categories
string | doi
string | id
string | year
float64 | venue
string | link
string | updated
string | published
string | title
string | abstract
string | authors
sequence |
|---|---|---|---|---|---|---|---|---|---|---|
null | null | 2406.12038 | null | null | http://arxiv.org/pdf/2406.12038v1 | 2024-06-17T19:11:40Z | 2024-06-17T19:11:40Z | Soft Prompting for Unlearning in Large Language Models | The widespread popularity of Large Language Models (LLMs), partly due to their unique ability to perform in-context learning, has also brought to light the importance of ethical and safety considerations when deploying these pre-trained models. In this work, we focus on investigating machine unlearning for LLMs motivated by data protection regulations. In contrast to the growing literature on fine-tuning methods to achieve unlearning, we focus on a comparatively lightweight alternative called soft prompting to realize the unlearning of a subset of training data. With losses designed to enforce forgetting as well as utility preservation, our framework textbf{S}oft textbf{P}rompting for textbf{U}ntextbf{l}earning (SPUL) learns prompt tokens that can be appended to an arbitrary query to induce unlearning of specific examples at inference time without updating LLM parameters. We conduct a rigorous evaluation of the proposed method and our results indicate that SPUL can significantly improve the trade-off between utility and forgetting in the context of text classification with LLMs. We further validate our method using multiple LLMs to highlight the scalability of our framework and provide detailed insights into the choice of hyperparameters and the influence of the size of unlearning data. Our implementation is available at url{https://github.com/karuna-bhaila/llm_unlearning}. | [
"['Karuna Bhaila' 'Minh-Hao Van' 'Xintao Wu']"
] |
null | null | 2406.12042 | null | null | http://arxiv.org/pdf/2406.12042v1 | 2024-06-17T19:22:04Z | 2024-06-17T19:22:04Z | Not All Prompts Are Made Equal: Prompt-based Pruning of Text-to-Image
Diffusion Models | Text-to-image (T2I) diffusion models have demonstrated impressive image generation capabilities. Still, their computational intensity prohibits resource-constrained organizations from deploying T2I models after fine-tuning them on their internal target data. While pruning techniques offer a potential solution to reduce the computational burden of T2I models, static pruning methods use the same pruned model for all input prompts, overlooking the varying capacity requirements of different prompts. Dynamic pruning addresses this issue by utilizing a separate sub-network for each prompt, but it prevents batch parallelism on GPUs. To overcome these limitations, we introduce Adaptive Prompt-Tailored Pruning (APTP), a novel prompt-based pruning method designed for T2I diffusion models. Central to our approach is a prompt router model, which learns to determine the required capacity for an input text prompt and routes it to an architecture code, given a total desired compute budget for prompts. Each architecture code represents a specialized model tailored to the prompts assigned to it, and the number of codes is a hyperparameter. We train the prompt router and architecture codes using contrastive learning, ensuring that similar prompts are mapped to nearby codes. Further, we employ optimal transport to prevent the codes from collapsing into a single one. We demonstrate APTP's effectiveness by pruning Stable Diffusion (SD) V2.1 using CC3M and COCO as target datasets. APTP outperforms the single-model pruning baselines in terms of FID, CLIP, and CMMD scores. Our analysis of the clusters learned by APTP reveals they are semantically meaningful. We also show that APTP can automatically discover previously empirically found challenging prompts for SD, e.g., prompts for generating text images, assigning them to higher capacity codes. | [
"['Alireza Ganjdanesh' 'Reza Shirkavand' 'Shangqian Gao' 'Heng Huang']"
] |
