional delay differential equations. The results demonstrate that integrating fractional Jacobi functions into KANs significantly improves training speed and performance across diverse fields and applications. | [
"['Alireza Afzal Aghaei']"
] |
null | null | 2406.07457 | null | null | http://arxiv.org/pdf/2406.07457v1 | 2024-06-11T17:01:52Z | 2024-06-11T17:01:52Z | Estimating the Hallucination Rate of Generative AI | This work is about estimating the hallucination rate for in-context learning (ICL) with Generative AI. In ICL, a conditional generative model (CGM) is prompted with a dataset and asked to make a prediction based on that dataset. The Bayesian interpretation of ICL assumes that the CGM is calculating a posterior predictive distribution over an unknown Bayesian model of a latent parameter and data. With this perspective, we define a textit{hallucination} as a generated prediction that has low-probability under the true latent parameter. We develop a new method that takes an ICL problem -- that is, a CGM, a dataset, and a prediction question -- and estimates the probability that a CGM will generate a hallucination. Our method only requires generating queries and responses from the model and evaluating its response log probability. We empirically evaluate our method on synthetic regression and natural language ICL tasks using large language models. | [
"['Andrew Jesson' 'Nicolas Beltran-Velez' 'Quentin Chu' 'Sweta Karlekar'\n 'Jannik Kossen' 'Yarin Gal' 'John P. Cunningham' 'David Blei']"
] |
null | null | 2406.07466 | null | null | http://arxiv.org/pdf/2406.07466v1 | 2024-06-11T17:12:41Z | 2024-06-11T17:12:41Z | Multimodal Belief Prediction | Recognizing a speaker's level of commitment to a belief is a difficult task; humans do not only interpret the meaning of the words in context, but also understand cues from intonation and other aspects of the audio signal. Many papers and corpora in the NLP community have approached the belief prediction task using text-only approaches. We are the first to frame and present results on the multimodal belief prediction task. We use the CB-Prosody corpus (CBP), containing aligned text and audio with speaker belief annotations. We first report baselines and significant features using acoustic-prosodic features and traditional machine learning methods. We then present text and audio baselines for the CBP corpus fine-tuning on BERT and Whisper respectively. Finally, we present our multimodal architecture which fine-tunes on BERT and Whisper and uses multiple fusion methods, improving on both modalities alone. | [
"['John Murzaku' 'Adil Soubki' 'Owen Rambow']"
] |
null | null | 2406.07474 | null | null | http://arxiv.org/pdf/2406.07474v2 | 2024-06-17T17:19:01Z | 2024-06-11T17:20:28Z | Quantifying Local Model Validity using Active Learning | Real-world applications of machine learning models are often subject to legal or policy-based regulations. Some of these regulations require ensuring the validity of the model, i.e., the approximation error being smaller than a threshold. A global metric is generally too insensitive to determine the validity of a specific prediction, whereas evaluating local validity is costly since it requires gathering additional data.We propose learning the model error to acquire a local validity estimate while reducing the amount of required data through active learning. Using model validation benchmarks, we provide empirical evidence that the proposed method can lead to an error model with sufficient discriminative properties using a relatively small amount of data. Furthermore, an increased sensitivity to local changes of the validity bounds compared to alternative approaches is demonstrated. | [
"['Sven Lämmle' 'Can Bogoclu' 'Robert Voßhall' 'Anselm Haselhoff'\n 'Dirk Roos']"
] |
null | null | 2406.07475 | null | null | http://arxiv.org/pdf/2406.07475v1 | 2024-06-11T17:21:15Z | 2024-06-11T17:21:15Z | Partially Observed Trajectory Inference using Optimal Transport and a
