categories
string | doi
string | id
string | year
float64 | venue
string | link
string | updated
string | published
string | title
string | abstract
string | authors
sequence |
|---|---|---|---|---|---|---|---|---|---|---|
null | null | 2406.08673 | null | null | http://arxiv.org/pdf/2406.08673v1 | 2024-06-12T22:28:08Z | 2024-06-12T22:28:08Z | HelpSteer2: Open-source dataset for training top-performing reward
models | High-quality preference datasets are essential for training reward models that can effectively guide large language models (LLMs) in generating high-quality responses aligned with human preferences. As LLMs become stronger and better aligned, permissively licensed preference datasets, such as Open Assistant, HH-RLHF, and HelpSteer need to be updated to remain effective for reward modeling. Methods that distil preference data from proprietary LLMs such as GPT-4 have restrictions on commercial usage imposed by model providers. To improve upon both generated responses and attribute labeling quality, we release HelpSteer2, a permissively licensed preference dataset (CC-BY-4.0). Using a powerful internal base model trained on HelpSteer2, we are able to achieve the SOTA score (92.0%) on Reward-Bench's primary dataset, outperforming currently listed open and proprietary models, as of June 12th, 2024. Notably, HelpSteer2 consists of only ten thousand response pairs, an order of magnitude fewer than existing preference datasets (e.g., HH-RLHF), which makes it highly efficient for training reward models. Our extensive experiments demonstrate that reward models trained with HelpSteer2 are effective in aligning LLMs. In particular, we propose SteerLM 2.0, a model alignment approach that can effectively make use of the rich multi-attribute score predicted by our reward models. HelpSteer2 is available at https://huggingface.co/datasets/nvidia/HelpSteer2 and code is available at https://github.com/NVIDIA/NeMo-Aligner | [
"['Zhilin Wang' 'Yi Dong' 'Olivier Delalleau' 'Jiaqi Zeng' 'Gerald Shen'\n 'Daniel Egert' 'Jimmy J. Zhang' 'Makesh Narsimhan Sreedhar'\n 'Oleksii Kuchaiev']"
] |
null | null | 2406.08686 | null | null | http://arxiv.org/pdf/2406.08686v1 | 2024-06-12T22:58:45Z | 2024-06-12T22:58:45Z | Opportunities in deep learning methods development for computational
biology | Advances in molecular technologies underlie an enormous growth in the size of data sets pertaining to biology and biomedicine. These advances parallel those in the deep learning subfield of machine learning. Components in the differentiable programming toolbox that makes deep learning possible are allowing computer scientists to address an increasingly large array of problems with flexible and effective tools. However many of these tools have not fully proliferated into the computational biology and bioinformatics fields. In this perspective we survey some of these advances and highlight exemplary examples of their utilization in the biosciences, with the goal of increasing awareness among practitioners of emerging opportunities to blend expert knowledge with newly emerging deep learning architectural tools. | [
"['Alex Jihun Lee' 'Reza Abbasi-Asl']"
] |
null | null | 2406.08691 | null | null | http://arxiv.org/pdf/2406.08691v1 | 2024-06-12T23:22:23Z | 2024-06-12T23:22:23Z | UnO: Unsupervised Occupancy Fields for Perception and Forecasting | Perceiving the world and forecasting its future state is a critical task for self-driving. Supervised approaches leverage annotated object labels to learn a model of the world -- traditionally with object detections and trajectory predictions, or temporal bird's-eye-view (BEV) occupancy fields. However, these annotations are expensive and typically limited to a set of predefined categories that do not cover everything we might encounter on the road. Instead, we learn to perceive and forecast a continuous 4D (spatio-temporal) occupancy field with self-supervision from LiDAR data. This unsupervised world model can be easily and effectively transferred to downstream tasks. We tackle point cloud forecasting by adding a lightweight learned renderer and achieve state-of-the-art performance in Argoverse 2, nuScenes, and KITTI. To further showcase its transferability, we fine-tune our model for BEV semantic occupancy forecasting and show that it outperforms the fully supervised state-of-the-art, especially when labeled data is scarce. Finally, when compared to prior state-of-the-art on spatio-temporal geometric occupancy prediction, our 4D world model achieves a much higher recall of objects from classes relevant to self-driving. | [
"['Ben Agro' 'Quinlan Sykora' 'Sergio Casas' 'Thomas Gilles'\n 'Raquel Urtasun']"
] |
null | null | 2406.08695 | null | null | http://arxiv.org/pdf/2406.08695v1 | 2024-06-12T23:36:16Z | 2024-06-12T23:36:16Z | Global AI Governance in Healthcare: A Cross-Jurisdictional Regulatory
Analysis | Artificial Intelligence (AI) is being adopted across the world and promises a new revolution in healthcare. While AI-enabled medical devices in North America dominate 42.3% of the global market, the use of AI-enabled medical devices in other countries is still a story waiting to be unfolded. We aim to delve deeper into global regulatory approaches towards AI use in healthcare, with a focus on how common themes are emerging globally. We compare these themes to the World Health Organization's (WHO) regulatory considerations and principles on ethical use of AI for healthcare applications. Our work seeks to take a global perspective on AI policy by analyzing 14 legal jurisdictions including countries representative of various regions in the world (North America, South America, South East Asia, Middle East, Africa, Australia, and the Asia-Pacific). Our eventual goal is to foster a global conversation on the ethical use of AI in healthcare and the regulations that will guide it. We propose solutions to promote international harmonization of AI regulations and examine the requirements for regulating generative AI, using China and Singapore as examples of countries with well-developed policies in this area. | [
"['Attrayee Chakraborty' 'Mandar Karhade']"
] |
null | null | 2406.08697 | null | null | http://arxiv.org/pdf/2406.08697v1 | 2024-06-12T23:41:43Z | 2024-06-12T23:41:43Z | Orthogonalized Estimation of Difference of $Q$-functions | Offline reinforcement learning is important in many settings with available observational data but the inability to deploy new policies online due to safety, cost, and other concerns. Many recent advances in causal inference and machine learning target estimation of causal contrast functions such as CATE, which is sufficient for optimizing decisions and can adapt to potentially smoother structure. We develop a dynamic generalization of the R-learner (Nie and Wager 2021, Lewis and Syrgkanis 2021) for estimating and optimizing the difference of $Q^pi$-functions, $Q^pi(s,1)-Q^pi(s,0)$ (which can be used to optimize multiple-valued actions). We leverage orthogonal estimation to improve convergence rates in the presence of slower nuisance estimation rates and prove consistency of policy optimization under a margin condition. The method can leverage black-box nuisance estimators of the $Q$-function and behavior policy to target estimation of a more structured $Q$-function contrast. | [
"['Angela Zhou']"
] |
null | null | 2406.08709 | null | null | http://arxiv.org/pdf/2406.08709v1 | 2024-06-13T00:18:20Z | 2024-06-13T00:18:20Z | Introducing Diminutive Causal Structure into Graph Representation
Learning | When engaging in end-to-end graph representation learning with Graph Neural Networks (GNNs), the intricate causal relationships and rules inherent in graph data pose a formidable challenge for the model in accurately capturing authentic data relationships. A proposed mitigating strategy involves the direct integration of rules or relationships corresponding to the graph data into the model. However, within the domain of graph representation learning, the inherent complexity of graph data obstructs the derivation of a comprehensive causal structure that encapsulates universal rules or relationships governing the entire dataset. Instead, only specialized diminutive causal structures, delineating specific causal relationships within constrained subsets of graph data, emerge as discernible. Motivated by empirical insights, it is observed that GNN models exhibit a tendency to converge towards such specialized causal structures during the training process. Consequently, we posit that the introduction of these specific causal structures is advantageous for the training of GNN models. Building upon this proposition, we introduce a novel method that enables GNN models to glean insights from these specialized diminutive causal structures, thereby enhancing overall performance. Our method specifically extracts causal knowledge from the model representation of these diminutive causal structures and incorporates interchange intervention to optimize the learning process. Theoretical analysis serves to corroborate the efficacy of our proposed method. Furthermore, empirical experiments consistently demonstrate significant performance improvements across diverse datasets. | [
"['Hang Gao' 'Peng Qiao' 'Yifan Jin' 'Fengge Wu' 'Jiangmeng Li'\n 'Changwen Zheng']"
] |
null | null | 2406.08739 | null | null | http://arxiv.org/pdf/2406.08739v1 | 2024-04-30T23:26:24Z | 2024-04-30T23:26:24Z | At the edge of a generative cultural precipice | Since NFTs and large generative models (such as DALLE2 and Stable Diffusion) have been publicly available, artists have seen their jobs threatened and stolen. While artists depend on sharing their art on online platforms such as Deviantart, Pixiv, and Artstation, many slowed down sharing their work or downright removed their past work therein, especially if these platforms fail to provide certain guarantees regarding the copyright of their uploaded work. Text-to-image (T2I) generative models are trained using human-produced content to better guide the style and themes they can produce. Still, if the trend continues where data found online is generated by a machine instead of a human, this will have vast repercussions in culture. Inspired by recent work in generative models, we wish to tell a cautionary tale and ask what will happen to the visual arts if generative models continue on the path to be (eventually) trained solely on generated content. | [
"['Diego Porres' 'Alex Gomez-Villa']"
] |
null | null | 2406.08740 | null | null | http://arxiv.org/abs/2406.08740v2 | 2024-07-03T16:54:44Z | 2024-06-13T02:00:13Z | An AI Architecture with the Capability to Explain Recognition Results | Explainability is needed to establish confidence in machine learning results. Some explainable methods take a post hoc approach to explain the weights of machine learning models, others highlight areas of the input contributing to decisions. These methods do not adequately explain decisions, in plain terms. Explainable property-based systems have been shown to provide explanations in plain terms, however, they have not performed as well as leading unexplainable machine learning methods. This research focuses on the importance of metrics to explainability and contributes two methods yielding performance gains. The first method introduces a combination of explainable and unexplainable flows, proposing a metric to characterize explainability of a decision. The second method compares classic metrics for estimating the effectiveness of neural networks in the system, posing a new metric as the leading performer. Results from the new methods and examples from handwritten datasets are presented. | [
"['Paul Whitten' 'Francis Wolff' 'Chris Papachristou']"
] |
null | null | 2406.08743 | null | null | http://arxiv.org/pdf/2406.08743v1 | 2024-06-13T02:03:22Z | 2024-06-13T02:03:22Z | Generalizable Implicit Neural Representation As a Universal
Spatiotemporal Traffic Data Learner | $textbf{This is the conference version of our paper: Spatiotemporal Implicit Neural Representation as a Generalized Traffic Data Learner}$. Spatiotemporal Traffic Data (STTD) measures the complex dynamical behaviors of the multiscale transportation system. Existing methods aim to reconstruct STTD using low-dimensional models. However, they are limited to data-specific dimensions or source-dependent patterns, restricting them from unifying representations. Here, we present a novel paradigm to address the STTD learning problem by parameterizing STTD as an implicit neural representation. To discern the underlying dynamics in low-dimensional regimes, coordinate-based neural networks that can encode high-frequency structures are employed to directly map coordinates to traffic variables. To unravel the entangled spatial-temporal interactions, the variability is decomposed into separate processes. We further enable modeling in irregular spaces such as sensor graphs using spectral embedding. Through continuous representations, our approach enables the modeling of a variety of STTD with a unified input, thereby serving as a generalized learner of the underlying traffic dynamics. It is also shown that it can learn implicit low-rank priors and smoothness regularization from the data, making it versatile for learning different dominating data patterns. We validate its effectiveness through extensive experiments in real-world scenarios, showcasing applications from corridor to network scales. Empirical results not only indicate that our model has significant superiority over conventional low-rank models, but also highlight that the versatility of the approach. We anticipate that this pioneering modeling perspective could lay the foundation for universal representation of STTD in various real-world tasks. $textbf{The full version can be found at:}$ https://doi.org/10.48550/arXiv.2405.03185. | [
"['Tong Nie' 'Guoyang Qin' 'Wei Ma' 'Jian Sun']"
] |
null | null | 2406.08748 | null | null | http://arxiv.org/pdf/2406.08748v1 | 2024-06-13T02:12:18Z | 2024-06-13T02:12:18Z | Learning in Feature Spaces via Coupled Covariances: Asymmetric Kernel
SVD and Nyström method | In contrast with Mercer kernel-based approaches as used e.g., in Kernel Principal Component Analysis (KPCA), it was previously shown that Singular Value Decomposition (SVD) inherently relates to asymmetric kernels and Asymmetric Kernel Singular Value Decomposition (KSVD) has been proposed. However, the existing formulation to KSVD cannot work with infinite-dimensional feature mappings, the variational objective can be unbounded, and needs further numerical evaluation and exploration towards machine learning. In this work, i) we introduce a new asymmetric learning paradigm based on coupled covariance eigenproblem (CCE) through covariance operators, allowing infinite-dimensional feature maps. The solution to CCE is ultimately obtained from the SVD of the induced asymmetric kernel matrix, providing links to KSVD. ii) Starting from the integral equations corresponding to a pair of coupled adjoint eigenfunctions, we formalize the asymmetric Nystr"om method through a finite sample approximation to speed up training. iii) We provide the first empirical evaluations verifying the practical utility and benefits of KSVD and compare with methods resorting to symmetrization or linear SVD across multiple tasks. | [
"['Qinghua Tao' 'Francesco Tonin' 'Alex Lambert' 'Yingyi Chen'\n 'Panagiotis Patrinos' 'Johan A. K. Suykens']"
] |
null | null | 2406.08749 | null | null | http://arxiv.org/pdf/2406.08749v1 | 2024-06-13T02:17:19Z | 2024-06-13T02:17:19Z | Mathematical models for off-ball scoring prediction in basketball | In professional basketball, the accurate prediction of scoring opportunities based on strategic decision-making is crucial for space and player evaluations. However, traditional models often face challenges in accounting for the complexities of off-ball movements, which are essential for accurate predictive performance. In this study, we propose two mathematical models to predict off-ball scoring opportunities in basketball, considering both pass-to-score and dribble-to-score movements: the Ball Movement for Off-ball Scoring (BMOS) and the Ball Intercept and Movement for Off-ball Scoring (BIMOS) models. The BMOS adapts principles from the Off-Ball Scoring Opportunities (OBSO) model, originally designed for soccer, to basketball, whereas the BIMOS also incorporates the likelihood of interception during ball movements. We evaluated these models using player tracking data from 630 NBA games in the 2015-2016 regular season, demonstrating that the BIMOS outperforms the BMOS in terms of scoring prediction accuracy. Thus, our models provide valuable insights for tactical analysis and player evaluation in basketball. | [
"['Rikako Kono' 'Keisuke Fujii']"
] |
null | null | 2406.08756 | null | null | http://arxiv.org/pdf/2406.08756v2 | 2024-06-27T12:45:38Z | 2024-06-13T02:31:36Z | Optimizing Large Model Training through Overlapped Activation
Recomputation | Large model training has been using recomputation to alleviate the memory pressure and pipelining to exploit the parallelism of data, tensor, and devices. The existing recomputation approaches may incur up to 40% overhead when training real-world models, e.g., the GPT model with 22B parameters. This is because they are executed on demand in the critical training path. In this paper, we design a new recomputation framework, Lynx, to reduce the overhead by overlapping the recomputation with communication occurring in training pipelines. It consists of an optimal scheduling algorithm (OPT) and a heuristic-based scheduling algorithm (HEU). OPT achieves a global optimum but suffers from a long search time. HEU was designed based on our observation that there are identical structures in large DNN models so that we can apply the same scheduling policy to all identical structures. HEU achieves a local optimum but reduces the search time by 99% compared to OPT. Our comprehensive evaluation using GPT models with 1.3B-20B parameters shows that both OPT and HEU outperform the state-of-the-art recomputation approaches (e.g., Megatron-LM and Checkmake) by 1.02-1.53x. HEU achieves a similar performance as OPT with a search time of 0.16s on average. | [
"['Ping Chen' 'Wenjie Zhang' 'Shuibing He' 'Yingjie Gu' 'Zhuwei Peng'\n 'Kexin Huang' 'Xuan Zhan' 'Weijian Chen' 'Yi Zheng' 'Zhefeng Wang'\n 'Yanlong Yin' 'Gang Chen']"
] |
null | null | 2406.08765 | null | null | http://arxiv.org/pdf/2406.08765v1 | 2024-06-13T02:51:18Z | 2024-06-13T02:51:18Z | LLM-based Knowledge Pruning for Time Series Data Analytics on
Edge-computing Devices | Limited by the scale and diversity of time series data, the neural networks trained on time series data often overfit and show unsatisfacotry performances. In comparison, large language models (LLMs) recently exhibit impressive generalization in diverse fields. Although massive LLM based approaches are proposed for time series tasks, these methods require to load the whole LLM in both training and reference. This high computational demands limit practical applications in resource-constrained settings, like edge-computing and IoT devices. To address this issue, we propose Knowledge Pruning (KP), a novel paradigm for time series learning in this paper. For a specific downstream task, we argue that the world knowledge learned by LLMs is much redundant and only the related knowledge termed as "pertinent knowledge" is useful. Unlike other methods, our KP targets to prune the redundant knowledge and only distill the pertinent knowledge into the target model. This reduces model size and computational costs significantly. Additionally, different from existing LLM based approaches, our KP does not require to load the LLM in the process of training and testing, further easing computational burdens. With our proposed KP, a lightweight network can effectively learn the pertinent knowledge, achieving satisfactory performances with a low computation cost. To verify the effectiveness of our KP, two fundamental tasks on edge-computing devices are investigated in our experiments, where eight diverse environments or benchmarks with different networks are used to verify the generalization of our KP. Through experiments, our KP demonstrates effective learning of pertinent knowledge, achieving notable performance improvements in regression (19.7% on average) and classification (up to 13.7%) tasks, showcasing state-of-the-art results. | [