null | null | 2406.12052 | null | null | http://arxiv.org/pdf/2406.12052v1 | 2024-06-17T19:45:21Z | 2024-06-17T19:45:21Z | UniGLM: Training One Unified Language Model for Text-Attributed Graphs | Representation learning on text-attributed graphs (TAGs), where nodes are represented by textual descriptions, is crucial for textual and relational knowledge systems and recommendation systems. Currently, state-of-the-art embedding methods for TAGs primarily focus on fine-tuning language models (e.g., BERT) using structure-aware training signals. While effective, these methods are tailored for individual TAG and cannot generalize across various graph scenarios. Given the shared textual space, leveraging multiple TAGs for joint fine-tuning, aligning text and graph structure from different aspects, would be more beneficial. Motivated by this, we introduce a novel Unified Graph Language Model (UniGLM) framework, the first graph embedding model that generalizes well to both in-domain and cross-domain TAGs. Specifically, UniGLM is trained over multiple TAGs with different domains and scales using self-supervised contrastive learning. UniGLM includes an adaptive positive sample selection technique for identifying structurally similar nodes and a lazy contrastive module that is devised to accelerate training by minimizing repetitive encoding calculations. Extensive empirical results across 9 benchmark TAGs demonstrate UniGLM's efficacy against leading embedding baselines in terms of generalization (various downstream tasks and backbones) and transfer learning (in and out of domain scenarios). The code is available at https://github.com/NYUSHCS/UniGLM. | [
"['Yi Fang' 'Dongzhe Fan' 'Sirui Ding' 'Ninghao Liu' 'Qiaoyu Tan']"
] |
null | null | 2406.12054 | null | null | http://arxiv.org/pdf/2406.12054v1 | 2024-06-17T19:46:49Z | 2024-06-17T19:46:49Z | FAWN: Floor-And-Walls Normal Regularization for Direct Neural TSDF
Reconstruction | Leveraging 3D semantics for direct 3D reconstruction has a great potential yet unleashed. For instance, by assuming that walls are vertical, and a floor is planar and horizontal, we can correct distorted room shapes and eliminate local artifacts such as holes, pits, and hills. In this paper, we propose FAWN, a modification of truncated signed distance function (TSDF) reconstruction methods, which considers scene structure by detecting walls and floor in a scene, and penalizing the corresponding surface normals for deviating from the horizontal and vertical directions. Implemented as a 3D sparse convolutional module, FAWN can be incorporated into any trainable pipeline that predicts TSDF. Since FAWN requires 3D semantics only for training, no additional limitations on further use are imposed. We demonstrate, that FAWN-modified methods use semantics more effectively, than existing semantic-based approaches. Besides, we apply our modification to state-of-the-art TSDF reconstruction methods, and demonstrate a quality gain in SCANNET, ICL-NUIM, TUM RGB-D, and 7SCENES benchmarks. | [
"['Anna Sokolova' 'Anna Vorontsova' 'Bulat Gabdullin' 'Alexander Limonov']"
] |
null | null | 2406.12056 | null | null | http://arxiv.org/pdf/2406.12056v2 | 2024-06-22T22:50:05Z | 2024-06-17T19:48:42Z | Learning Molecular Representation in a Cell | Predicting drug efficacy and safety in vivo requires information on biological responses (e.g., cell morphology and gene expression) to small molecule perturbations. However, current molecular representation learning methods do not provide a comprehensive view of cell states under these perturbations and struggle to remove noise, hindering model generalization. We introduce the Information Alignment (InfoAlign) approach to learn molecular representations through the information bottleneck method in cells. We integrate molecules and cellular response data as nodes into a context graph, connecting them with weighted edges based on chemical, biological, and computational criteria. For each molecule in a training batch, InfoAlign optimizes the encoder's latent representation with a minimality objective to discard redundant structural information. A sufficiency objective decodes the representation to align with different feature spaces from the molecule's neighborhood in the context graph. We demonstrate that the proposed sufficiency objective for alignment is tighter than existing encoder-based contrastive methods. Empirically, we validate representations from InfoAlign in two downstream tasks: molecular property prediction against up to 19 baseline methods across four datasets, plus zero-shot molecule-morphology matching. | [