Dynamics Prior | Trajectory inference seeks to recover the temporal dynamics of a population from snapshots of its (uncoupled) temporal marginals, i.e. where observed particles are not tracked over time. Lavenant et al. arXiv:2102.09204 addressed this challenging problem under a stochastic differential equation (SDE) model with a gradient-driven drift in the observed space, introducing a minimum entropy estimator relative to the Wiener measure. Chizat et al. arXiv:2205.07146 then provided a practical grid-free mean-field Langevin (MFL) algorithm using Schr"odinger bridges. Motivated by the overwhelming success of observable state space models in the traditional paired trajectory inference problem (e.g. target tracking), we extend the above framework to a class of latent SDEs in the form of observable state space models. In this setting, we use partial observations to infer trajectories in the latent space under a specified dynamics model (e.g. the constant velocity/acceleration models from target tracking). We introduce PO-MFL to solve this latent trajectory inference problem and provide theoretical guarantees by extending the results of arXiv:2102.09204 to the partially observed setting. We leverage the MFL framework of arXiv:2205.07146, yielding an algorithm based on entropic OT between dynamics-adjusted adjacent time marginals. Experiments validate the robustness of our method and the exponential convergence of the MFL dynamics, and demonstrate significant outperformance over the latent-free method of arXiv:2205.07146 in key scenarios. | [
"['Anming Gu' 'Edward Chien' 'Kristjan Greenewald']"
] |
null | null | 2406.07482 | null | null | http://arxiv.org/pdf/2406.07482v1 | 2024-06-11T17:25:46Z | 2024-06-11T17:25:46Z | Comparing Deep Learning Models for Rice Mapping in Bhutan Using High
Resolution Satellite Imagery | The Bhutanese government is increasing its utilization of technological approaches such as including Remote Sensing-based knowledge in their decision-making process. This study focuses on crop type and crop extent in Paro, one of the top rice-yielding districts in Bhutan, and employs publicly available NICFI high-resolution satellite imagery from Planet. Two Deep Learning (DL) approaches, point-based (DNN) and patch-based (U-Net), models were used in conjunction with cloud-computing platforms. Three different models per DL approaches (DNN and U-Net) were trained: 1) RGBN channels from Planet; 2) RGBN and elevation data (RGBNE); 3) RGBN and Sentinel-1 (S1) data (RGBNS), and RGBN with E and S1 data (RGBNES). From this comprehensive analysis, the U-Net displayed higher performance metrics across both model training and model validation efforts. Among the U-Net model sets, the RGBN, RGBNE, RGBNS, and RGBNES models had an F1-score of 0.8546, 0.8563, 0.8467, and 0.8500 respectively. An independent model evaluation was performed and found a high level of performance variation across all the metrics. For this independent model evaluation, the U-Net RGBN, RGBNE, RGBNES, and RGBN models displayed the F1-scores of 0.5935, 0.6154, 0.5882, and 0.6582, suggesting U-Net RGBNES as the best model. The study shows that the DL approaches can predict rice. Also, DL methods can be used with the survey-based approaches currently utilized by the Bhutan Department of Agriculture. Further, this study demonstrated the usage of regional land cover products such as SERVIR's RLCMS as a weak label approach to capture different strata addressing the class imbalance problem and improving the sampling design for DL application. Finally, through preliminary model testing and comparisons outlined it was shown that using additional features such as NDVI, EVI, and NDWI did not drastically improve model performance. | [
"['Biplov Bhandari' 'Timothy Mayer']"
] |
null | null | 2406.07484 | null | null | http://arxiv.org/pdf/2406.07484v1 | 2024-06-11T17:26:14Z | 2024-06-11T17:26:14Z | Towards Generalized Hydrological Forecasting using Transformer Models
for 120-Hour Streamflow Prediction | This study explores the efficacy of a Transformer model for 120-hour streamflow prediction across 125 diverse locations in Iowa, US. Utilizing data from the preceding 72 hours, including precipitation, evapotranspiration, and discharge values, we developed a generalized model to predict future streamflow. Our approach contrasts with traditional methods that typically rely on location-specific models. We benchmarked the Transformer model's performance against three deep learning models (LSTM, GRU, and Seq2Seq) and the Persistence approach, employing Nash-Sutcliffe Efficiency (NSE), Kling-Gupta Efficiency (KGE), Pearson's r, and Normalized Root Mean Square Error (NRMSE) as metrics. The study reveals the Transformer model's superior performance, maintaining higher median NSE and KGE scores and exhibiting the lowest NRMSE values. This indicates its capability to accurately simulate and predict streamflow, adapting effectively to varying hydrological conditions and geographical variances. Our findings underscore the Transformer model's potential as an advanced tool in hydrological modeling, offering significant improvements over traditional and contemporary approaches. | [