"['Ruibing Jin' 'Qing Xu' 'Min Wu' 'Yuecong Xu' 'Dan Li' 'Xiaoli Li'\n 'Zhenghua Chen']"
] |
null | null | 2406.08788 | null | null | http://arxiv.org/pdf/2406.08788v1 | 2024-06-13T03:47:12Z | 2024-06-13T03:47:12Z | Understanding the Generalizability of Link Predictors Under Distribution
Shifts on Graphs | Recently, multiple models proposed for link prediction (LP) demonstrate impressive results on benchmark datasets. However, many popular benchmark datasets often assume that dataset samples are drawn from the same distribution (i.e., IID samples). In real-world situations, this assumption is often incorrect; since uncontrolled factors may lead train and test samples to come from separate distributions. To tackle the distribution shift problem, recent work focuses on creating datasets that feature distribution shifts and designing generalization methods that perform well on the new data. However, those studies only consider distribution shifts that affect {it node-} and {it graph-level} tasks, thus ignoring link-level tasks. Furthermore, relatively few LP generalization methods exist. To bridge this gap, we introduce a set of LP-specific data splits which utilizes structural properties to induce a controlled distribution shift. We verify the shift's effect empirically through evaluation of different SOTA LP methods and subsequently couple these methods with generalization techniques. Interestingly, LP-specific methods frequently generalize poorly relative to heuristics or basic GNN methods. Finally, this work provides analysis to uncover insights for enhancing LP generalization. Our code is available at: href{https://github.com/revolins/LPStructGen}{https://github.com/revolins/LPStructGen} | [
"['Jay Revolinsky' 'Harry Shomer' 'Jiliang Tang']"
] |
null | null | 2406.08799 | null | null | http://arxiv.org/abs/2406.08799v1 | 2024-06-13T04:28:00Z | 2024-06-13T04:28:00Z | Pareto Front-Diverse Batch Multi-Objective Bayesian Optimization | We consider the problem of multi-objective optimization (MOO) of expensive black-box functions with the goal of discovering high-quality and diverse Pareto fronts where we are allowed to evaluate a batch of inputs. This problem arises in many real-world applications including penicillin production where diversity of solutions is critical. We solve this problem in the framework of Bayesian optimization (BO) and propose a novel approach referred to as Pareto front-Diverse Batch Multi-Objective BO (PDBO). PDBO tackles two important challenges: 1) How to automatically select the best acquisition function in each BO iteration, and 2) How to select a diverse batch of inputs by considering multiple objectives. We propose principled solutions to address these two challenges. First, PDBO employs a multi-armed bandit approach to select one acquisition function from a given library. We solve a cheap MOO problem by assigning the selected acquisition function for each expensive objective function to obtain a candidate set of inputs for evaluation. Second, it utilizes Determinantal Point Processes (DPPs) to choose a Pareto-front-diverse batch of inputs for evaluation from the candidate set obtained from the first step. The key parameters for the methods behind these two steps are updated after each round of function evaluations. Experiments on multiple MOO benchmarks demonstrate that PDBO outperforms prior methods in terms of both the quality and diversity of Pareto solutions. | [
"['Alaleh Ahmadianshalchi' 'Syrine Belakaria' 'Janardhan Rao Doppa']"
] |
null | null | 2406.08800 | null | null | http://arxiv.org/pdf/2406.08800v1 | 2024-06-13T04:33:05Z | 2024-06-13T04:33:05Z | Can Synthetic Audio From Generative Foundation Models Assist Audio
Recognition and Speech Modeling? | Recent advances in foundation models have enabled audio-generative models that produce high-fidelity sounds associated with music, events, and human actions. Despite the success achieved in modern audio-generative models, the conventional approach to assessing the quality of the audio generation relies heavily on distance metrics like Frechet Audio Distance. In contrast, we aim to evaluate the quality of audio generation by examining the effectiveness of using them as training data. Specifically, we conduct studies to explore the use of synthetic audio for audio recognition. Moreover, we investigate whether synthetic audio can serve as a resource for data augmentation in speech-related modeling. Our comprehensive experiments demonstrate the potential of using synthetic audio for audio recognition and speech-related modeling. Our code is available at https://github.com/usc-sail/SynthAudio. | [
"['Tiantian Feng' 'Dimitrios Dimitriadis' 'Shrikanth Narayanan']"
] |
null | null | 2406.08805 | null | null | http://arxiv.org/pdf/2406.08805v1 | 2024-06-13T04:39:42Z | 2024-06-13T04:39:42Z | A Dual Approach to Imitation Learning from Observations with Offline
Datasets | Demonstrations are an effective alternative to task specification for learning agents in settings where designing a reward function is difficult. However, demonstrating expert behavior in the action space of the agent becomes unwieldy when robots have complex, unintuitive morphologies. We consider the practical setting where an agent has a dataset of prior interactions with the environment and is provided with observation-only expert demonstrations. Typical learning from observations approaches have required either learning an inverse dynamics model or a discriminator as intermediate steps of training. Errors in these intermediate one-step models compound during downstream policy learning or deployment. We overcome these limitations by directly learning a multi-step utility function that quantifies how each action impacts the agent's divergence from the expert's visitation distribution. Using the principle of duality, we derive DILO(Dual Imitation Learning from Observations), an algorithm that can leverage arbitrary suboptimal data to learn imitating policies without requiring expert actions. DILO reduces the learning from observations problem to that of simply learning an actor and a critic, bearing similar complexity to vanilla offline RL. This allows DILO to gracefully scale to high dimensional observations, and demonstrate improved performance across the board. Project page (code and videos): $href{https://hari-sikchi.github.io/dilo/}{text{hari-sikchi.github.io/dilo/}}$ | [
"['Harshit Sikchi' 'Caleb Chuck' 'Amy Zhang' 'Scott Niekum']"
] |
null | null | 2406.08819 | null | null | http://arxiv.org/abs/2406.08819v2 | 2024-06-18T07:02:45Z | 2024-06-13T05:21:10Z | AIM: Attributing, Interpreting, Mitigating Data Unfairness | Data collected in the real world often encapsulates historical discrimination against disadvantaged groups and individuals. Existing fair machine learning (FairML) research has predominantly focused on mitigating discriminative bias in the model prediction, with far less effort dedicated towards exploring how to trace biases present in the data, despite its importance for the transparency and interpretability of FairML. To fill this gap, we investigate a novel research problem: discovering samples that reflect biases/prejudices from the training data. Grounding on the existing fairness notions, we lay out a sample bias criterion and propose practical algorithms for measuring and countering sample bias. The derived bias score provides intuitive sample-level attribution and explanation of historical bias in data. On this basis, we further design two FairML strategies via sample-bias-informed minimal data editing. They can mitigate both group and individual unfairness at the cost of minimal or zero predictive utility loss. Extensive experiments and analyses on multiple real-world datasets demonstrate the effectiveness of our methods in explaining and mitigating unfairness. Code is available at https://github.com/ZhiningLiu1998/AIM. | [
"['Zhining Liu' 'Ruizhong Qiu' 'Zhichen Zeng' 'Yada Zhu' 'Hendrik Hamann'\n 'Hanghang Tong']"
] |
null | null | 2406.08830 | null | null | http://arxiv.org/pdf/2406.08830v1 | 2024-06-13T05:49:29Z | 2024-06-13T05:49:29Z | Center-Sensitive Kernel Optimization for Efficient On-Device Incremental
Learning | To facilitate the evolution of edge intelligence in ever-changing environments, we study on-device incremental learning constrained in limited computation resource in this paper. Current on-device training methods just focus on efficient training without considering the catastrophic forgetting, preventing the model getting stronger when continually exploring the world. To solve this problem, a direct solution is to involve the existing incremental learning mechanisms into the on-device training framework. Unfortunately, such a manner cannot work well as those mechanisms usually introduce large additional computational cost to the network optimization process, which would inevitably exceed the memory capacity of the edge devices. To address this issue, this paper makes an early effort to propose a simple but effective edge-friendly incremental learning framework. Based on an empirical study on the knowledge intensity of the kernel elements of the neural network, we find that the center kernel is the key for maximizing the knowledge intensity for learning new data, while freezing the other kernel elements would get a good balance on the model's capacity for overcoming catastrophic forgetting. Upon this finding, we further design a center-sensitive kernel optimization framework to largely alleviate the cost of the gradient computation and back-propagation. Besides, a dynamic channel element selection strategy is also proposed to facilitate a sparse orthogonal gradient projection for further reducing the optimization complexity, upon the knowledge explored from the new task data. Extensive experiments validate our method is efficient and effective, e.g., our method achieves average accuracy boost of 38.08% with even less memory and approximate computation compared to existing on-device training methods, indicating its significant potential for on-device incremental learning. | [
"['Dingwen Zhang' 'Yan Li' 'De Cheng' 'Nannan Wang' 'Junwei Han']"
] |
null | null | 2406.08837 | null | null | http://arxiv.org/pdf/2406.08837v1 | 2024-06-13T06:00:28Z | 2024-06-13T06:00:28Z | Research on Deep Learning Model of Feature Extraction Based on
Convolutional Neural Network | Neural networks with relatively shallow layers and simple structures may have limited ability in accurately identifying pneumonia. In addition, deep neural networks also have a large demand for computing resources, which may cause convolutional neural networks to be unable to be implemented on terminals. Therefore, this paper will carry out the optimal classification of convolutional neural networks. Firstly, according to the characteristics of pneumonia images, AlexNet and InceptionV3 were selected to obtain better image recognition results. Combining the features of medical images, the forward neural network with deeper and more complex structure is learned. Finally, knowledge extraction technology is used to extract the obtained data into the AlexNet model to achieve the purpose of improving computing efficiency and reducing computing costs. The results showed that the prediction accuracy, specificity, and sensitivity of the trained AlexNet model increased by 4.25 percentage points, 7.85 percentage points, and 2.32 percentage points, respectively. The graphics processing usage has decreased by 51% compared to the InceptionV3 mode. | [
"['Houze Liu' 'Iris Li' 'Yaxin Liang' 'Dan Sun' 'Yining Yang' 'Haowei Yang']"
] |
null | null | 2406.08838 | null | null | http://arxiv.org/pdf/2406.08838v1 | 2024-06-13T06:03:59Z | 2024-06-13T06:03:59Z | Research on Optimization of Natural Language Processing Model Based on
Multimodal Deep Learning | This project intends to study the image representation based on attention mechanism and multimodal data. By adding multiple pattern layers to the attribute model, the semantic and hidden layers of image content are integrated. The word vector is quantified by the Word2Vec method and then evaluated by a word embedding convolutional neural network. The published experimental results of the two groups were tested. The experimental results show that this method can convert discrete features into continuous characters, thus reducing the complexity of feature preprocessing. Word2Vec and natural language processing technology are integrated to achieve the goal of direct evaluation of missing image features. The robustness of the image feature evaluation model is improved by using the excellent feature analysis characteristics of a convolutional neural network. This project intends to improve the existing image feature identification methods and eliminate the subjective influence in the evaluation process. The findings from the simulation indicate that the novel approach has developed is viable, effectively augmenting the features within the produced representations. | [
"['Dan Sun' 'Yaxin Liang' 'Yining Yang' 'Yuhan Ma' 'Qishi Zhan' 'Erdi Gao']"
] |
null | null | 2406.08840 | null | null | http://arxiv.org/pdf/2406.08840v1 | 2024-06-13T06:04:34Z | 2024-06-13T06:04:34Z | Conceptual Learning via Embedding Approximations for Reinforcing
Interpretability and Transparency | Concept bottleneck models (CBMs) have emerged as critical tools in domains where interpretability is paramount. These models rely on predefined textual descriptions, referred to as concepts, to inform their decision-making process and offer more accurate reasoning. As a result, the selection of concepts used in the model is of utmost significance. This study proposes underline{textbf{C}}onceptual underline{textbf{L}}earning via underline{textbf{E}}mbedding underline{textbf{A}}pproximations for underline{textbf{R}}einforcing Interpretability and Transparency, abbreviated as CLEAR, a framework for constructing a CBM for image classification. Using score matching and Langevin sampling, we approximate the embedding of concepts within the latent space of a vision-language model (VLM) by learning the scores associated with the joint distribution of images and concepts. A concept selection process is then employed to optimize the similarity between the learned embeddings and the predefined ones. The derived bottleneck offers insights into the CBM's decision-making process, enabling more comprehensive interpretations. Our approach was evaluated through extensive experiments and achieved state-of-the-art performance on various benchmarks. The code for our experiments is available at https://github.com/clearProject/CLEAR/tree/main | [
"['Maor Dikter' 'Tsachi Blau' 'Chaim Baskin']"
] |
null | null | 2406.08847 | null | null | http://arxiv.org/pdf/2406.08847v1 | 2024-06-13T06:15:44Z | 2024-06-13T06:15:44Z | Roping in Uncertainty: Robustness and Regularization in Markov Games | We study robust Markov games (RMG) with $s$-rectangular uncertainty. We show a general equivalence between computing a robust Nash equilibrium (RNE) of a $s$-rectangular RMG and computing a Nash equilibrium (NE) of an appropriately constructed regularized MG. The equivalence result yields a planning algorithm for solving $s$-rectangular RMGs, as well as provable robustness guarantees for policies computed using regularized methods. However, we show that even for just reward-uncertain two-player zero-sum matrix games, computing an RNE is PPAD-hard. Consequently, we derive a special uncertainty structure called efficient player-decomposability and show that RNE for two-player zero-sum RMG in this class can be provably solved in polynomial time. This class includes commonly used uncertainty sets such as $L_1$ and $L_infty$ ball uncertainty sets. | [
"['Jeremy McMahan' 'Giovanni Artiglio' 'Qiaomin Xie']"
] |
null | null | 2406.08851 | null | null | http://arxiv.org/pdf/2406.08851v1 | 2024-06-13T06:29:16Z | 2024-06-13T06:29:16Z | Inverse Probability of Treatment Weighting with Deep Sequence Models
Enables Accurate treatment effect Estimation from Electronic Health Records | Observational data have been actively used to estimate treatment effect, driven by the growing availability of electronic health records (EHRs). However, EHRs typically consist of longitudinal records, often introducing time-dependent confoundings that hinder the unbiased estimation of treatment effect. Inverse probability of treatment weighting (IPTW) is a widely used propensity score method since it provides unbiased treatment effect estimation and its derivation is straightforward. In this study, we aim to utilize IPTW to estimate treatment effect in the presence of time-dependent confounding using claims records. Previous studies have utilized propensity score methods with features derived from claims records through feature processing, which generally requires domain knowledge and additional resources to extract information to accurately estimate propensity scores. Deep sequence models, particularly recurrent neural networks and self-attention-based architectures, have demonstrated good performance in modeling EHRs for various downstream tasks. We propose that these deep sequence models can provide accurate IPTW estimation of treatment effect by directly estimating the propensity scores from claims records without the need for feature processing. We empirically demonstrate this by conducting comprehensive evaluations using synthetic and semi-synthetic datasets. | [
"['Junghwan Lee' 'Simin Ma' 'Nicoleta Serban' 'Shihao Yang']"
] |
null | null | 2406.08853 | null | null | http://arxiv.org/pdf/2406.08853v1 | 2024-06-13T06:36:19Z | 2024-06-13T06:36:19Z | Assessment of Uncertainty Quantification in Universal Differential
Equations | Scientific Machine Learning is a new class of approaches that integrate physical knowledge and mechanistic models with data-driven techniques for uncovering governing equations of complex processes. Among the available approaches, Universal Differential Equations (UDEs) are used to combine prior knowledge in the form of mechanistic formulations with universal function approximators, like neural networks. Integral to the efficacy of UDEs is the joint estimation of parameters within mechanistic formulations and the universal function approximators using empirical data. The robustness and applicability of resultant models, however, hinge upon the rigorous quantification of uncertainties associated with these parameters, as well as the predictive capabilities of the overall model or its constituent components. With this work, we provide a formalisation of uncertainty quantification (UQ) for UDEs and investigate important frequentist and Bayesian methods. By analysing three synthetic examples of varying complexity, we evaluate the validity and efficiency of ensembles, variational inference and Markov chain Monte Carlo sampling as epistemic UQ methods for UDEs. | [