"['Gang Liu' 'Srijit Seal' 'John Arevalo' 'Zhenwen Liang'\n 'Anne E. Carpenter' 'Meng Jiang' 'Shantanu Singh']"
] |
null | null | 2406.12059 | null | null | http://arxiv.org/pdf/2406.12059v1 | 2024-06-17T19:57:34Z | 2024-06-17T19:57:34Z | A Scalable and Effective Alternative to Graph Transformers | Graph Neural Networks (GNNs) have shown impressive performance in graph representation learning, but they face challenges in capturing long-range dependencies due to their limited expressive power. To address this, Graph Transformers (GTs) were introduced, utilizing self-attention mechanism to effectively model pairwise node relationships. Despite their advantages, GTs suffer from quadratic complexity w.r.t. the number of nodes in the graph, hindering their applicability to large graphs. In this work, we present Graph-Enhanced Contextual Operator (GECO), a scalable and effective alternative to GTs that leverages neighborhood propagation and global convolutions to effectively capture local and global dependencies in quasilinear time. Our study on synthetic datasets reveals that GECO reaches 169x speedup on a graph with 2M nodes w.r.t. optimized attention. Further evaluations on diverse range of benchmarks showcase that GECO scales to large graphs where traditional GTs often face memory and time limitations. Notably, GECO consistently achieves comparable or superior quality compared to baselines, improving the SOTA up to 4.5%, and offering a scalable and effective solution for large-scale graph learning. | [
"['Kaan Sancak' 'Zhigang Hua' 'Jin Fang' 'Yan Xie' 'Andrey Malevich'\n 'Bo Long' 'Muhammed Fatih Balin' 'Ümit V. Çatalyürek']"
] |
null | null | 2406.12060 | null | null | http://arxiv.org/pdf/2406.12060v1 | 2024-06-17T20:00:04Z | 2024-06-17T20:00:04Z | Not Eliminate but Aggregate: Post-Hoc Control over Mixture-of-Experts to
Address Shortcut Shifts in Natural Language Understanding | Recent models for natural language understanding are inclined to exploit simple patterns in datasets, commonly known as shortcuts. These shortcuts hinge on spurious correlations between labels and latent features existing in the training data. At inference time, shortcut-dependent models are likely to generate erroneous predictions under distribution shifts, particularly when some latent features are no longer correlated with the labels. To avoid this, previous studies have trained models to eliminate the reliance on shortcuts. In this study, we explore a different direction: pessimistically aggregating the predictions of a mixture-of-experts, assuming each expert captures relatively different latent features. The experimental results demonstrate that our post-hoc control over the experts significantly enhances the model's robustness to the distribution shift in shortcuts. Besides, we show that our approach has some practical advantages. We also analyze our model and provide results to support the assumption. | [
"['Ukyo Honda' 'Tatsushi Oka' 'Peinan Zhang' 'Masato Mita']"
] |
null | null | 2406.12062 | null | null | http://arxiv.org/pdf/2406.12062v1 | 2024-06-17T20:02:43Z | 2024-06-17T20:02:43Z | Entropic Regression DMD (ERDMD) Discovers Informative Sparse and
Nonuniformly Time Delayed Models | In this work, we present a method which determines optimal multi-step dynamic mode decomposition (DMD) models via entropic regression, which is a nonlinear information flow detection algorithm. Motivated by the higher-order DMD (HODMD) method of cite{clainche}, and the entropic regression (ER) technique for network detection and model construction found in cite{bollt, bollt2}, we develop a method that we call ERDMD that produces high fidelity time-delay DMD models that allow for nonuniform time space, and the time spacing is discovered by consider most informativity based on ER. These models are shown to be highly efficient and robust. We test our method over several data sets generated by chaotic attractors and show that we are able to build excellent reconstructions using relatively minimal models. We likewise are able to better identify multiscale features via our models which enhances the utility of dynamic mode decomposition. | [