"['Bekir Z. Demiray' 'Ibrahim Demir']"
] |
null | null | 2406.07496 | null | null | http://arxiv.org/pdf/2406.07496v1 | 2024-06-11T17:32:21Z | 2024-06-11T17:32:21Z | TextGrad: Automatic "Differentiation" via Text | AI is undergoing a paradigm shift, with breakthroughs achieved by systems orchestrating multiple large language models (LLMs) and other complex components. As a result, developing principled and automated optimization methods for compound AI systems is one of the most important new challenges. Neural networks faced a similar challenge in its early days until backpropagation and automatic differentiation transformed the field by making optimization turn-key. Inspired by this, we introduce TextGrad, a powerful framework performing automatic ``differentiation'' via text. TextGrad backpropagates textual feedback provided by LLMs to improve individual components of a compound AI system. In our framework, LLMs provide rich, general, natural language suggestions to optimize variables in computation graphs, ranging from code snippets to molecular structures. TextGrad follows PyTorch's syntax and abstraction and is flexible and easy-to-use. It works out-of-the-box for a variety of tasks, where the users only provide the objective function without tuning components or prompts of the framework. We showcase TextGrad's effectiveness and generality across a diverse range of applications, from question answering and molecule optimization to radiotherapy treatment planning. Without modifying the framework, TextGrad improves the zero-shot accuracy of GPT-4o in Google-Proof Question Answering from $51%$ to $55%$, yields $20%$ relative performance gain in optimizing LeetCode-Hard coding problem solutions, improves prompts for reasoning, designs new druglike small molecules with desirable in silico binding, and designs radiation oncology treatment plans with high specificity. TextGrad lays a foundation to accelerate the development of the next-generation of AI systems. | [
"['Mert Yuksekgonul' 'Federico Bianchi' 'Joseph Boen' 'Sheng Liu'\n 'Zhi Huang' 'Carlos Guestrin' 'James Zou']"
] |
null | null | 2406.07506 | null | null | http://arxiv.org/pdf/2406.07506v1 | 2024-06-11T17:40:31Z | 2024-06-11T17:40:31Z | Understanding Visual Concepts Across Models | Large multimodal models such as Stable Diffusion can generate, detect, and classify new visual concepts after fine-tuning just a single word embedding. Do models learn similar words for the same concepts (i.e. <orange-cat> = orange + cat)? We conduct a large-scale analysis on three state-of-the-art models in text-to-image generation, open-set object detection, and zero-shot classification, and find that new word embeddings are model-specific and non-transferable. Across 4,800 new embeddings trained for 40 diverse visual concepts on four standard datasets, we find perturbations within an $epsilon$-ball to any prior embedding that generate, detect, and classify an arbitrary concept. When these new embeddings are spliced into new models, fine-tuning that targets the original model is lost. We show popular soft prompt-tuning approaches find these perturbative solutions when applied to visual concept learning tasks, and embeddings for visual concepts are not transferable. Code for reproducing our work is available at: https://visual-words.github.io. | [
"['Brandon Trabucco' 'Max Gurinas' 'Kyle Doherty' 'Ruslan Salakhutdinov']"
] |