"['Nina Schmid' 'David Fernandes del Pozo' 'Willem Waegeman'\n 'Jan Hasenauer']"
] |
null | null | 2406.08854 | null | null | http://arxiv.org/pdf/2406.08854v1 | 2024-06-13T06:38:09Z | 2024-06-13T06:38:09Z | Current applications and potential future directions of reinforcement
learning-based Digital Twins in agriculture | Digital Twins have gained attention in various industries for simulation, monitoring, and decision-making, relying on ever-improving machine learning models. However, agricultural Digital Twin implementations are limited compared to other industries. Meanwhile, machine learning, particularly reinforcement learning, has shown potential in agricultural applications like optimizing decision-making, task automation, and resource management. A key aspect of Digital Twins is representing physical assets or systems in a virtual environment, which aligns well with reinforcement learning's need for environment representations to learn the best policy for a task. Reinforcement learning in agriculture can thus enable various Digital Twin applications in agricultural domains. This review aims to categorize existing research employing reinforcement learning in agricultural settings by application domains like robotics, greenhouse management, irrigation systems, and crop management, identifying potential future areas for reinforcement learning-based Digital Twins. It also categorizes the reinforcement learning techniques used, including tabular methods, Deep Q-Networks (DQN), Policy Gradient methods, and Actor-Critic algorithms, to overview currently employed models. The review seeks to provide insights into the state-of-the-art in integrating Digital Twins and reinforcement learning in agriculture, identifying gaps and opportunities for future research, and exploring synergies to tackle agricultural challenges and optimize farming, paving the way for more efficient and sustainable farming methodologies. | [
"['Georg Goldenits' 'Kevin Mallinger' 'Sebastian Raubitzek'\n 'Thomas Neubauer']"
] |
null | null | 2406.08858 | null | null | http://arxiv.org/pdf/2406.08858v1 | 2024-06-13T06:44:46Z | 2024-06-13T06:44:46Z | OmniH2O: Universal and Dexterous Human-to-Humanoid Whole-Body
Teleoperation and Learning | We present OmniH2O (Omni Human-to-Humanoid), a learning-based system for whole-body humanoid teleoperation and autonomy. Using kinematic pose as a universal control interface, OmniH2O enables various ways for a human to control a full-sized humanoid with dexterous hands, including using real-time teleoperation through VR headset, verbal instruction, and RGB camera. OmniH2O also enables full autonomy by learning from teleoperated demonstrations or integrating with frontier models such as GPT-4. OmniH2O demonstrates versatility and dexterity in various real-world whole-body tasks through teleoperation or autonomy, such as playing multiple sports, moving and manipulating objects, and interacting with humans. We develop an RL-based sim-to-real pipeline, which involves large-scale retargeting and augmentation of human motion datasets, learning a real-world deployable policy with sparse sensor input by imitating a privileged teacher policy, and reward designs to enhance robustness and stability. We release the first humanoid whole-body control dataset, OmniH2O-6, containing six everyday tasks, and demonstrate humanoid whole-body skill learning from teleoperated datasets. | [
"['Tairan He' 'Zhengyi Luo' 'Xialin He' 'Wenli Xiao' 'Chong Zhang'\n 'Weinan Zhang' 'Kris Kitani' 'Changliu Liu' 'Guanya Shi']"
] |
null | null | 2406.08862 | null | null | http://arxiv.org/pdf/2406.08862v1 | 2024-06-13T06:54:37Z | 2024-06-13T06:54:37Z | Cognitively Inspired Energy-Based World Models | One of the predominant methods for training world models is autoregressive prediction in the output space of the next element of a sequence. In Natural Language Processing (NLP), this takes the form of Large Language Models (LLMs) predicting the next token; in Computer Vision (CV), this takes the form of autoregressive models predicting the next frame/token/pixel. However, this approach differs from human cognition in several respects. First, human predictions about the future actively influence internal cognitive processes. Second, humans naturally evaluate the plausibility of predictions regarding future states. Based on this capability, and third, by assessing when predictions are sufficient, humans allocate a dynamic amount of time to make a prediction. This adaptive process is analogous to System 2 thinking in psychology. All these capabilities are fundamental to the success of humans at high-level reasoning and planning. Therefore, to address the limitations of traditional autoregressive models lacking these human-like capabilities, we introduce Energy-Based World Models (EBWM). EBWM involves training an Energy-Based Model (EBM) to predict the compatibility of a given context and a predicted future state. In doing so, EBWM enables models to achieve all three facets of human cognition described. Moreover, we developed a variant of the traditional autoregressive transformer tailored for Energy-Based models, termed the Energy-Based Transformer (EBT). Our results demonstrate that EBWM scales better with data and GPU Hours than traditional autoregressive transformers in CV, and that EBWM offers promising early scaling in NLP. Consequently, this approach offers an exciting path toward training future models capable of System 2 thinking and intelligently searching across state spaces. | [
"['Alexi Gladstone' 'Ganesh Nanduru' 'Md Mofijul Islam' 'Aman Chadha'\n 'Jundong Li' 'Tariq Iqbal']"
] |
null | null | 2406.08864 | null | null | http://arxiv.org/pdf/2406.08864v1 | 2024-06-13T07:04:22Z | 2024-06-13T07:04:22Z | Research on Early Warning Model of Cardiovascular Disease Based on
Computer Deep Learning | This project intends to study a cardiovascular disease risk early warning model based on one-dimensional convolutional neural networks. First, the missing values of 13 physiological and symptom indicators such as patient age, blood glucose, cholesterol, and chest pain were filled and Z-score was standardized. The convolutional neural network is converted into a 2D matrix, the convolution function of 1,3, and 5 is used for the first-order convolution operation, and the Max Pooling algorithm is adopted for dimension reduction. Set the learning rate and output rate. It is optimized by the Adam algorithm. The result of classification is output by a soft classifier. This study was conducted based on Statlog in the UCI database and heart disease database respectively. The empirical data indicate that the forecasting precision of this technique has been enhanced by 11.2%, relative to conventional approaches, while there is a significant improvement in the logarithmic curve fitting. The efficacy and applicability of the novel approach are corroborated through the examination employing a one-dimensional convolutional neural network. | [
"['Yuxiang Hu' 'Jinxin Hu' 'Ting Xu' 'Bo Zhang' 'Jiajie Yuan'\n 'Haozhang Deng']"
] |
null | null | 2406.08878 | null | null | http://arxiv.org/pdf/2406.08878v3 | 2024-06-26T04:56:39Z | 2024-06-13T07:31:29Z | CIMRL: Combining IMitation and Reinforcement Learning for Safe
Autonomous Driving | Modern approaches to autonomous driving rely heavily on learned components trained with large amounts of human driving data via imitation learning. However, these methods require large amounts of expensive data collection and even then face challenges with safely handling long-tail scenarios and compounding errors over time. At the same time, pure Reinforcement Learning (RL) methods can fail to learn performant policies in sparse, constrained, and challenging-to-define reward settings like driving. Both of these challenges make deploying purely cloned policies in safety critical applications like autonomous vehicles challenging. In this paper we propose Combining IMitation and Reinforcement Learning (CIMRL) approach - a framework that enables training driving policies in simulation through leveraging imitative motion priors and safety constraints. CIMRL does not require extensive reward specification and improves on the closed loop behavior of pure cloning methods. By combining RL and imitation, we demonstrate that our method achieves state-of-the-art results in closed loop simulation driving benchmarks. | [
"['Jonathan Booher' 'Khashayar Rohanimanesh' 'Junhong Xu'\n 'Vladislav Isenbaev' 'Ashwin Balakrishna' 'Ishan Gupta' 'Wei Liu'\n 'Aleksandr Petiushko']"
] |
null | null | 2406.08884 | null | null | http://arxiv.org/pdf/2406.08884v1 | 2024-06-13T07:37:16Z | 2024-06-13T07:37:16Z | The Penalized Inverse Probability Measure for Conformal Classification | The deployment of safe and trustworthy machine learning systems, and particularly complex black box neural networks, in real-world applications requires reliable and certified guarantees on their performance. The conformal prediction framework offers such formal guarantees by transforming any point into a set predictor with valid, finite-set, guarantees on the coverage of the true at a chosen level of confidence. Central to this methodology is the notion of the nonconformity score function that assigns to each example a measure of ''strangeness'' in comparison with the previously seen observations. While the coverage guarantees are maintained regardless of the nonconformity measure, the point predictor and the dataset, previous research has shown that the performance of a conformal model, as measured by its efficiency (the average size of the predicted sets) and its informativeness (the proportion of prediction sets that are singletons), is influenced by the choice of the nonconformity score function. The current work introduces the Penalized Inverse Probability (PIP) nonconformity score, and its regularized version RePIP, that allow the joint optimization of both efficiency and informativeness. Through toy examples and empirical results on the task of crop and weed image classification in agricultural robotics, the current work shows how PIP-based conformal classifiers exhibit precisely the desired behavior in comparison with other nonconformity measures and strike a good balance between informativeness and efficiency. | [
"['Paul Melki' 'Lionel Bombrun' 'Boubacar Diallo' 'Jérôme Dias'\n 'Jean-Pierre da Costa']"
] |
null | null | 2406.08897 | null | null | http://arxiv.org/pdf/2406.08897v1 | 2024-06-13T07:50:44Z | 2024-06-13T07:50:44Z | Motif-driven Subgraph Structure Learning for Graph Classification | To mitigate the suboptimal nature of graph structure, Graph Structure Learning (GSL) has emerged as a promising approach to improve graph structure and boost performance in downstream tasks. Despite the proposal of numerous GSL methods, the progresses in this field mostly concentrated on node-level tasks, while graph-level tasks (e.g., graph classification) remain largely unexplored. Notably, applying node-level GSL to graph classification is non-trivial due to the lack of find-grained guidance for intricate structure learning. Inspired by the vital role of subgraph in graph classification, in this paper we explore the potential of subgraph structure learning for graph classification by tackling the challenges of key subgraph selection and structure optimization. We propose a novel Motif-driven Subgraph Structure Learning method for Graph Classification (MOSGSL). Specifically, MOSGSL incorporates a subgraph structure learning module which can adaptively select important subgraphs. A motif-driven structure guidance module is further introduced to capture key subgraph-level structural patterns (motifs) and facilitate personalized structure learning. Extensive experiments demonstrate a significant and consistent improvement over baselines, as well as its flexibility and generalizability for various backbones and learning procedures. | [
"['Zhiyao Zhou' 'Sheng Zhou' 'Bochao Mao' 'Jiawei Chen' 'Qingyun Sun'\n 'Yan Feng' 'Chun Chen' 'Can Wang']"
] |
null | null | 2406.08898 | null | null | http://arxiv.org/pdf/2406.08898v1 | 2024-06-13T07:51:08Z | 2024-06-13T07:51:08Z | Computer Vision Approaches for Automated Bee Counting Application | Many application from the bee colony health state monitoring could be efficiently solved using a computer vision techniques. One of such challenges is an efficient way for counting the number of incoming and outcoming bees, which could be used to further analyse many trends, such as the bee colony health state, blooming periods, or for investigating the effects of agricultural spraying. In this paper, we compare three methods for the automated bee counting over two own datasets. The best performing method is based on the ResNet-50 convolutional neural network classifier, which achieved accuracy of 87% over the BUT1 dataset and the accuracy of 93% over the BUT2 dataset. | [
"['Simon Bilik' 'Ilona Janakova' 'Adam Ligocki' 'Dominik Ficek'\n 'Karel Horak']"
] |
null | null | 2406.08904 | null | null | http://arxiv.org/pdf/2406.08904v1 | 2024-06-13T07:58:15Z | 2024-06-13T07:58:15Z | AdaPTwin: Low-Cost Adaptive Compression of Product Twins in Transformers | While large transformer-based models have exhibited remarkable performance in speaker-independent speech recognition, their large size and computational requirements make them expensive or impractical to use in resource-constrained settings. In this work, we propose a low-rank adaptive compression technique called AdaPTwin that jointly compresses product-dependent pairs of weight matrices in the transformer attention layer. Our approach can prioritize the compressed model's performance on a specific speaker while maintaining generalizability to new speakers and acoustic conditions. Notably, our technique requires only 8 hours of speech data for fine-tuning, which can be accomplished in under 20 minutes, making it highly cost-effective compared to other compression methods. We demonstrate the efficacy of our approach by compressing the Whisper and Distil-Whisper models by up to 45% while incurring less than a 2% increase in word error rate. | [
"['Emil Biju' 'Anirudh Sriram' 'Mert Pilanci']"
] |
null | null | 2406.08914 | null | null | http://arxiv.org/pdf/2406.08914v1 | 2024-06-13T08:20:58Z | 2024-06-13T08:20:58Z | Transcription-Free Fine-Tuning of Speech Separation Models for Noisy and
Reverberant Multi-Speaker Automatic Speech Recognition | One solution to automatic speech recognition (ASR) of overlapping speakers is to separate speech and then perform ASR on the separated signals. Commonly, the separator produces artefacts which often degrade ASR performance. Addressing this issue typically requires reference transcriptions to jointly train the separation and ASR networks. This is often not viable for training on real-world in-domain audio where reference transcript information is not always available. This paper proposes a transcription-free method for joint training using only audio signals. The proposed method uses embedding differences of pre-trained ASR encoders as a loss with a proposed modification to permutation invariant training (PIT) called guided PIT (GPIT). The method achieves a 6.4% improvement in word error rate (WER) measures over a signal-level loss and also shows enhancement improvements in perceptual measures such as short-time objective intelligibility (STOI). | [
"['William Ravenscroft' 'George Close' 'Stefan Goetze' 'Thomas Hain'\n 'Mohammad Soleymanpour' 'Anurag Chowdhury' 'Mark C. Fuhs']"
] |
null | null | 2406.08917 | null | null | http://arxiv.org/pdf/2406.08917v1 | 2024-06-13T08:28:14Z | 2024-06-13T08:28:14Z | Predicting Fault-Ride-Through Probability of Inverter-Dominated Power
Grids using Machine Learning | Due to the increasing share of renewables, the analysis of the dynamical behavior of power grids gains importance. Effective risk assessments necessitate the analysis of large number of fault scenarios. The computational costs inherent in dynamic simulations impose constraints on the number of configurations that can be analyzed. Machine Learning (ML) has proven to efficiently predict complex power grid properties. Hence, we analyze the potential of ML for predicting dynamic stability of future power grids with large shares of inverters. For this purpose, we generate a new dataset consisting of synthetic power grid models and perform dynamical simulations. As targets for the ML training, we calculate the fault-ride-through probability, which we define as the probability of staying within a ride-through curve after a fault at a bus has been cleared. Importantly, we demonstrate that ML models accurately predict the fault-ride-through probability of synthetic power grids. Finally, we also show that the ML models generalize to an IEEE-96 Test System, which emphasizes the potential of deploying ML methods to study probabilistic stability of power grids. | [
"['Christian Nauck' 'Anna Büttner' 'Sebastian Liemann' 'Frank Hellmann'\n 'Michael Lindner']"
] |
null | null | 2406.08918 | null | null | http://arxiv.org/pdf/2406.08918v2 | 2024-07-10T08:01:26Z | 2024-06-13T08:30:29Z | Beyond the Calibration Point: Mechanism Comparison in Differential
Privacy | In differentially private (DP) machine learning, the privacy guarantees of DP mechanisms are often reported and compared on the basis of a single $(varepsilon, delta)$-pair. This practice overlooks that DP guarantees can vary substantially even between mechanisms sharing a given $(varepsilon, delta)$, and potentially introduces privacy vulnerabilities which can remain undetected. This motivates the need for robust, rigorous methods for comparing DP guarantees in such cases. Here, we introduce the $Delta$-divergence between mechanisms which quantifies the worst-case excess privacy vulnerability of choosing one mechanism over another in terms of $(varepsilon, delta)$, $f$-DP and in terms of a newly presented Bayesian interpretation. Moreover, as a generalisation of the Blackwell theorem, it is endowed with strong decision-theoretic foundations. Through application examples, we show that our techniques can facilitate informed decision-making and reveal gaps in the current understanding of privacy risks, as current practices in DP-SGD often result in choosing mechanisms with high excess privacy vulnerabilities. | [
"['Georgios Kaissis' 'Stefan Kolek' 'Borja Balle' 'Jamie Hayes'\n 'Daniel Rueckert']"
] |
null | null | 2406.08922 | null | null | http://arxiv.org/pdf/2406.08922v1 | 2024-06-13T08:37:01Z | 2024-06-13T08:37:01Z | Navigating the Shadows: Unveiling Effective Disturbances for Modern AI
Content Detectors | With the launch of ChatGPT, large language models (LLMs) have attracted global attention. In the realm of article writing, LLMs have witnessed extensive utilization, giving rise to concerns related to intellectual property protection, personal privacy, and academic integrity. In response, AI-text detection has emerged to distinguish between human and machine-generated content. However, recent research indicates that these detection systems often lack robustness and struggle to effectively differentiate perturbed texts. Currently, there is a lack of systematic evaluations regarding detection performance in real-world applications, and a comprehensive examination of perturbation techniques and detector robustness is also absent. To bridge this gap, our work simulates real-world scenarios in both informal and professional writing, exploring the out-of-the-box performance of current detectors. Additionally, we have constructed 12 black-box text perturbation methods to assess the robustness of current detection models across various perturbation granularities. Furthermore, through adversarial learning experiments, we investigate the impact of perturbation data augmentation on the robustness of AI-text detectors. We have released our code and data at https://github.com/zhouying20/ai-text-detector-evaluation. | [