"['Christopher W. Curtis' 'Erik Bollt' 'Daniel Jay Alford-Lago']"
] |
null | null | 2406.12065 | null | null | http://arxiv.org/pdf/2406.12065v1 | 2024-06-17T20:08:05Z | 2024-06-17T20:08:05Z | STNAGNN: Spatiotemporal Node Attention Graph Neural Network for
Task-based fMRI Analysis | Task-based fMRI uses actions or stimuli to trigger task-specific brain responses and measures them using BOLD contrast. Despite the significant task-induced spatiotemporal brain activation fluctuations, most studies on task-based fMRI ignore the task context information aligned with fMRI and consider task-based fMRI a coherent sequence. In this paper, we show that using the task structures as data-driven guidance is effective for spatiotemporal analysis. We propose STNAGNN, a GNN-based spatiotemporal architecture, and validate its performance in an autism classification task. The trained model is also interpreted for identifying autism-related spatiotemporal brain biomarkers. | [
"['Jiyao Wang' 'Nicha C. Dvornek' 'Peiyu Duan' 'Lawrence H. Staib'\n 'Pamela Ventola' 'James S. Duncan']"
] |
null | null | 2406.12072 | null | null | http://arxiv.org/pdf/2406.12072v2 | 2024-06-19T03:58:35Z | 2024-06-17T20:16:12Z | DTGB: A Comprehensive Benchmark for Dynamic Text-Attributed Graphs | Dynamic text-attributed graphs (DyTAGs) are prevalent in various real-world scenarios, where each node and edge are associated with text descriptions, and both the graph structure and text descriptions evolve over time. Despite their broad applicability, there is a notable scarcity of benchmark datasets tailored to DyTAGs, which hinders the potential advancement in many research fields. To address this gap, we introduce Dynamic Text-attributed Graph Benchmark (DTGB), a collection of large-scale, time-evolving graphs from diverse domains, with nodes and edges enriched by dynamically changing text attributes and categories. To facilitate the use of DTGB, we design standardized evaluation procedures based on four real-world use cases: future link prediction, destination node retrieval, edge classification, and textual relation generation. These tasks require models to understand both dynamic graph structures and natural language, highlighting the unique challenges posed by DyTAGs. Moreover, we conduct extensive benchmark experiments on DTGB, evaluating 7 popular dynamic graph learning algorithms and their variants of adapting to text attributes with LLM embeddings, along with 6 powerful large language models (LLMs). Our results show the limitations of existing models in handling DyTAGs. Our analysis also demonstrates the utility of DTGB in investigating the incorporation of structural and textual dynamics. The proposed DTGB fosters research on DyTAGs and their broad applications. It offers a comprehensive benchmark for evaluating and advancing models to handle the interplay between dynamic graph structures and natural language. The dataset and source code are available at https://github.com/zjs123/DTGB. | [
"['Jiasheng Zhang' 'Jialin Chen' 'Menglin Yang' 'Aosong Feng'\n 'Shuang Liang' 'Jie Shao' 'Rex Ying']"
] |
null | null | 2406.12079 | null | null | http://arxiv.org/pdf/2406.12079v1 | 2024-06-17T20:40:09Z | 2024-06-17T20:40:09Z | Multi-Dimensional Pruning: Joint Channel, Layer and Block Pruning with