null | null | 2406.07507 | null | null | http://arxiv.org/pdf/2406.07507v1 | 2024-06-11T17:41:26Z | 2024-06-11T17:41:26Z | Flow Map Matching | Generative models based on dynamical transport of measure, such as diffusion models, flow matching models, and stochastic interpolants, learn an ordinary or stochastic differential equation whose trajectories push initial conditions from a known base distribution onto the target. While training is cheap, samples are generated via simulation, which is more expensive than one-step models like GANs. To close this gap, we introduce flow map matching -- an algorithm that learns the two-time flow map of an underlying ordinary differential equation. The approach leads to an efficient few-step generative model whose step count can be chosen a-posteriori to smoothly trade off accuracy for computational expense. Leveraging the stochastic interpolant framework, we introduce losses for both direct training of flow maps and distillation from pre-trained (or otherwise known) velocity fields. Theoretically, we show that our approach unifies many existing few-step generative models, including consistency models, consistency trajectory models, progressive distillation, and neural operator approaches, which can be obtained as particular cases of our formalism. With experiments on CIFAR-10 and ImageNet 32x32, we show that flow map matching leads to high-quality samples with significantly reduced sampling cost compared to diffusion or stochastic interpolant methods. | [
"['Nicholas M. Boffi' 'Michael S. Albergo' 'Eric Vanden-Eijnden']"
] |
null | null | 2406.07515 | null | null | http://arxiv.org/pdf/2406.07515v1 | 2024-06-11T17:46:16Z | 2024-06-11T17:46:16Z | Beyond Model Collapse: Scaling Up with Synthesized Data Requires
Reinforcement | Synthesized data from generative models is increasingly considered as an alternative to human-annotated data for fine-tuning Large Language Models. This raises concerns about model collapse: a drop in performance of models fine-tuned on generated data. Considering that it is easier for both humans and machines to tell between good and bad examples than to generate high-quality samples, we investigate the use of feedback on synthesized data to prevent model collapse. We derive theoretical conditions under which a Gaussian mixture classification model can achieve asymptotically optimal performance when trained on feedback-augmented synthesized data, and provide supporting simulations for finite regimes. We illustrate our theoretical predictions on two practical problems: computing matrix eigenvalues with transformers and news summarization with large language models, which both undergo model collapse when trained on model-generated data. We show that training from feedback-augmented synthesized data, either by pruning incorrect predictions or by selecting the best of several guesses, can prevent model collapse, validating popular approaches like RLHF. | [
"['Yunzhen Feng' 'Elvis Dohmatob' 'Pu Yang' 'Francois Charton'\n 'Julia Kempe']"
] |
null | null | 2406.07519 | null | null | http://arxiv.org/pdf/2406.07519v1 | 2024-06-11T17:50:04Z | 2024-06-11T17:50:04Z | Physics-guided weak-form discovery of reduced-order models for trapped
ultracold hydrodynamics | We study the relaxation of a highly collisional, ultracold but nondegenerate gas of polar molecules. Confined within a harmonic trap, the gas is subject to fluid-gaseous coupled dynamics that lead to a breakdown of first-order hydrodynamics. An attempt to treat these higher-order hydrodynamic effects was previously made with a Gaussian ansatz and coarse-graining model parameter [R. R. W. Wang & J. L. Bohn, Phys. Rev. A 108, 013322 (2023)], leading to an approximate set of equations for a few collective observables accessible to experiments. Here we present substantially improved reduced-order models for these same observables, admissible beyond previous parameter regimes, discovered directly from particle simulations using the WSINDy algorithm (Weak-form Sparse Identification of Nonlinear Dynamics). The interpretable nature of the learning algorithm enables estimation of previously unknown physical quantities and discovery of model terms with candidate physical mechanisms, revealing new physics in mixed collisional regimes. Our approach constitutes a general framework for data-driven model identification leveraging known physics. | [
"['Reuben R. W. Wang' 'Daniel Messenger']"
] |
null | null | 2406.07521 | null | null | http://arxiv.org/pdf/2406.07521v1 | 2024-06-11T17:50:20Z | 2024-06-11T17:50:20Z | Faster Spectral Density Estimation and Sparsification in the Nuclear