"['Ying Zhou' 'Ben He' 'Le Sun']"
] |
null | null | 2406.08924 | null | null | http://arxiv.org/pdf/2406.08924v1 | 2024-06-13T08:44:12Z | 2024-06-13T08:44:12Z | Learning Images Across Scales Using Adversarial Training | The real world exhibits rich structure and detail across many scales of observation. It is difficult, however, to capture and represent a broad spectrum of scales using ordinary images. We devise a novel paradigm for learning a representation that captures an orders-of-magnitude variety of scales from an unstructured collection of ordinary images. We treat this collection as a distribution of scale-space slices to be learned using adversarial training, and additionally enforce coherency across slices. Our approach relies on a multiscale generator with carefully injected procedural frequency content, which allows to interactively explore the emerging continuous scale space. Training across vastly different scales poses challenges regarding stability, which we tackle using a supervision scheme that involves careful sampling of scales. We show that our generator can be used as a multiscale generative model, and for reconstructions of scale spaces from unstructured patches. Significantly outperforming the state of the art, we demonstrate zoom-in factors of up to 256x at high quality and scale consistency. | [
"['Krzysztof Wolski' 'Adarsh Djeacoumar' 'Alireza Javanmardi'\n 'Hans-Peter Seidel' 'Christian Theobalt' 'Guillaume Cordonnier'\n 'Karol Myszkowski' 'George Drettakis' 'Xingang Pan' 'Thomas Leimkühler']"
] |
null | null | 2406.08929 | null | null | http://arxiv.org/pdf/2406.08929v2 | 2024-06-23T23:18:07Z | 2024-06-13T08:58:45Z | Step-by-Step Diffusion: An Elementary Tutorial | We present an accessible first course on diffusion models and flow matching for machine learning, aimed at a technical audience with no diffusion experience. We try to simplify the mathematical details as much as possible (sometimes heuristically), while retaining enough precision to derive correct algorithms. | [
"['Preetum Nakkiran' 'Arwen Bradley' 'Hattie Zhou' 'Madhu Advani']"
] |
null | null | 2406.08930 | null | null | http://arxiv.org/pdf/2406.08930v1 | 2024-06-13T08:58:59Z | 2024-06-13T08:58:59Z | Efficient Multi-View Fusion and Flexible Adaptation to View Missing in
Cardiovascular System Signals | The progression of deep learning and the widespread adoption of sensors have facilitated automatic multi-view fusion (MVF) about the cardiovascular system (CVS) signals. However, prevalent MVF model architecture often amalgamates CVS signals from the same temporal step but different views into a unified representation, disregarding the asynchronous nature of cardiovascular events and the inherent heterogeneity across views, leading to catastrophic view confusion. Efficient training strategies specifically tailored for MVF models to attain comprehensive representations need simultaneous consideration. Crucially, real-world data frequently arrives with incomplete views, an aspect rarely noticed by researchers. Thus, the View-Centric Transformer (VCT) and Multitask Masked Autoencoder (M2AE) are specifically designed to emphasize the centrality of each view and harness unlabeled data to achieve superior fused representations. Additionally, we systematically define the missing-view problem for the first time and introduce prompt techniques to aid pretrained MVF models in flexibly adapting to various missing-view scenarios. Rigorous experiments involving atrial fibrillation detection, blood pressure estimation, and sleep staging-typical health monitoring tasks-demonstrate the remarkable advantage of our method in MVF compared to prevailing methodologies. Notably, the prompt technique requires finetuning less than 3% of the entire model's data, substantially fortifying the model's resilience to view missing while circumventing the need for complete retraining. The results demonstrate the effectiveness of our approaches, highlighting their potential for practical applications in cardiovascular health monitoring. Codes and models are released at URL. | [
"['Qihan Hu' 'Daomiao Wang' 'Hong Wu' 'Jian Liu' 'Cuiwei Yang']"
] |
null | null | 2406.08933 | null | null | http://arxiv.org/pdf/2406.08933v1 | 2024-06-13T09:03:53Z | 2024-06-13T09:03:53Z | LaCoOT: Layer Collapse through Optimal Transport | Although deep neural networks are well-known for their remarkable performance in tackling complex tasks, their hunger for computational resources remains a significant hurdle, posing energy-consumption issues and restricting their deployment on resource-constrained devices, which stalls their widespread adoption. In this paper, we present an optimal transport method to reduce the depth of over-parametrized deep neural networks, alleviating their computational burden. More specifically, we propose a new regularization strategy based on the Max-Sliced Wasserstein distance to minimize the distance between the intermediate feature distributions in the neural network. We show that minimizing this distance enables the complete removal of intermediate layers in the network, with almost no performance loss and without requiring any finetuning. We assess the effectiveness of our method on traditional image classification setups. We commit to releasing the source code upon acceptance of the article. | [
"['Victor Quétu' 'Nour Hezbri' 'Enzo Tartaglione']"
] |
null | null | 2406.08938 | null | null | http://arxiv.org/pdf/2406.08938v1 | 2024-06-13T09:07:22Z | 2024-06-13T09:07:22Z | Mirror and Preconditioned Gradient Descent in Wasserstein Space | As the problem of minimizing functionals on the Wasserstein space encompasses many applications in machine learning, different optimization algorithms on $mathbb{R}^d$ have received their counterpart analog on the Wasserstein space. We focus here on lifting two explicit algorithms: mirror descent and preconditioned gradient descent. These algorithms have been introduced to better capture the geometry of the function to minimize and are provably convergent under appropriate (namely relative) smoothness and convexity conditions. Adapting these notions to the Wasserstein space, we prove guarantees of convergence of some Wasserstein-gradient-based discrete-time schemes for new pairings of objective functionals and regularizers. The difficulty here is to carefully select along which curves the functionals should be smooth and convex. We illustrate the advantages of adapting the geometry induced by the regularizer on ill-conditioned optimization tasks, and showcase the improvement of choosing different discrepancies and geometries in a computational biology task of aligning single-cells. | [
"['Clément Bonet' 'Théo Uscidda' 'Adam David'\n 'Pierre-Cyril Aubin-Frankowski' 'Anna Korba']"
] |
null | null | 2406.08943 | null | null | http://arxiv.org/pdf/2406.08943v1 | 2024-06-13T09:12:26Z | 2024-06-13T09:12:26Z | Neural NeRF Compression | Neural Radiance Fields (NeRFs) have emerged as powerful tools for capturing detailed 3D scenes through continuous volumetric representations. Recent NeRFs utilize feature grids to improve rendering quality and speed; however, these representations introduce significant storage overhead. This paper presents a novel method for efficiently compressing a grid-based NeRF model, addressing the storage overhead concern. Our approach is based on the non-linear transform coding paradigm, employing neural compression for compressing the model's feature grids. Due to the lack of training data involving many i.i.d scenes, we design an encoder-free, end-to-end optimized approach for individual scenes, using lightweight decoders. To leverage the spatial inhomogeneity of the latent feature grids, we introduce an importance-weighted rate-distortion objective and a sparse entropy model employing a masking mechanism. Our experimental results validate that our proposed method surpasses existing works in terms of grid-based NeRF compression efficacy and reconstruction quality. | [
"['Tuan Pham' 'Stephan Mandt']"
] |
null | null | 2406.08953 | null | null | http://arxiv.org/pdf/2406.08953v1 | 2024-06-13T09:32:40Z | 2024-06-13T09:32:40Z | Preserving Identity with Variational Score for General-purpose 3D
Editing | We present Piva (Preserving Identity with Variational Score Distillation), a novel optimization-based method for editing images and 3D models based on diffusion models. Specifically, our approach is inspired by the recently proposed method for 2D image editing - Delta Denoising Score (DDS). We pinpoint the limitations in DDS for 2D and 3D editing, which causes detail loss and over-saturation. To address this, we propose an additional score distillation term that enforces identity preservation. This results in a more stable editing process, gradually optimizing NeRF models to match target prompts while retaining crucial input characteristics. We demonstrate the effectiveness of our approach in zero-shot image and neural field editing. Our method successfully alters visual attributes, adds both subtle and substantial structural elements, translates shapes, and achieves competitive results on standard 2D and 3D editing benchmarks. Additionally, our method imposes no constraints like masking or pre-training, making it compatible with a wide range of pre-trained diffusion models. This allows for versatile editing without needing neural field-to-mesh conversion, offering a more user-friendly experience. | [
"['Duong H. Le' 'Tuan Pham' 'Aniruddha Kembhavi' 'Stephan Mandt'\n 'Wei-Chiu Ma' 'Jiasen Lu']"
] |
null | null | 2406.08958 | null | null | http://arxiv.org/pdf/2406.08958v1 | 2024-06-13T09:36:27Z | 2024-06-13T09:36:27Z | An Unsupervised Approach to Achieve Supervised-Level Explainability in
Healthcare Records | Electronic healthcare records are vital for patient safety as they document conditions, plans, and procedures in both free text and medical codes. Language models have significantly enhanced the processing of such records, streamlining workflows and reducing manual data entry, thereby saving healthcare providers significant resources. However, the black-box nature of these models often leaves healthcare professionals hesitant to trust them. State-of-the-art explainability methods increase model transparency but rely on human-annotated evidence spans, which are costly. In this study, we propose an approach to produce plausible and faithful explanations without needing such annotations. We demonstrate on the automated medical coding task that adversarial robustness training improves explanation plausibility and introduce AttInGrad, a new explanation method superior to previous ones. By combining both contributions in a fully unsupervised setup, we produce explanations of comparable quality, or better, to that of a supervised approach. We release our code and model weights. | [
"['Joakim Edin' 'Maria Maistro' 'Lars Maaløe' 'Lasse Borgholt'\n 'Jakob D. Havtorn' 'Tuukka Ruotsalo']"
] |
null | null | 2406.08961 | null | null | http://arxiv.org/pdf/2406.08961v1 | 2024-06-13T09:49:58Z | 2024-06-13T09:49:58Z | SIU: A Million-Scale Structural Small Molecule-Protein Interaction
Dataset for Unbiased Bioactivity Prediction | Small molecules play a pivotal role in modern medicine, and scrutinizing their interactions with protein targets is essential for the discovery and development of novel, life-saving therapeutics. The term "bioactivity" encompasses various biological effects resulting from these interactions, including both binding and functional responses. The magnitude of bioactivity dictates the therapeutic or toxic pharmacological outcomes of small molecules, rendering accurate bioactivity prediction crucial for the development of safe and effective drugs. However, existing structural datasets of small molecule-protein interactions are often limited in scale and lack systematically organized bioactivity labels, thereby impeding our understanding of these interactions and precise bioactivity prediction. In this study, we introduce a comprehensive dataset of small molecule-protein interactions, consisting of over a million binding structures, each annotated with real biological activity labels. This dataset is designed to facilitate unbiased bioactivity prediction. We evaluated several classical models on this dataset, and the results demonstrate that the task of unbiased bioactivity prediction is challenging yet essential. | [
"['Yanwen Huang' 'Bowen Gao' 'Yinjun Jia' 'Hongbo Ma' 'Wei-Ying Ma'\n 'Ya-Qin Zhang' 'Yanyan Lan']"
] |
null | null | 2406.08966 | null | null | http://arxiv.org/pdf/2406.08966v1 | 2024-06-13T09:52:44Z | 2024-06-13T09:52:44Z | Separation Power of Equivariant Neural Networks | The separation power of a machine learning model refers to its capacity to distinguish distinct inputs, and it is often employed as a proxy for its expressivity. In this paper, we propose a theoretical framework to investigate the separation power of equivariant neural networks with point-wise activations. Using the proposed framework, we can derive an explicit description of inputs indistinguishable by a family of neural networks with given architecture, demonstrating that it remains unaffected by the choice of non-polynomial activation function employed. We are able to understand the role played by activation functions in separability. Indeed, we show that all non-polynomial activations, such as ReLU and sigmoid, are equivalent in terms of expressivity, and that they reach maximum discrimination capacity. We demonstrate how assessing the separation power of an equivariant neural network can be simplified to evaluating the separation power of minimal representations. We conclude by illustrating how these minimal components form a hierarchy in separation power. | [
"['Marco Pacini' 'Xiaowen Dong' 'Bruno Lepri' 'Gabriele Santin']"
] |
null | null | 2406.08973 | null | null | http://arxiv.org/pdf/2406.08973v1 | 2024-06-13T10:04:17Z | 2024-06-13T10:04:17Z | XLand-100B: A Large-Scale Multi-Task Dataset for In-Context
Reinforcement Learning | Following the success of the in-context learning paradigm in large-scale language and computer vision models, the recently emerging field of in-context reinforcement learning is experiencing a rapid growth. However, its development has been held back by the lack of challenging benchmarks, as all the experiments have been carried out in simple environments and on small-scale datasets. We present textbf{XLand-100B}, a large-scale dataset for in-context reinforcement learning based on the XLand-MiniGrid environment, as a first step to alleviate this problem. It contains complete learning histories for nearly $30,000$ different tasks, covering $100$B transitions and $2.5$B episodes. It took $50,000$ GPU hours to collect the dataset, which is beyond the reach of most academic labs. Along with the dataset, we provide the utilities to reproduce or expand it even further. With this substantial effort, we aim to democratize research in the rapidly growing field of in-context reinforcement learning and provide a solid foundation for further scaling. The code is open-source and available under Apache 2.0 licence at https://github.com/dunno-lab/xland-minigrid-datasets. | [
"['Alexander Nikulin' 'Ilya Zisman' 'Alexey Zemtsov' 'Viacheslav Sinii'\n 'Vladislav Kurenkov' 'Sergey Kolesnikov']"
] |
null | null | 2406.08980 | null | null | http://arxiv.org/pdf/2406.08980v1 | 2024-06-13T10:23:52Z | 2024-06-13T10:23:52Z | From Theory to Therapy: Reframing SBDD Model Evaluation via Practical
Metrics | Recent advancements in structure-based drug design (SBDD) have significantly enhanced the efficiency and precision of drug discovery by generating molecules tailored to bind specific protein pockets. Despite these technological strides, their practical application in real-world drug development remains challenging due to the complexities of synthesizing and testing these molecules. The reliability of the Vina docking score, the current standard for assessing binding abilities, is increasingly questioned due to its susceptibility to overfitting. To address these limitations, we propose a comprehensive evaluation framework that includes assessing the similarity of generated molecules to known active compounds, introducing a virtual screening-based metric for practical deployment capabilities, and re-evaluating binding affinity more rigorously. Our experiments reveal that while current SBDD models achieve high Vina scores, they fall short in practical usability metrics, highlighting a significant gap between theoretical predictions and real-world applicability. Our proposed metrics and dataset aim to bridge this gap, enhancing the practical applicability of future SBDD models and aligning them more closely with the needs of pharmaceutical research and development. | [
"['Bowen Gao' 'Haichuan Tan' 'Yanwen Huang' 'Minsi Ren' 'Xiao Huang'\n 'Wei-Ying Ma' 'Ya-Qin Zhang' 'Yanyan Lan']"
] |
null | null | 2406.08990 | null | null | http://arxiv.org/pdf/2406.08990v2 | 2024-06-18T17:54:13Z | 2024-06-13T10:38:38Z | BTS: Building Timeseries Dataset: Empowering Large-Scale Building
Analytics | Buildings play a crucial role in human well-being, influencing occupant comfort, health, and safety. Additionally, they contribute significantly to global energy consumption, accounting for one-third of total energy usage, and carbon emissions. Optimizing building performance presents a vital opportunity to combat climate change and promote human flourishing. However, research in building analytics has been hampered by the lack of accessible, available, and comprehensive real-world datasets on multiple building operations. In this paper, we introduce the Building TimeSeries (BTS) dataset. Our dataset covers three buildings over a three-year period, comprising more than ten thousand timeseries data points with hundreds of unique ontologies. Moreover, the metadata is standardized using the Brick schema. To demonstrate the utility of this dataset, we performed benchmarks on two tasks: timeseries ontology classification and zero-shot forecasting. These tasks represent an essential initial step in addressing challenges related to interoperability in building analytics. Access to the dataset and the code used for benchmarking are available here: https://github.com/cruiseresearchgroup/DIEF_BTS . | [
"['Arian Prabowo' 'Xiachong Lin' 'Imran Razzak' 'Hao Xue' 'Emily W. Yap'\n 'Matthew Amos' 'Flora D. Salim']"
] |
null | null | 2406.08993 | null | null | http://arxiv.org/pdf/2406.08993v1 | 2024-06-13T10:53:33Z | 2024-06-13T10:53:33Z | Classic GNNs are Strong Baselines: Reassessing GNNs for Node
Classification | Graph Transformers (GTs) have recently emerged as popular alternatives to traditional message-passing Graph Neural Networks (GNNs), due to their theoretically superior expressiveness and impressive performance reported on standard node classification benchmarks, often significantly outperforming GNNs. In this paper, we conduct a thorough empirical analysis to reevaluate the performance of three classic GNN models (GCN, GAT, and GraphSAGE) against GTs. Our findings suggest that the previously reported superiority of GTs may have been overstated due to suboptimal hyperparameter configurations in GNNs. Remarkably, with slight hyperparameter tuning, these classic GNN models achieve state-of-the-art performance, matching or even exceeding that of recent GTs across 17 out of the 18 diverse datasets examined. Additionally, we conduct detailed ablation studies to investigate the influence of various GNN configurations, such as normalization, dropout, residual connections, network depth, and jumping knowledge mode, on node classification performance. Our study aims to promote a higher standard of empirical rigor in the field of graph machine learning, encouraging more accurate comparisons and evaluations of model capabilities. | [
"['Yuankai Luo' 'Lei Shi' 'Xiao-Ming Wu']"
] |
null | null | 2406.09003 | null | null | http://arxiv.org/pdf/2406.09003v1 | 2024-06-13T11:12:46Z | 2024-06-13T11:12:46Z | Enhancing Cross-Modal Fine-Tuning with Gradually Intermediate Modality
Generation | Large-scale pretrained models have proven immensely valuable in handling data-intensive modalities like text and image. However, fine-tuning these models for certain specialized modalities, such as protein sequence and cosmic ray, poses challenges due to the significant modality discrepancy and scarcity of labeled data. In this paper, we propose an end-to-end method, PaRe, to enhance cross-modal fine-tuning, aiming to transfer a large-scale pretrained model to various target modalities. PaRe employs a gating mechanism to select key patches from both source and target data. Through a modality-agnostic Patch Replacement scheme, these patches are preserved and combined to construct data-rich intermediate modalities ranging from easy to hard. By gradually intermediate modality generation, we can not only effectively bridge the modality gap to enhance stability and transferability of cross-modal fine-tuning, but also address the challenge of limited data in the target modality by leveraging enriched intermediate modality data. Compared with hand-designed, general-purpose, task-specific, and state-of-the-art cross-modal fine-tuning approaches, PaRe demonstrates superior performance across three challenging benchmarks, encompassing more than ten modalities. | [
"['Lincan Cai' 'Shuang Li' 'Wenxuan Ma' 'Jingxuan Kang' 'Binhui Xie'\n 'Zixun Sun' 'Chengwei Zhu']"
] |
null | null | 2406.09009 | null | null | http://arxiv.org/abs/2406.09009v4 | 2024-07-03T14:24:39Z | 2024-06-13T11:29:21Z | Fredformer: Frequency Debiased Transformer for Time Series Forecasting | The Transformer model has shown leading performance in time series forecasting. Nevertheless, in some complex scenarios, it tends to learn low-frequency features in the data and overlook high-frequency features, showing a frequency bias. This bias prevents the model from accurately capturing important high-frequency data features. In this paper, we undertook empirical analyses to understand this bias and discovered that frequency bias results from the model disproportionately focusing on frequency features with higher energy. Based on our analysis, we formulate this bias and propose Fredformer, a Transformer-based framework designed to mitigate frequency bias by learning features equally across different frequency bands. This approach prevents the model from overlooking lower amplitude features important for accurate forecasting. Extensive experiments show the effectiveness of our proposed approach, which can outperform other baselines in different real-world time-series datasets. Furthermore, we introduce a lightweight variant of the Fredformer with an attention matrix approximation, which achieves comparable performance but with much fewer parameters and lower computation costs. The code is available at: https://github.com/chenzRG/Fredformer | [
"['Xihao Piao' 'Zheng Chen' 'Taichi Murayama' 'Yasuko Matsubara'\n 'Yasushi Sakurai']"
] |
null | null | 2406.09014 | null | null | http://arxiv.org/pdf/2406.09014v3 | 2024-06-21T05:24:28Z | 2024-06-13T11:38:58Z | Deep learning empowered sensor fusion to improve infant movement
classification | There is a recent boom in the development of AI solutions to facilitate and enhance diagnostic procedures for established clinical tools. To assess the integrity of the developing nervous system, the Prechtl general movement assessment (GMA) is recognized for its clinical value in diagnosing neurological impairments in early infancy. GMA has been increasingly augmented through machine learning approaches intending to scale-up its application, circumvent costs in the training of human assessors and further standardize classification of spontaneous motor patterns. Available deep learning tools, all of which are based on single sensor modalities, are however still considerably inferior to that of well-trained human assessors. These approaches are hardly comparable as all models are designed, trained and evaluated on proprietary/silo-data sets. With this study we propose a sensor fusion approach for assessing fidgety movements (FMs) comparing three different sensor modalities (pressure, inertial, and visual sensors). Various combinations and two sensor fusion approaches (late and early fusion) for infant movement classification were tested to evaluate whether a multi-sensor system outperforms single modality assessments. The performance of the three-sensor fusion (classification accuracy of 94.5%) was significantly higher than that of any single modality evaluated, suggesting the sensor fusion approach is a promising avenue for automated classification of infant motor patterns. The development of a robust sensor fusion system may significantly enhance AI-based early recognition of neurofunctions, ultimately facilitating automated early detection of neurodevelopmental conditions. | [
"['Tomas Kulvicius' 'Dajie Zhang' 'Luise Poustka' 'Sven Bölte'\n 'Lennart Jahn' 'Sarah Flügge' 'Marc Kraft' 'Markus Zweckstetter'\n 'Karin Nielsen-Saines' 'Florentin Wörgötter' 'Peter B Marschik']"
] |
null | null | 2406.09023 | null | null | http://arxiv.org/pdf/2406.09023v1 | 2024-06-13T11:56:20Z | 2024-06-13T11:56:20Z | Schur's Positive-Definite Network: Deep Learning in the SPD cone with
structure | Estimating matrices in the symmetric positive-definite (SPD) cone is of interest for many applications ranging from computer vision to graph learning. While there exist various convex optimization-based estimators, they remain limited in expressivity due to their model-based approach. The success of deep learning has thus led many to use neural networks to learn to estimate SPD matrices in a data-driven fashion. For learning structured outputs, one promising strategy involves architectures designed by unrolling iterative algorithms, which potentially benefit from inductive bias properties. However, designing correct unrolled architectures for SPD learning is difficult: they either do not guarantee that their output has all the desired properties, rely on heavy computations, or are overly restrained to specific matrices which hinders their expressivity. In this paper, we propose a novel and generic learning module with guaranteed SPD outputs called SpodNet, that also enables learning a larger class of functions than existing approaches. Notably, it solves the challenging task of learning jointly SPD and sparse matrices. Our experiments demonstrate the versatility of SpodNet layers. | [
"['Can Pouliquen' 'Mathurin Massias' 'Titouan Vayer']"
] |
null | null | 2406.09028 | null | null | http://arxiv.org/pdf/2406.09028v1 | 2024-06-13T12:02:51Z | 2024-06-13T12:02:51Z | From Biased to Unbiased Dynamics: An Infinitesimal Generator Approach | We investigate learning the eigenfunctions of evolution operators for time-reversal invariant stochastic processes, a prime example being the Langevin equation used in molecular dynamics. Many physical or chemical processes described by this equation involve transitions between metastable states separated by high potential barriers that can hardly be crossed during a simulation. To overcome this bottleneck, data are collected via biased simulations that explore the state space more rapidly. We propose a framework for learning from biased simulations rooted in the infinitesimal generator of the process and the associated resolvent operator. We contrast our approach to more common ones based on the transfer operator, showing that it can provably learn the spectral properties of the unbiased system from biased data. In experiments, we highlight the advantages of our method over transfer operator approaches and recent developments based on generator learning, demonstrating its effectiveness in estimating eigenfunctions and eigenvalues. Importantly, we show that even with datasets containing only a few relevant transitions due to sub-optimal biasing, our approach recovers relevant information about the transition mechanism. | [
"['Timothée Devergne' 'Vladimir Kostic' 'Michele Parrinello'\n 'Massimiliano Pontil']"
] |
null | null | 2406.09030 | null | null | http://arxiv.org/pdf/2406.09030v1 | 2024-06-13T12:03:40Z | 2024-06-13T12:03:40Z | CUER: Corrected Uniform Experience Replay for Off-Policy Continuous Deep
Reinforcement Learning Algorithms | The utilization of the experience replay mechanism enables agents to effectively leverage their experiences on several occasions. In previous studies, the sampling probability of the transitions was modified based on their relative significance. The process of reassigning sample probabilities for every transition in the replay buffer after each iteration is considered extremely inefficient. Hence, in order to enhance computing efficiency, experience replay prioritization algorithms reassess the importance of a transition as it is sampled. However, the relative importance of the transitions undergoes dynamic adjustments when the agent's policy and value function are iteratively updated. Furthermore, experience replay is a mechanism that retains the transitions generated by the agent's past policies, which could potentially diverge significantly from the agent's most recent policy. An increased deviation from the agent's most recent policy results in a greater frequency of off-policy updates, which has a negative impact on the agent's performance. In this paper, we develop a novel algorithm, Corrected Uniform Experience Replay (CUER), which stochastically samples the stored experience while considering the fairness among all other experiences without ignoring the dynamic nature of the transition importance by making sampled state distribution more on-policy. CUER provides promising improvements for off-policy continuous control algorithms in terms of sample efficiency, final performance, and stability of the policy during the training. | [
"['Arda Sarp Yenicesu' 'Furkan B. Mutlu' 'Suleyman S. Kozat'\n 'Ozgur S. Oguz']"
] |
null | null | 2406.09031 | null | null | http://arxiv.org/pdf/2406.09031v2 | 2024-06-16T12:32:17Z | 2024-06-13T12:04:40Z | A Comprehensive Graph Pooling Benchmark: Effectiveness, Robustness and
Generalizability | Graph pooling has gained attention for its ability to obtain effective node and graph representations for various downstream tasks. Despite the recent surge in graph pooling approaches, there is a lack of standardized experimental settings and fair benchmarks to evaluate their performance. To address this issue, we have constructed a comprehensive benchmark that includes 15 graph pooling methods and 21 different graph datasets. This benchmark systematically assesses the performance of graph pooling methods in three dimensions, i.e., effectiveness, robustness, and generalizability. We first evaluate the performance of these graph pooling approaches across different tasks including graph classification, graph regression and node classification. Then, we investigate their performance under potential noise attacks and out-of-distribution shifts in real-world scenarios. We also involve detailed efficiency analysis and parameter analysis. Extensive experiments validate the strong capability and applicability of graph pooling approaches in various scenarios, which can provide valuable insights and guidance for deep geometric learning research. The source code of our benchmark is available at https://github.com/goose315/Graph_Pooling_Benchmark. | [
"['Pengyun Wang' 'Junyu Luo' 'Yanxin Shen' 'Siyu Heng' 'Xiao Luo']"
] |
null | null | 2406.09038 | null | null | http://arxiv.org/abs/2406.09038v1 | 2024-06-13T12:22:08Z | 2024-06-13T12:22:08Z | CGP++ : A Modern C++ Implementation of Cartesian Genetic Programming | The reference implementation of Cartesian Genetic Programming (CGP) was written in the C programming language. C inherently follows a procedural programming paradigm, which entails challenges in providing a reusable and scalable implementation model for complex structures and methods. Moreover, due to the limiting factors of C, the reference implementation of CGP does not provide a generic framework and is therefore restricted to a set of predefined evaluation types. Besides the reference implementation, we also observe that other existing implementations are limited with respect to the features provided. In this work, we therefore propose the first version of a modern C++ implementation of CGP that pursues object-oriented design and generic programming paradigm to provide an efficient implementation model that can facilitate the discovery of new problem domains and the implementation of complex advanced methods that have been proposed for CGP over time. With the proposal of our new implementation, we aim to generally promote interpretability, accessibility and reproducibility in the field of CGP. | [
"['Roman Kalkreuth' 'Thomas Baeck']"
] |
null | null | 2406.09041 | null | null | http://arxiv.org/pdf/2406.09041v1 | 2024-06-13T12:27:55Z | 2024-06-13T12:27:55Z | ME-Switch: A Memory-Efficient Expert Switching Framework for Large
Language Models | The typical process for developing LLMs involves pre-training a general foundation model on massive data, followed by fine-tuning on task-specific data to create specialized experts. Serving these experts poses challenges, as loading all experts onto devices is impractical, and frequent switching between experts in response to user requests incurs substantial I/O costs, increasing latency and expenses. Previous approaches decompose expert weights into pre-trained model weights and residual delta weights, then quantize the delta weights to reduce model size. However, these methods often lead to significant quantization errors at extremely low bitwidths and assume the appropriate model for a user request is known in advance, which is not practical. To address these issues, we introduce ME-Switch, a memory-efficient expert switching framework for LLM serving. ME-Switch uses mixed-precision quantization, selectively quantizing non-salient input channels of delta weights to extremely low bits while keeping salient ones intact, significantly reducing storage demands while maintaining performance. Additionally, we develop a routing method that efficiently directs user queries to the most suitable expert by transforming the model selection problem into a domain classification problem. Extensive experiments show ME-Switch's promising memory efficiency and routing performance. For example, when serving three models from the Mistral-7B family, ME-Switch reduces model size by 1.74x while maintaining nearly lossless performance on instruction, mathematical reasoning, and code generation tasks. Furthermore, ME-Switch can efficiently serve 16 models from the Mistral-7B family on a single NVIDIA A100 GPU. | [
"['Jing Liu' 'Ruihao Gong' 'Mingyang Zhang' 'Yefei He' 'Jianfei Cai'\n 'Bohan Zhuang']"
] |
null | null | 2406.09046 | null | null | http://arxiv.org/pdf/2406.09046v2 | 2024-07-06T09:25:10Z | 2024-06-11T17:06:34Z | ExioML: Eco-economic dataset for Machine Learning in Global Sectoral
Sustainability | The Environmental Extended Multi-Regional Input-Output analysis is the predominant framework in Ecological Economics for assessing the environmental impact of economic activities. This paper introduces ExioML, the first Machine Learning benchmark dataset designed for sustainability analysis, aimed at lowering barriers and fostering collaboration between Machine Learning and Ecological Economics research. A crucial greenhouse gas emission regression task was conducted to evaluate sectoral sustainability and demonstrate the usability of the dataset. We compared the performance of traditional shallow models with deep learning models, utilizing a diverse Factor Accounting table and incorporating various categorical and numerical features. Our findings reveal that ExioML, with its high usability, enables deep and ensemble models to achieve low mean square errors, establishing a baseline for future Machine Learning research. Through ExioML, we aim to build a foundational dataset supporting various Machine Learning applications and promote climate actions and sustainable investment decisions. | [
"['Yanming Guo' 'Charles Guan' 'Jin Ma']"
] |
null | null | 2406.09048 | null | null | http://arxiv.org/pdf/2406.09048v1 | 2024-06-10T11:05:48Z | 2024-06-10T11:05:48Z | Central Limit Theorem for Bayesian Neural Network trained with
Variational Inference | In this paper, we rigorously derive Central Limit Theorems (CLT) for Bayesian two-layerneural networks in the infinite-width limit and trained by variational inference on a regression task. The different networks are trained via different maximization schemes of the regularized evidence lower bound: (i) the idealized case with exact estimation of a multiple Gaussian integral from the reparametrization trick, (ii) a minibatch scheme using Monte Carlo sampling, commonly known as Bayes-by-Backprop, and (iii) a computationally cheaper algorithm named Minimal VI. The latter was recently introduced by leveraging the information obtained at the level of the mean-field limit. Laws of large numbers are already rigorously proven for the three schemes that admits the same asymptotic limit. By deriving CLT, this work shows that the idealized and Bayes-by-Backprop schemes have similar fluctuation behavior, that is different from the Minimal VI one. Numerical experiments then illustrate that the Minimal VI scheme is still more efficient, in spite of bigger variances, thanks to its important gain in computational complexity. | [
"['Arnaud Descours' 'Tom Huix' 'Arnaud Guillin' 'Manon Michel'\n 'Éric Moulines' 'Boris Nectoux']"
] |
null | null | 2406.09049 | null | null | http://arxiv.org/pdf/2406.09049v1 | 2024-06-10T10:01:07Z | 2024-06-10T10:01:07Z | Efficiently Deciding Algebraic Equivalence of Bow-Free Acyclic Path
Diagrams | For causal discovery in the presence of latent confounders, constraints beyond conditional independences exist that can enable causal discovery algorithms to distinguish more pairs of graphs. Such constraints are not well-understood yet. In the setting of linear structural equation models without bows, we study algebraic constraints and argue that these provide the most fine-grained resolution achievable. We propose efficient algorithms that decide whether two graphs impose the same algebraic constraints, or whether the constraints imposed by one graph are a subset of those imposed by another graph. | [
"['Thijs van Ommen']"
] |
null | null | 2406.09051 | null | null | http://arxiv.org/abs/2406.09051v2 | 2024-06-20T07:47:28Z | 2024-06-07T23:12:51Z | Bayesian Structural Model Updating with Multimodal Variational