Norm | We consider the problem of estimating the spectral density of the normalized adjacency matrix of an $n$-node undirected graph. We provide a randomized algorithm that, with $O(nepsilon^{-2})$ queries to a degree and neighbor oracle and in $O(nepsilon^{-3})$ time, estimates the spectrum up to $epsilon$ accuracy in the Wasserstein-1 metric. This improves on previous state-of-the-art methods, including an $O(nepsilon^{-7})$ time algorithm from [Braverman et al., STOC 2022] and, for sufficiently small $epsilon$, a $2^{O(epsilon^{-1})}$ time method from [Cohen-Steiner et al., KDD 2018]. To achieve this result, we introduce a new notion of graph sparsification, which we call nuclear sparsification. We provide an $O(nepsilon^{-2})$-query and $O(nepsilon^{-2})$-time algorithm for computing $O(nepsilon^{-2})$-sparse nuclear sparsifiers. We show that this bound is optimal in both its sparsity and query complexity, and we separate our results from the related notion of additive spectral sparsification. Of independent interest, we show that our sparsification method also yields the first deterministic algorithm for spectral density estimation that scales linearly with $n$ (sublinear in the representation size of the graph). | [
"['Yujia Jin' 'Ishani Karmarkar' 'Christopher Musco' 'Aaron Sidford'\n 'Apoorv Vikram Singh']"
] |
null | null | 2406.07522 | null | null | http://arxiv.org/pdf/2406.07522v1 | 2024-06-11T17:50:51Z | 2024-06-11T17:50:51Z | Samba: Simple Hybrid State Space Models for Efficient Unlimited Context
Language Modeling | Efficiently modeling sequences with infinite context length has been a long-standing problem. Past works suffer from either the quadratic computation complexity or the limited extrapolation ability on length generalization. In this work, we present Samba, a simple hybrid architecture that layer-wise combines Mamba, a selective State Space Model (SSM), with Sliding Window Attention (SWA). Samba selectively compresses a given sequence into recurrent hidden states while still maintaining the ability to precisely recall memories with the attention mechanism. We scale Samba up to 3.8B parameters with 3.2T training tokens and show that Samba substantially outperforms the state-of-the-art models based on pure attention or SSMs on a wide range of benchmarks. When trained on 4K length sequences, Samba can be efficiently extrapolated to 256K context length with perfect memory recall and show improved token predictions up to 1M context length. As a linear-time sequence model, Samba enjoys a 3.73x higher throughput compared to Transformers with grouped-query attention when processing user prompts of 128K length, and 3.64x speedup when generating 64K tokens with unlimited streaming. A sample implementation of Samba is publicly available in https://github.com/microsoft/Samba. | [
"['Liliang Ren' 'Yang Liu' 'Yadong Lu' 'Yelong Shen' 'Chen Liang'\n 'Weizhu Chen']"
] |
null | null | 2406.07524 | null | null | http://arxiv.org/pdf/2406.07524v1 | 2024-06-11T17:51:40Z | 2024-06-11T17:51:40Z | Simple and Effective Masked Diffusion Language Models | While diffusion models excel at generating high-quality images, prior work reports a significant performance gap between diffusion and autoregressive (AR) methods in language modeling. In this work, we show that simple masked discrete diffusion is more performant than previously thought. We apply an effective training recipe that improves the performance of masked diffusion models and derive a simplified, Rao-Blackwellized objective that results in additional improvements. Our objective has a simple form -- it is a mixture of classical masked language modeling losses -- and can be used to train encoder-only language models that admit efficient samplers, including ones that can generate arbitrary lengths of text semi-autoregressively like a traditional language model. On language modeling benchmarks, a range of masked diffusion models trained with modern engineering practices achieves a new state-of-the-art among diffusion models, and approaches AR perplexity. We release our code at: https://github.com/kuleshov-group/mdlm | [
"['Subham Sekhar Sahoo' 'Marianne Arriola' 'Yair Schiff' 'Aaron Gokaslan'\n 'Edgar Marroquin' 'Justin T Chiu' 'Alexander Rush' 'Volodymyr Kuleshov']"
] |
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