Autoencoder | A novel framework for Bayesian structural model updating is presented in this study. The proposed method utilizes the surrogate unimodal encoders of a multimodal variational autoencoder (VAE). The method facilitates an approximation of the likelihood when dealing with a small number of observations. It is particularly suitable for high-dimensional correlated simultaneous observations applicable to various dynamic analysis models. The proposed approach was benchmarked using a numerical model of a single-story frame building with acceleration and dynamic strain measurements. Additionally, an example involving a Bayesian update of nonlinear model parameters for a three-degree-of-freedom lumped mass model demonstrates computational efficiency when compared to using the original VAE, while maintaining adequate accuracy for practical applications. | [
"['Tatsuya Itoi' 'Kazuho Amishiki' 'Sangwon Lee' 'Taro Yaoyama']"
] |
null | null | 2406.09052 | null | null | http://arxiv.org/pdf/2406.09052v1 | 2024-06-07T17:51:27Z | 2024-06-07T17:51:27Z | Data-Free Generative Replay for Class-Incremental Learning on Imbalanced
Data | Continual learning is a challenging problem in machine learning, especially for image classification tasks with imbalanced datasets. It becomes even more challenging when it involves learning new classes incrementally. One method for incremental class learning, addressing dataset imbalance, is rehearsal using previously stored data. In rehearsal-based methods, access to previous data is required for either training the classifier or the generator, but it may not be feasible due to storage, legal, or data access constraints. Although there are many rehearsal-free alternatives for class incremental learning, such as parameter or loss regularization, knowledge distillation, and dynamic architectures, they do not consistently achieve good results, especially on imbalanced data. This paper proposes a new approach called Data-Free Generative Replay (DFGR) for class incremental learning, where the generator is trained without access to real data. In addition, DFGR also addresses dataset imbalance in continual learning of an image classifier. Instead of using training data, DFGR trains a generator using mean and variance statistics of batch-norm and feature maps derived from a pre-trained classification model. The results of our experiments demonstrate that DFGR performs significantly better than other data-free methods and reveal the performance impact of specific parameter settings. DFGR achieves up to 88.5% and 46.6% accuracy on MNIST and FashionMNIST datasets, respectively. Our code is available at https://github.com/2younis/DFGR | [
"['Sohaib Younis' 'Bernhard Seeger']"
] |
null | null | 2406.09062 | null | null | http://arxiv.org/pdf/2406.09062v1 | 2024-06-13T12:51:22Z | 2024-06-13T12:51:22Z | State-Space Modeling in Long Sequence Processing: A Survey on Recurrence
in the Transformer Era | Effectively learning from sequential data is a longstanding goal of Artificial Intelligence, especially in the case of long sequences. From the dawn of Machine Learning, several researchers engaged in the search of algorithms and architectures capable of processing sequences of patterns, retaining information about the past inputs while still leveraging the upcoming data, without losing precious long-term dependencies and correlations. While such an ultimate goal is inspired by the human hallmark of continuous real-time processing of sensory information, several solutions simplified the learning paradigm by artificially limiting the processed context or dealing with sequences of limited length, given in advance. These solutions were further emphasized by the large ubiquity of Transformers, that have initially shaded the role of Recurrent Neural Nets. However, recurrent networks are facing a strong recent revival due to the growing popularity of (deep) State-Space models and novel instances of large-context Transformers, which are both based on recurrent computations to go beyond several limits of currently ubiquitous technologies. In fact, the fast development of Large Language Models enhanced the interest in efficient solutions to process data over time. This survey provides an in-depth summary of the latest approaches that are based on recurrent models for sequential data processing. A complete taxonomy over the latest trends in architectural and algorithmic solutions is reported and discussed, guiding researchers in this appealing research field. The emerging picture suggests that there is room for thinking of novel routes, constituted by learning algorithms which depart from the standard Backpropagation Through Time, towards a more realistic scenario where patterns are effectively processed online, leveraging local-forward computations, opening to further research on this topic. | [
"['Matteo Tiezzi' 'Michele Casoni' 'Alessandro Betti' 'Marco Gori'\n 'Stefano Melacci']"
] |
null | null | 2406.09068 | null | null | http://arxiv.org/pdf/2406.09068v1 | 2024-06-13T12:54:29Z | 2024-06-13T12:54:29Z | Dispelling the Mirage of Progress in Offline MARL through Standardised
Baselines and Evaluation | Offline multi-agent reinforcement learning (MARL) is an emerging field with great promise for real-world applications. Unfortunately, the current state of research in offline MARL is plagued by inconsistencies in baselines and evaluation protocols, which ultimately makes it difficult to accurately assess progress, trust newly proposed innovations, and allow researchers to easily build upon prior work. In this paper, we firstly identify significant shortcomings in existing methodologies for measuring the performance of novel algorithms through a representative study of published offline MARL work. Secondly, by directly comparing to this prior work, we demonstrate that simple, well-implemented baselines can achieve state-of-the-art (SOTA) results across a wide range of tasks. Specifically, we show that on 35 out of 47 datasets used in prior work (almost 75% of cases), we match or surpass the performance of the current purported SOTA. Strikingly, our baselines often substantially outperform these more sophisticated algorithms. Finally, we correct for the shortcomings highlighted from this prior work by introducing a straightforward standardised methodology for evaluation and by providing our baseline implementations with statistically robust results across several scenarios, useful for comparisons in future work. Our proposal includes simple and sensible steps that are easy to adopt, which in combination with solid baselines and comparative results, could substantially improve the overall rigour of empirical science in offline MARL moving forward. | [
"['Claude Formanek' 'Callum Rhys Tilbury' 'Louise Beyers' 'Jonathan Shock'\n 'Arnu Pretorius']"
] |
null | null | 2406.09069 | null | null | http://arxiv.org/pdf/2406.09069v1 | 2024-06-13T12:54:53Z | 2024-06-13T12:54:53Z | On the Robustness of Global Feature Effect Explanations | We study the robustness of global post-hoc explanations for predictive models trained on tabular data. Effects of predictor features in black-box supervised learning are an essential diagnostic tool for model debugging and scientific discovery in applied sciences. However, how vulnerable they are to data and model perturbations remains an open research question. We introduce several theoretical bounds for evaluating the robustness of partial dependence plots and accumulated local effects. Our experimental results with synthetic and real-world datasets quantify the gap between the best and worst-case scenarios of (mis)interpreting machine learning predictions globally. | [
"['Hubert Baniecki' 'Giuseppe Casalicchio' 'Bernd Bischl'\n 'Przemyslaw Biecek']"
] |
null | null | 2406.09070 | null | null | http://arxiv.org/pdf/2406.09070v1 | 2024-06-13T12:55:10Z | 2024-06-13T12:55:10Z | EquiPrompt: Debiasing Diffusion Models via Iterative Bootstrapping in
Chain of Thoughts | In the domain of text-to-image generative models, the inadvertent propagation of biases inherent in training datasets poses significant ethical challenges, particularly in the generation of socially sensitive content. This paper introduces EquiPrompt, a novel method employing Chain of Thought (CoT) reasoning to reduce biases in text-to-image generative models. EquiPrompt uses iterative bootstrapping and bias-aware exemplar selection to balance creativity and ethical responsibility. It integrates iterative reasoning refinement with controlled evaluation techniques, addressing zero-shot CoT issues in sensitive contexts. Experiments on several generation tasks show EquiPrompt effectively lowers bias while maintaining generative quality, advancing ethical AI and socially responsible creative processes.Code will be publically available. | [
"['Zahraa Al Sahili' 'Ioannis Patras' 'Matthew Purver']"
] |
null | null | 2406.09071 | null | null | http://arxiv.org/pdf/2406.09071v1 | 2024-06-07T07:57:34Z | 2024-06-07T07:57:34Z | FlamePINN-1D: Physics-informed neural networks to solve forward and
inverse problems of 1D laminar flames | Given the existence of various forward and inverse problems in combustion studies and applications that necessitate distinct methods for resolution, a framework to solve them in a unified way is critically needed. A promising approach is the integration of machine learning methods with governing equations of combustion systems, which exhibits superior generality and few-shot learning ability compared to purely data-driven methods. In this work, the FlamePINN-1D framework is proposed to solve the forward and inverse problems of 1D laminar flames based on physics-informed neural networks. Three cases with increasing complexity have been tested: Case 1 are freely-propagating premixed (FPP) flames with simplified physical models, while Case 2 and Case 3 are FPP and counterflow premixed (CFP) flames with detailed models, respectively. For forward problems, FlamePINN-1D aims to solve the flame fields and infer the unknown eigenvalues (such as laminar flame speeds) under the constraints of governing equations and boundary conditions. For inverse problems, FlamePINN-1D aims to reconstruct the continuous fields and infer the unknown parameters (such as transport and chemical kinetics parameters) from noisy sparse observations of the flame. Our results strongly validate these capabilities of FlamePINN-1D across various flames and working conditions. Compared to traditional methods, FlamePINN-1D is differentiable and mesh-free, exhibits no discretization errors, and is easier to implement for inverse problems. The inverse problem results also indicate the possibility of optimizing chemical mechanisms from measurements of laboratory 1D flames. Furthermore, some proposed strategies, such as hard constraints and thin-layer normalization, are proven to be essential for the robust learning of FlamePINN-1D. The code for this paper is partially available at https://github.com/CAME-THU/FlamePINN-1D. | [
"['Jiahao Wu' 'Su Zhang' 'Yuxin Wu' 'Guihua Zhang' 'Xin Li' 'Hai Zhang']"
] |
null | null | 2406.09073 | null | null | http://arxiv.org/pdf/2406.09073v1 | 2024-06-13T12:58:00Z | 2024-06-13T12:58:00Z | Are we making progress in unlearning? Findings from the first NeurIPS
unlearning competition | We present the findings of the first NeurIPS competition on unlearning, which sought to stimulate the development of novel algorithms and initiate discussions on formal and robust evaluation methodologies. The competition was highly successful: nearly 1,200 teams from across the world participated, and a wealth of novel, imaginative solutions with different characteristics were contributed. In this paper, we analyze top solutions and delve into discussions on benchmarking unlearning, which itself is a research problem. The evaluation methodology we developed for the competition measures forgetting quality according to a formal notion of unlearning, while incorporating model utility for a holistic evaluation. We analyze the effectiveness of different instantiations of this evaluation framework vis-a-vis the associated compute cost, and discuss implications for standardizing evaluation. We find that the ranking of leading methods remains stable under several variations of this framework, pointing to avenues for reducing the cost of evaluation. Overall, our findings indicate progress in unlearning, with top-performing competition entries surpassing existing algorithms under our evaluation framework. We analyze trade-offs made by different algorithms and strengths or weaknesses in terms of generalizability to new datasets, paving the way for advancing both benchmarking and algorithm development in this important area. | [
"['Eleni Triantafillou' 'Peter Kairouz' 'Fabian Pedregosa' 'Jamie Hayes'\n 'Meghdad Kurmanji' 'Kairan Zhao' 'Vincent Dumoulin'\n 'Julio Jacques Junior' 'Ioannis Mitliagkas' 'Jun Wan'\n 'Lisheng Sun Hosoya' 'Sergio Escalera' 'Gintare Karolina Dziugaite'\n 'Peter Triantafillou' 'Isabelle Guyon']"
] |
null | null | 2406.09079 | null | null | http://arxiv.org/pdf/2406.09079v1 | 2024-06-13T13:03:37Z | 2024-06-13T13:03:37Z | Latent Assistance Networks: Rediscovering Hyperbolic Tangents in RL | Activation functions are one of the key components of a neural network. The most commonly used activation functions can be classed into the category of continuously differentiable (e.g. tanh) and linear-unit functions (e.g. ReLU), both having their own strengths and drawbacks with respect to downstream performance and representation capacity through learning (e.g. measured by the number of dead neurons and the effective rank). In reinforcement learning, the performance of continuously differentiable activations often falls short as compared to linear-unit functions. From the perspective of the activations in the last hidden layer, this paper provides insights regarding this sub-optimality and explores how activation functions influence the occurrence of dead neurons and the magnitude of the effective rank. Additionally, a novel neural architecture is proposed that leverages the product of independent activation values. In the Atari domain, we show faster learning, a reduction in dead neurons and increased effective rank. | [
"['Jacob E. Kooi' 'Mark Hoogendoorn' 'Vincent François-Lavet']"
] |
null | null | 2406.09084 | null | null | http://arxiv.org/pdf/2406.09084v1 | 2024-06-13T13:07:52Z | 2024-06-13T13:07:52Z | Operator-informed score matching for Markov diffusion models | Diffusion models are typically trained using score matching, yet score matching is agnostic to the particular forward process that defines the model. This paper argues that Markov diffusion models enjoy an advantage over other types of diffusion model, as their associated operators can be exploited to improve the training process. In particular, (i) there exists an explicit formal solution to the forward process as a sequence of time-dependent kernel mean embeddings; and (ii) the derivation of score-matching and related estimators can be streamlined. Building upon (i), we propose Riemannian diffusion kernel smoothing, which ameliorates the need for neural score approximation, at least in the low-dimensional context; Building upon (ii), we propose operator-informed score matching, a variance reduction technique that is straightforward to implement in both low- and high-dimensional diffusion modeling and is demonstrated to improve score matching in an empirical proof-of-concept. | [
"['Zheyang Shen' 'Chris J. Oates']"
] |
null | null | 2406.09089 | null | null | http://arxiv.org/pdf/2406.09089v1 | 2024-06-13T13:15:40Z | 2024-06-13T13:15:40Z | DiffPoGAN: Diffusion Policies with Generative Adversarial Networks for
Offline Reinforcement Learning | Offline reinforcement learning (RL) can learn optimal policies from pre-collected offline datasets without interacting with the environment, but the sampled actions of the agent cannot often cover the action distribution under a given state, resulting in the extrapolation error issue. Recent works address this issue by employing generative adversarial networks (GANs). However, these methods often suffer from insufficient constraints on policy exploration and inaccurate representation of behavior policies. Moreover, the generator in GANs fails in fooling the discriminator while maximizing the expected returns of a policy. Inspired by the diffusion, a generative model with powerful feature expressiveness, we propose a new offline RL method named Diffusion Policies with Generative Adversarial Networks (DiffPoGAN). In this approach, the diffusion serves as the policy generator to generate diverse distributions of actions, and a regularization method based on maximum likelihood estimation (MLE) is developed to generate data that approximate the distribution of behavior policies. Besides, we introduce an additional regularization term based on the discriminator output to effectively constrain policy exploration for policy improvement. Comprehensive experiments are conducted on the datasets for deep data-driven reinforcement learning (D4RL), and experimental results show that DiffPoGAN outperforms state-of-the-art methods in offline RL. | [
"['Xuemin Hu' 'Shen Li' 'Yingfen Xu' 'Bo Tang' 'Long Chen']"
] |
null | null | 2406.09105 | null | null | http://arxiv.org/pdf/2406.09105v1 | 2024-06-13T13:31:49Z | 2024-06-13T13:31:49Z | INS-MMBench: A Comprehensive Benchmark for Evaluating LVLMs' Performance
in Insurance | Large Vision-Language Models (LVLMs) have demonstrated outstanding performance in various general multimodal applications such as image recognition and visual reasoning, and have also shown promising potential in specialized domains. However, the application potential of LVLMs in the insurance domain-characterized by rich application scenarios and abundant multimodal data-has not been effectively explored. There is no systematic review of multimodal tasks in the insurance domain, nor a benchmark specifically designed to evaluate the capabilities of LVLMs in insurance. This gap hinders the development of LVLMs within the insurance domain. In this paper, we systematically review and distill multimodal tasks for four representative types of insurance: auto insurance, property insurance, health insurance, and agricultural insurance. We propose INS-MMBench, the first comprehensive LVLMs benchmark tailored for the insurance domain. INS-MMBench comprises a total of 2.2K thoroughly designed multiple-choice questions, covering 12 meta-tasks and 22 fundamental tasks. Furthermore, we evaluate multiple representative LVLMs, including closed-source models such as GPT-4o and open-source models like BLIP-2. This evaluation not only validates the effectiveness of our benchmark but also provides an in-depth performance analysis of current LVLMs on various multimodal tasks in the insurance domain. We hope that INS-MMBench will facilitate the further application of LVLMs in the insurance domain and inspire interdisciplinary development. Our dataset and evaluation code are available at https://github.com/FDU-INS/INS-MMBench. | [
"['Chenwei Lin' 'Hanjia Lyu' 'Xian Xu' 'Jiebo Luo']"
] |
null | null | 2406.09112 | null | null | http://arxiv.org/pdf/2406.09112v1 | 2024-06-13T13:43:01Z | 2024-06-13T13:43:01Z | Large-Scale Evaluation of Open-Set Image Classification Techniques | The goal for classification is to correctly assign labels to unseen samples. However, most methods misclassify samples with unseen labels and assign them to one of the known classes. Open-Set Classification (OSC) algorithms aim to maximize both closed and open-set recognition capabilities. Recent studies showed the utility of such algorithms on small-scale data sets, but limited experimentation makes it difficult to assess their performances in real-world problems. Here, we provide a comprehensive comparison of various OSC algorithms, including training-based (SoftMax, Garbage, EOS) and post-processing methods (Maximum SoftMax Scores, Maximum Logit Scores, OpenMax, EVM, PROSER), the latter are applied on features from the former. We perform our evaluation on three large-scale protocols that mimic real-world challenges, where we train on known and negative open-set samples, and test on known and unknown instances. Our results show that EOS helps to improve performance of almost all post-processing algorithms. Particularly, OpenMax and PROSER are able to exploit better-trained networks, demonstrating the utility of hybrid models. However, while most algorithms work well on negative test samples -- samples of open-set classes seen during training -- they tend to perform poorly when tested on samples of previously unseen unknown classes, especially in challenging conditions. | [
"['Halil Bisgin' 'Andres Palechor' 'Mike Suter' 'Manuel Günther']"
] |
null | null | 2406.09116 | null | null | http://arxiv.org/pdf/2406.09116v1 | 2024-06-13T13:43:59Z | 2024-06-13T13:43:59Z | Injective Flows for parametric hypersurfaces | Normalizing Flows (NFs) are powerful and efficient models for density estimation. When modeling densities on manifolds, NFs can be generalized to injective flows but the Jacobian determinant becomes computationally prohibitive. Current approaches either consider bounds on the log-likelihood or rely on some approximations of the Jacobian determinant. In contrast, we propose injective flows for parametric hypersurfaces and show that for such manifolds we can compute the Jacobian determinant exactly and efficiently, with the same cost as NFs. Furthermore, we show that for the subclass of star-like manifolds we can extend the proposed framework to always allow for a Cartesian representation of the density. We showcase the relevance of modeling densities on hypersurfaces in two settings. Firstly, we introduce a novel Objective Bayesian approach to penalized likelihood models by interpreting level-sets of the penalty as star-like manifolds. Secondly, we consider Bayesian mixture models and introduce a general method for variational inference by defining the posterior of mixture weights on the probability simplex. | [
"['Marcello Massimo Negri' 'Jonathan Aellen' 'Volker Roth']"
] |
null | null | 2406.09130 | null | null | http://arxiv.org/pdf/2406.09130v1 | 2024-06-13T14:01:34Z | 2024-06-13T14:01:34Z | Time-Series Forecasting for Out-of-Distribution Generalization Using
Invariant Learning | Time-series forecasting (TSF) finds broad applications in real-world scenarios. Due to the dynamic nature of time-series data, it is crucial to equip TSF models with out-of-distribution (OOD) generalization abilities, as historical training data and future test data can have different distributions. In this paper, we aim to alleviate the inherent OOD problem in TSF via invariant learning. We identify fundamental challenges of invariant learning for TSF. First, the target variables in TSF may not be sufficiently determined by the input due to unobserved core variables in TSF, breaking the conventional assumption of invariant learning. Second, time-series datasets lack adequate environment labels, while existing environmental inference methods are not suitable for TSF. To address these challenges, we propose FOIL, a model-agnostic framework that enables timeseries Forecasting for Out-of-distribution generalization via Invariant Learning. FOIL employs a novel surrogate loss to mitigate the impact of unobserved variables. Further, FOIL implements a joint optimization by alternately inferring environments effectively with a multi-head network while preserving the temporal adjacency structure, and learning invariant representations across inferred environments for OOD generalized TSF. We demonstrate that the proposed FOIL significantly improves the performance of various TSF models, achieving gains of up to 85%. | [
"['Haoxin Liu' 'Harshavardhan Kamarthi' 'Lingkai Kong' 'Zhiyuan Zhao'\n 'Chao Zhang' 'B. Aditya Prakash']"
] |
null | null | 2406.09131 | null | null | http://arxiv.org/pdf/2406.09131v1 | 2024-06-13T14:02:18Z | 2024-06-13T14:02:18Z | OLGA: One-cLass Graph Autoencoder | One-class learning (OCL) comprises a set of techniques applied when real-world problems have a single class of interest. The usual procedure for OCL is learning a hypersphere that comprises instances of this class and, ideally, repels unseen instances from any other classes. Besides, several OCL algorithms for graphs have been proposed since graph representation learning has succeeded in various fields. These methods may use a two-step strategy, initially representing the graph and, in a second step, classifying its nodes. On the other hand, end-to-end methods learn the node representations while classifying the nodes in one learning process. We highlight three main gaps in the literature on OCL for graphs: (i) non-customized representations for OCL; (ii) the lack of constraints on hypersphere parameters learning; and (iii) the methods' lack of interpretability and visualization. We propose One-cLass Graph Autoencoder (OLGA). OLGA is end-to-end and learns the representations for the graph nodes while encapsulating the interest instances by combining two loss functions. We propose a new hypersphere loss function to encapsulate the interest instances. OLGA combines this new hypersphere loss with the graph autoencoder reconstruction loss to improve model learning. OLGA achieved state-of-the-art results and outperformed six other methods with a statistically significant difference from five methods. Moreover, OLGA learns low-dimensional representations maintaining the classification performance with an interpretable model representation learning and results. | [
"['M. P. S. Gôlo' 'J. G. B. M. Junior' 'D. F. Silva' 'R. M. Marcacini']"
] |
null | null | 2406.09132 | null | null | http://arxiv.org/pdf/2406.09132v2 | 2024-06-18T02:15:18Z | 2024-06-13T14:04:34Z | Jacobian-Enhanced Neural Networks | Jacobian-Enhanced Neural Networks (JENN) are densely connected multi-layer perceptrons, whose training process is modified to predict partial derivatives accurately. Their main benefit is better accuracy with fewer training points compared to standard neural networks. These attributes are particularly desirable in the field of computer-aided design, where there is often the need to replace computationally expensive, physics-based models with fast running approximations, known as surrogate models or meta-models. Since a surrogate emulates the original model accurately in near-real time, it yields a speed benefit that can be used to carry out orders of magnitude more function calls quickly. However, in the special case of gradient-enhanced methods, there is the additional value proposition that partial derivatives are accurate, which is a critical property for one important use-case: surrogate-based optimization. This work derives the complete theory and exemplifies its superiority over standard neural nets for surrogate-based optimization. | [
"['Steven H. Berguin']"
] |
null | null | 2406.09136 | null | null | http://arxiv.org/pdf/2406.09136v1 | 2024-06-13T14:07:02Z | 2024-06-13T14:07:02Z | Chain of Preference Optimization: Improving Chain-of-Thought Reasoning
in LLMs | The recent development of chain-of-thought (CoT) decoding has enabled large language models (LLMs) to generate explicit logical reasoning paths for complex problem-solving. However, research indicates that these paths are not always deliberate and optimal. The tree-of-thought (ToT) method employs tree-searching to extensively explore the reasoning space and find better reasoning paths that CoT decoding might overlook. This deliberation, however, comes at the cost of significantly increased inference complexity. In this work, we demonstrate that fine-tuning LLMs leveraging the search tree constructed by ToT allows CoT to achieve similar or better performance, thereby avoiding the substantial inference burden. This is achieved through Chain of Preference Optimization (CPO), where LLMs are fine-tuned to align each step of the CoT reasoning paths with those of ToT using the inherent preference information in the tree-search process. Extensive experimental results show that CPO significantly improves LLM performance in solving a variety of complex problems, including question answering, fact verification, and arithmetic reasoning, demonstrating its effectiveness. Our code is available at https://github.com/sail-sg/CPO. | [
"['Xuan Zhang' 'Chao Du' 'Tianyu Pang' 'Qian Liu' 'Wei Gao' 'Min Lin']"
] |
null | null | 2406.09137 | null | null | http://arxiv.org/pdf/2406.09137v1 | 2024-06-13T14:07:15Z | 2024-06-13T14:07:15Z | Dynamic Correlation Clustering in Sublinear Update Time | We study the classic problem of correlation clustering in dynamic node streams. In this setting, nodes are either added or randomly deleted over time, and each node pair is connected by a positive or negative edge. The objective is to continuously find a partition which minimizes the sum of positive edges crossing clusters and negative edges within clusters. We present an algorithm that maintains an $O(1)$-approximation with $O$(polylog $n$) amortized update time. Prior to our work, Behnezhad, Charikar, Ma, and L. Tan achieved a $5$-approximation with $O(1)$ expected update time in edge streams which translates in node streams to an $O(D)$-update time where $D$ is the maximum possible degree. Finally we complement our theoretical analysis with experiments on real world data. | [
"['Vincent Cohen-Addad' 'Silvio Lattanzi' 'Andreas Maggiori'\n 'Nikos Parotsidis']"
] |
null | null | 2406.09141 | null | null | http://arxiv.org/pdf/2406.09141v1 | 2024-06-13T14:10:57Z | 2024-06-13T14:10:57Z | Optimal Control of Agent-Based Dynamics under Deep Galerkin Feedback
Laws | Ever since the concepts of dynamic programming were introduced, one of the most difficult challenges has been to adequately address high-dimensional control problems. With growing dimensionality, the utilisation of Deep Neural Networks promises to circumvent the issue of an otherwise exponentially increasing complexity. The paper specifically investigates the sampling issues the Deep Galerkin Method is subjected to. It proposes a drift relaxation-based sampling approach to alleviate the symptoms of high-variance policy approximations. This is validated on mean-field control problems; namely, the variations of the opinion dynamics presented by the Sznajd and the Hegselmann-Krause model. The resulting policies induce a significant cost reduction over manually optimised control functions and show improvements on the Linear-Quadratic Regulator problem over the Deep FBSDE approach. | [
"['Frederik Kelbel']"
] |
null | null | 2406.09143 | null | null | http://arxiv.org/pdf/2406.09143v2 | 2024-06-14T08:33:11Z | 2024-06-13T14:11:19Z | Generative AI-based Prompt Evolution Engineering Design Optimization
With Vision-Language Model | Engineering design optimization requires an efficient combination of a 3D shape representation, an optimization algorithm, and a design performance evaluation method, which is often computationally expensive. We present a prompt evolution design optimization (PEDO) framework contextualized in a vehicle design scenario that leverages a vision-language model for penalizing impractical car designs synthesized by a generative model. The backbone of our framework is an evolutionary strategy coupled with an optimization objective function that comprises a physics-based solver and a vision-language model for practical or functional guidance in the generated car designs. In the prompt evolutionary search, the optimizer iteratively generates a population of text prompts, which embed user specifications on the aerodynamic performance and visual preferences of the 3D car designs. Then, in addition to the computational fluid dynamics simulations, the pre-trained vision-language model is used to penalize impractical designs and, thus, foster the evolutionary algorithm to seek more viable designs. Our investigations on a car design optimization problem show a wide spread of potential car designs generated at the early phase of the search, which indicates a good diversity of designs in the initial populations, and an increase of over 20% in the probability of generating practical designs compared to a baseline framework without using a vision-language model. Visual inspection of the designs against the performance results demonstrates prompt evolution as a very promising paradigm for finding novel designs with good optimization performance while providing ease of use in specifying design specifications and preferences via a natural language interface. | [
"['Melvin Wong' 'Thiago Rios' 'Stefan Menzel' 'Yew Soon Ong']"
] |
null | null | 2406.09147 | null | null | http://arxiv.org/pdf/2406.09147v1 | 2024-06-13T14:14:27Z | 2024-06-13T14:14:27Z | Weakly-supervised anomaly detection for multimodal data distributions | Weakly-supervised anomaly detection can outperform existing unsupervised methods with the assistance of a very small number of labeled anomalies, which attracts increasing attention from researchers. However, existing weakly-supervised anomaly detection methods are limited as these methods do not factor in the multimodel nature of the real-world data distribution. To mitigate this, we propose the Weakly-supervised Variational-mixture-model-based Anomaly Detector (WVAD). WVAD excels in multimodal datasets. It consists of two components: a deep variational mixture model, and an anomaly score estimator. The deep variational mixture model captures various features of the data from different clusters, then these features are delivered to the anomaly score estimator to assess the anomaly levels. Experimental results on three real-world datasets demonstrate WVAD's superiority. | [
"['Xu Tan' 'Junqi Chen' 'Sylwan Rahardja' 'Jiawei Yang' 'Susanto Rahardja']"
] |
null | null | 2406.09152 | null | null | http://arxiv.org/pdf/2406.09152v1 | 2024-06-13T14:16:50Z | 2024-06-13T14:16:50Z | EncCluster: Scalable Functional Encryption in Federated Learning through
Weight Clustering and Probabilistic Filters | Federated Learning (FL) enables model training across decentralized devices by communicating solely local model updates to an aggregation server. Although such limited data sharing makes FL more secure than centralized approached, FL remains vulnerable to inference attacks during model update transmissions. Existing secure aggregation approaches rely on differential privacy or cryptographic schemes like Functional Encryption (FE) to safeguard individual client data. However, such strategies can reduce performance or introduce unacceptable computational and communication overheads on clients running on edge devices with limited resources. In this work, we present EncCluster, a novel method that integrates model compression through weight clustering with recent decentralized FE and privacy-enhancing data encoding using probabilistic filters to deliver strong privacy guarantees in FL without affecting model performance or adding unnecessary burdens to clients. We performed a comprehensive evaluation, spanning various datasets and architectures, to demonstrate EncCluster's scalability across encryption levels. Our findings reveal that EncCluster significantly reduces communication costs - below even conventional FedAvg - and accelerates encryption by more than four times over all baselines; at the same time, it maintains high model accuracy and enhanced privacy assurances. | [
"['Vasileios Tsouvalas' 'Samaneh Mohammadi' 'Ali Balador' 'Tanir Ozcelebi'\n 'Francesco Flammini' 'Nirvana Meratnia']"
] |
null | null | 2406.09155 | null | null | http://arxiv.org/pdf/2406.09155v1 | 2024-06-13T14:18:13Z | 2024-06-13T14:18:13Z | DefAn: Definitive Answer Dataset for LLMs Hallucination Evaluation | Large Language Models (LLMs) have demonstrated remarkable capabilities, revolutionizing the integration of AI in daily life applications. However, they are prone to hallucinations, generating claims that contradict established facts, deviating from prompts, and producing inconsistent responses when the same prompt is presented multiple times. Addressing these issues is challenging due to the lack of comprehensive and easily assessable benchmark datasets. Most existing datasets are small and rely on multiple-choice questions, which are inadequate for evaluating the generative prowess of LLMs. To measure hallucination in LLMs, this paper introduces a comprehensive benchmark dataset comprising over 75,000 prompts across eight domains. These prompts are designed to elicit definitive, concise, and informative answers. The dataset is divided into two segments: one publicly available for testing and assessing LLM performance and a hidden segment for benchmarking various LLMs. In our experiments, we tested six LLMs-GPT-3.5, LLama 2, LLama 3, Gemini, Mixtral, and Zephyr-revealing that overall factual hallucination ranges from 59% to 82% on the public dataset and 57% to 76% in the hidden benchmark. Prompt misalignment hallucination ranges from 6% to 95% in the public dataset and 17% to 94% in the hidden counterpart. Average consistency ranges from 21% to 61% and 22% to 63%, respectively. Domain-wise analysis shows that LLM performance significantly deteriorates when asked for specific numeric information while performing moderately with person, location, and date queries. Our dataset demonstrates its efficacy and serves as a comprehensive benchmark for LLM performance evaluation. Our dataset and LLMs responses are available at href{https://github.com/ashikiut/DefAn}{https://github.com/ashikiut/DefAn}. | [
"['A B M Ashikur Rahman' 'Saeed Anwar' 'Muhammad Usman' 'Ajmal Mian']"
] |
null | null | 2406.09156 | null | null | http://arxiv.org/pdf/2406.09156v1 | 2024-06-13T14:18:56Z | 2024-06-13T14:18:56Z | Towards Multilingual Audio-Visual Question Answering | In this paper, we work towards extending Audio-Visual Question Answering (AVQA) to multilingual settings. Existing AVQA research has predominantly revolved around English and replicating it for addressing AVQA in other languages requires a substantial allocation of resources. As a scalable solution, we leverage machine translation and present two multilingual AVQA datasets for eight languages created from existing benchmark AVQA datasets. This prevents extra human annotation efforts of collecting questions and answers manually. To this end, we propose, MERA framework, by leveraging state-of-the-art (SOTA) video, audio, and textual foundation models for AVQA in multiple languages. We introduce a suite of models namely MERA-L, MERA-C, MERA-T with varied model architectures to benchmark the proposed datasets. We believe our work will open new research directions and act as a reference benchmark for future works in multilingual AVQA. | [
"['Orchid Chetia Phukan' 'Priyabrata Mallick' 'Swarup Ranjan Behera'\n 'Aalekhya Satya Narayani' 'Arun Balaji Buduru' 'Rajesh Sharma']"
] |
null | null | 2406.09168 | null | null | http://arxiv.org/pdf/2406.09168v1 | 2024-06-13T14:30:35Z | 2024-06-13T14:30:35Z | SR-CACO-2: A Dataset for Confocal Fluorescence Microscopy Image
Super-Resolution | Confocal fluorescence microscopy is one of the most accessible and widely used imaging techniques for the study of biological processes. Scanning confocal microscopy allows the capture of high-quality images from 3D samples, yet suffers from well-known limitations such as photobleaching and phototoxicity of specimens caused by intense light exposure, which limits its use in some applications, especially for living cells. Cellular damage can be alleviated by changing imaging parameters to reduce light exposure, often at the expense of image quality. Machine/deep learning methods for single-image super-resolution (SISR) can be applied to restore image quality by upscaling lower-resolution (LR) images to produce high-resolution images (HR). These SISR methods have been successfully applied to photo-realistic images due partly to the abundance of publicly available data. In contrast, the lack of publicly available data partly limits their application and success in scanning confocal microscopy. In this paper, we introduce a large scanning confocal microscopy dataset named SR-CACO-2 that is comprised of low- and high-resolution image pairs marked for three different fluorescent markers. It allows the evaluation of performance of SISR methods on three different upscaling levels (X2, X4, X8). SR-CACO-2 contains the human epithelial cell line Caco-2 (ATCC HTB-37), and it is composed of 22 tiles that have been translated in the form of 9,937 image patches for experiments with SISR methods. Given the new SR-CACO-2 dataset, we also provide benchmarking results for 15 state-of-the-art methods that are representative of the main SISR families. Results show that these methods have limited success in producing high-resolution textures, indicating that SR-CACO-2 represents a challenging problem. Our dataset, code and pretrained weights are available: https://github.com/sbelharbi/sr-caco-2. | [
"['Soufiane Belharbi' 'Mara KM Whitford' 'Phuong Hoang' 'Shakeeb Murtaza'\n 'Luke McCaffrey' 'Eric Granger']"
] |
null | null | 2406.09172 | null | null | http://arxiv.org/pdf/2406.09172v1 | 2024-06-13T14:32:43Z | 2024-06-13T14:32:43Z | Generative vs. Discriminative modeling under the lens of uncertainty
quantification | Learning a parametric model from a given dataset indeed enables to capture intrinsic dependencies between random variables via a parametric conditional probability distribution and in turn predict the value of a label variable given observed variables. In this paper, we undertake a comparative analysis of generative and discriminative approaches which differ in their construction and the structure of the underlying inference problem. Our objective is to compare the ability of both approaches to leverage information from various sources in an epistemic uncertainty aware inference via the posterior predictive distribution. We assess the role of a prior distribution, explicit in the generative case and implicit in the discriminative case, leading to a discussion about discriminative models suffering from imbalanced dataset. We next examine the double role played by the observed variables in the generative case, and discuss the compatibility of both approaches with semi-supervised learning. We also provide with practical insights and we examine how the modeling choice impacts the sampling from the posterior predictive distribution. With regard to this, we propose a general sampling scheme enabling supervised learning for both approaches, as well as semi-supervised learning when compatible with the considered modeling approach. Throughout this paper, we illustrate our arguments and conclusions using the example of affine regression, and validate our comparative analysis through classification simulations using neural network based models. | [
"[\"Elouan Argouarc'h\" 'François Desbouvries' 'Eric Barat' 'Eiji Kawasaki']"
] |
null | null | 2406.09173 | null | null | http://arxiv.org/pdf/2406.09173v1 | 2024-06-13T14:35:11Z | 2024-06-13T14:35:11Z | Potion: Towards Poison Unlearning | Adversarial attacks by malicious actors on machine learning systems, such as introducing poison triggers into training datasets, pose significant risks. The challenge in resolving such an attack arises in practice when only a subset of the poisoned data can be identified. This necessitates the development of methods to remove, i.e. unlearn, poison triggers from already trained models with only a subset of the poison data available. The requirements for this task significantly deviate from privacy-focused unlearning where all of the data to be forgotten by the model is known. Previous work has shown that the undiscovered poisoned samples lead to a failure of established unlearning methods, with only one method, Selective Synaptic Dampening (SSD), showing limited success. Even full retraining, after the removal of the identified poison, cannot address this challenge as the undiscovered poison samples lead to a reintroduction of the poison trigger in the model. Our work addresses two key challenges to advance the state of the art in poison unlearning. First, we introduce a novel outlier-resistant method, based on SSD, that significantly improves model protection and unlearning performance. Second, we introduce Poison Trigger Neutralisation (PTN) search, a fast, parallelisable, hyperparameter search that utilises the characteristic "unlearning versus model protection" trade-off to find suitable hyperparameters in settings where the forget set size is unknown and the retain set is contaminated. We benchmark our contributions using ResNet-9 on CIFAR10 and WideResNet-28x10 on CIFAR100. Experimental results show that our method heals 93.72% of poison compared to SSD with 83.41% and full retraining with 40.68%. We achieve this while also lowering the average model accuracy drop caused by unlearning from 5.68% (SSD) to 1.41% (ours). | [
"['Stefan Schoepf' 'Jack Foster' 'Alexandra Brintrup']"
] |
null | null | 2406.09177 | null | null | http://arxiv.org/pdf/2406.09177v2 | 2024-06-18T12:52:29Z | 2024-06-13T14:39:40Z | Scalable and Flexible Causal Discovery with an Efficient Test for
Adjacency | To make accurate predictions, understand mechanisms, and design interventions in systems of many variables, we wish to learn causal graphs from large scale data. Unfortunately the space of all possible causal graphs is enormous so scalably and accurately searching for the best fit to the data is a challenge. In principle we could substantially decrease the search space, or learn the graph entirely, by testing the conditional independence of variables. However, deciding if two variables are adjacent in a causal graph may require an exponential number of tests. Here we build a scalable and flexible method to evaluate if two variables are adjacent in a causal graph, the Differentiable Adjacency Test (DAT). DAT replaces an exponential number of tests with a provably equivalent relaxed problem. It then solves this problem by training two neural networks. We build a graph learning method based on DAT, DAT-Graph, that can also learn from data with interventions. DAT-Graph can learn graphs of 1000 variables with state of the art accuracy. Using the graph learned by DAT-Graph, we also build models that make much more accurate predictions of the effects of interventions on large scale RNA sequencing data. | [
"['Alan Nawzad Amin' 'Andrew Gordon Wilson']"
] |
null | null | 2406.09179 | null | null | http://arxiv.org/pdf/2406.09179v1 | 2024-06-13T14:41:00Z | 2024-06-13T14:41:00Z | Unlearning with Control: Assessing Real-world Utility for Large Language
Model Unlearning | The compelling goal of eradicating undesirable data behaviors, while preserving usual model functioning, underscores the significance of machine unlearning within the domain of large language models (LLMs). Recent research has begun to approach LLM unlearning via gradient ascent (GA) -- increasing the prediction risk for those training strings targeted to be unlearned, thereby erasing their parameterized responses. Despite their simplicity and efficiency, we suggest that GA-based methods face the propensity towards excessive unlearning, resulting in various undesirable model behaviors, such as catastrophic forgetting, that diminish their practical utility. In this paper, we suggest a set of metrics that can capture multiple facets of real-world utility and propose several controlling methods that can regulate the extent of excessive unlearning. Accordingly, we suggest a general framework to better reflect the practical efficacy of various unlearning methods -- we begin by controlling the unlearning procedures/unlearned models such that no excessive unlearning occurs and follow by the evaluation for unlearning efficacy. Our experimental analysis on established benchmarks revealed that GA-based methods are far from perfect in practice, as strong unlearning is at the high cost of hindering the model utility. We conclude that there is still a long way towards practical and effective LLM unlearning, and more efforts are required in this field. | [
"['Qizhou Wang' 'Bo Han' 'Puning Yang' 'Jianing Zhu' 'Tongliang Liu'\n 'Masashi Sugiyama']"
] |
null | null | 2406.09180 | null | null | http://arxiv.org/pdf/2406.09180v1 | 2024-06-13T14:42:17Z | 2024-06-13T14:42:17Z | Detection-Rate-Emphasized Multi-objective Evolutionary Feature Selection
for Network Intrusion Detection | Network intrusion detection is one of the most important issues in the field of cyber security, and various machine learning techniques have been applied to build intrusion detection systems. However, since the number of features to describe the network connections is often large, where some features are redundant or noisy, feature selection is necessary in such scenarios, which can both improve the efficiency and accuracy. Recently, some researchers focus on using multi-objective evolutionary algorithms (MOEAs) to select features. But usually, they only consider the number of features and classification accuracy as the objectives, resulting in unsatisfactory performance on a critical metric, detection rate. This will lead to the missing of many real attacks and bring huge losses to the network system. In this paper, we propose DR-MOFS to model the feature selection problem in network intrusion detection as a three-objective optimization problem, where the number of features, accuracy and detection rate are optimized simultaneously, and use MOEAs to solve it. Experiments on two popular network intrusion detection datasets NSL-KDD and UNSW-NB15 show that in most cases the proposed method can outperform previous methods, i.e., lead to fewer features, higher accuracy and detection rate. | [
"['Zi-Hang Cheng' 'Haopu Shang' 'Chao Qian']"
] |
null | null | 2406.09182 | null | null | http://arxiv.org/pdf/2406.09182v1 | 2024-06-13T14:45:35Z | 2024-06-13T14:45:35Z | Federated Contrastive Learning for Personalized Semantic Communication | In this letter, we design a federated contrastive learning (FedCL) framework aimed at supporting personalized semantic communication. Our FedCL enables collaborative training of local semantic encoders across multiple clients and a global semantic decoder owned by the base station. This framework supports heterogeneous semantic encoders since it does not require client-side model aggregation. Furthermore, to tackle the semantic imbalance issue arising from heterogeneous datasets across distributed clients, we employ contrastive learning to train a semantic centroid generator (SCG). This generator obtains representative global semantic centroids that exhibit intra-semantic compactness and inter-semantic separability. Consequently, it provides superior supervision for learning discriminative local semantic features. Additionally, we conduct theoretical analysis to quantify the convergence performance of FedCL. Simulation results verify the superiority of the proposed FedCL framework compared to other distributed learning benchmarks in terms of task performance and robustness under different numbers of clients and channel conditions, especially in low signal-to-noise ratio and highly heterogeneous data scenarios. | [
"['Yining Wang' 'Wanli Ni' 'Wenqiang Yi' 'Xiaodong Xu' 'Ping Zhang'\n 'Arumugam Nallanathan']"
] |
null | null | 2406.09183 | null | null | http://arxiv.org/pdf/2406.09183v1 | 2024-06-13T14:46:08Z | 2024-06-13T14:46:08Z | Ridge interpolators in correlated factor regression models -- exact risk
analysis | We consider correlated emph{factor} regression models (FRM) and analyze the performance of classical ridge interpolators. Utilizing powerful emph{Random Duality Theory} (RDT) mathematical engine, we obtain emph{precise} closed form characterizations of the underlying optimization problems and all associated optimizing quantities. In particular, we provide emph{excess prediction risk} characterizations that clearly show the dependence on all key model parameters, covariance matrices, loadings, and dimensions. As a function of the over-parametrization ratio, the generalized least squares (GLS) risk also exhibits the well known emph{double-descent} (non-monotonic) behavior. Similarly to the classical linear regression models (LRM), we demonstrate that such FRM phenomenon can be smoothened out by the optimally tuned ridge regularization. The theoretical results are supplemented by numerical simulations and an excellent agrement between the two is observed. Moreover, we note that ``ridge smootenhing'' is often of limited effect already for over-parametrization ratios above $5$ and of virtually no effect for those above $10$. This solidifies the notion that one of the recently most popular neural networks paradigms -- emph{zero-training (interpolating) generalizes well} -- enjoys wider applicability, including the one within the FRM estimation/prediction context. | [
"['Mihailo Stojnic']"
] |
null | null | 2406.09187 | null | null | http://arxiv.org/pdf/2406.09187v1 | 2024-06-13T14:49:26Z | 2024-06-13T14:49:26Z | GuardAgent: Safeguard LLM Agents by a Guard Agent via Knowledge-Enabled
Reasoning | The rapid advancement of large language models (LLMs) has catalyzed the deployment of LLM-powered agents across numerous applications, raising new concerns regarding their safety and trustworthiness. Existing methods for enhancing the safety of LLMs are not directly transferable to LLM-powered agents due to their diverse objectives and output modalities. In this paper, we propose GuardAgent, the first LLM agent as a guardrail to other LLM agents. Specifically, GuardAgent oversees a target LLM agent by checking whether its inputs/outputs satisfy a set of given guard requests defined by the users. GuardAgent comprises two steps: 1) creating a task plan by analyzing the provided guard requests, and 2) generating guardrail code based on the task plan and executing the code by calling APIs or using external engines. In both steps, an LLM is utilized as the core reasoning component, supplemented by in-context demonstrations retrieved from a memory module. Such knowledge-enabled reasoning allows GuardAgent to understand various textual guard requests and accurately "translate" them into executable code that provides reliable guardrails. Furthermore, GuardAgent is equipped with an extendable toolbox containing functions and APIs and requires no additional LLM training, which underscores its generalization capabilities and low operational overhead. Additionally, we propose two novel benchmarks: an EICU-AC benchmark for assessing privacy-related access control for healthcare agents and a Mind2Web-SC benchmark for safety evaluation for web agents. We show the effectiveness of GuardAgent on these two benchmarks with 98.7% and 90.0% accuracy in moderating invalid inputs and outputs for the two types of agents, respectively. We also show that GuardAgent is able to define novel functions in adaption to emergent LLM agents and guard requests, which underscores its strong generalization capabilities. | [
"['Zhen Xiang' 'Linzhi Zheng' 'Yanjie Li' 'Junyuan Hong' 'Qinbin Li'\n 'Han Xie' 'Jiawei Zhang' 'Zidi Xiong' 'Chulin Xie' 'Carl Yang'\n 'Dawn Song' 'Bo Li']"
] |
null | null | 2406.09194 | null | null | http://arxiv.org/pdf/2406.09194v2 | 2024-06-16T17:34:27Z | 2024-06-13T14:54:30Z | Benign overfitting in Fixed Dimension via Physics-Informed Learning with
Smooth Inductive Bias | Recent advances in machine learning have inspired a surge of research into reconstructing specific quantities of interest from measurements that comply with certain physical laws. These efforts focus on inverse problems that are governed by partial differential equations (PDEs). In this work, we develop an asymptotic Sobolev norm learning curve for kernel ridge(less) regression when addressing (elliptical) linear inverse problems. Our results show that the PDE operators in the inverse problem can stabilize the variance and even behave benign overfitting for fixed-dimensional problems, exhibiting different behaviors from regression problems. Besides, our investigation also demonstrates the impact of various inductive biases introduced by minimizing different Sobolev norms as a form of implicit regularization. For the regularized least squares estimator, we find that all considered inductive biases can achieve the optimal convergence rate, provided the regularization parameter is appropriately chosen. The convergence rate is actually independent to the choice of (smooth enough) inductive bias for both ridge and ridgeless regression. Surprisingly, our smoothness requirement recovered the condition found in Bayesian setting and extend the conclusion to the minimum norm interpolation estimators. | [
"['Honam Wong' 'Wendao Wu' 'Fanghui Liu' 'Yiping Lu']"
] |
null | null | 2406.09196 | null | null | http://arxiv.org/pdf/2406.09196v1 | 2024-06-13T14:55:11Z | 2024-06-13T14:55:11Z | Adaptive Slot Attention: Object Discovery with Dynamic Slot Number | Object-centric learning (OCL) extracts the representation of objects with slots, offering an exceptional blend of flexibility and interpretability for abstracting low-level perceptual features. A widely adopted method within OCL is slot attention, which utilizes attention mechanisms to iteratively refine slot representations. However, a major drawback of most object-centric models, including slot attention, is their reliance on predefining the number of slots. This not only necessitates prior knowledge of the dataset but also overlooks the inherent variability in the number of objects present in each instance. To overcome this fundamental limitation, we present a novel complexity-aware object auto-encoder framework. Within this framework, we introduce an adaptive slot attention (AdaSlot) mechanism that dynamically determines the optimal number of slots based on the content of the data. This is achieved by proposing a discrete slot sampling module that is responsible for selecting an appropriate number of slots from a candidate list. Furthermore, we introduce a masked slot decoder that suppresses unselected slots during the decoding process. Our framework, tested extensively on object discovery tasks with various datasets, shows performance matching or exceeding top fixed-slot models. Moreover, our analysis substantiates that our method exhibits the capability to dynamically adapt the slot number according to each instance's complexity, offering the potential for further exploration in slot attention research. Project will be available at https://kfan21.github.io/AdaSlot/ | [
"['Ke Fan' 'Zechen Bai' 'Tianjun Xiao' 'Tong He' 'Max Horn' 'Yanwei Fu'\n 'Francesco Locatello' 'Zheng Zhang']"
] |
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