Latency Constraint | As we push the boundaries of performance in various vision tasks, the models grow in size correspondingly. To keep up with this growth, we need very aggressive pruning techniques for efficient inference and deployment on edge devices. Existing pruning approaches are limited to channel pruning and struggle with aggressive parameter reductions. In this paper, we propose a novel multi-dimensional pruning framework that jointly optimizes pruning across channels, layers, and blocks while adhering to latency constraints. We develop a latency modeling technique that accurately captures model-wide latency variations during pruning, which is crucial for achieving an optimal latency-accuracy trade-offs at high pruning ratio. We reformulate pruning as a Mixed-Integer Nonlinear Program (MINLP) to efficiently determine the optimal pruned structure with only a single pass. Our extensive results demonstrate substantial improvements over previous methods, particularly at large pruning ratios. In classification, our method significantly outperforms prior art HALP with a Top-1 accuracy of 70.0(v.s. 68.6) and an FPS of 5262 im/s(v.s. 4101 im/s). In 3D object detection, we establish a new state-of-the-art by pruning StreamPETR at a 45% pruning ratio, achieving higher FPS (37.3 vs. 31.7) and mAP (0.451 vs. 0.449) than the dense baseline. | [
"['Xinglong Sun' 'Barath Lakshmanan' 'Maying Shen' 'Shiyi Lan'\n 'Jingde Chen' 'Jose Alvarez']"
] |
null | null | 2406.12082 | null | null | http://arxiv.org/pdf/2406.12082v1 | 2024-06-17T20:46:18Z | 2024-06-17T20:46:18Z | Uncertainty modeling for fine-tuned implicit functions | Implicit functions such as Neural Radiance Fields (NeRFs), occupancy networks, and signed distance functions (SDFs) have become pivotal in computer vision for reconstructing detailed object shapes from sparse views. Achieving optimal performance with these models can be challenging due to the extreme sparsity of inputs and distribution shifts induced by data corruptions. To this end, large, noise-free synthetic datasets can serve as shape priors to help models fill in gaps, but the resulting reconstructions must be approached with caution. Uncertainty estimation is crucial for assessing the quality of these reconstructions, particularly in identifying areas where the model is uncertain about the parts it has inferred from the prior. In this paper, we introduce Dropsembles, a novel method for uncertainty estimation in tuned implicit functions. We demonstrate the efficacy of our approach through a series of experiments, starting with toy examples and progressing to a real-world scenario. Specifically, we train a Convolutional Occupancy Network on synthetic anatomical data and test it on low-resolution MRI segmentations of the lumbar spine. Our results show that Dropsembles achieve the accuracy and calibration levels of deep ensembles but with significantly less computational cost. | [
"['Anna Susmelj' 'Mael Macuglia' 'Nataša Tagasovska' 'Reto Sutter'\n 'Sebastiano Caprara' 'Jean-Philippe Thiran' 'Ender Konukoglu']"
] |
null | null | 2406.12087 | null | null | http://arxiv.org/pdf/2406.12087v1 | 2024-06-17T20:56:30Z | 2024-06-17T20:56:30Z | Mutual Learning for Finetuning Click-Through Rate Prediction Models | Click-Through Rate (CTR) prediction has become an essential task in digital industries, such as digital advertising or online shopping. Many deep learning-based methods have been implemented and have become state-of-the-art models in the domain. To further improve the performance of CTR models, Knowledge Distillation based approaches have been widely used. However, most of the current CTR prediction models do not have much complex architectures, so it's hard to call one of them 'cumbersome' and the other one 'tiny'. On the other hand, the performance gap is also not very large between complex and simple models. So, distilling knowledge from one model to the other could not be worth the effort. Under these considerations, Mutual Learning could be a better approach, since all the models could be improved mutually. In this paper, we showed how useful the mutual learning algorithm could be when it is between equals. In our experiments on the Criteo and Avazu datasets, the mutual learning algorithm improved the performance of the model by up to 0.66% relative improvement. | [
"['Ibrahim Can Yilmaz' 'Said Aldemir']"
] |
null | null | 2406.12091 | null | null | http://arxiv.org/pdf/2406.12091v2 | 2024-06-19T17:56:17Z | 2024-06-17T21:06:00Z | Is poisoning a real threat to LLM alignment? Maybe more so than you
think | Recent advancements in Reinforcement Learning with Human Feedback (RLHF) have significantly impacted the alignment of Large Language Models (LLMs). The sensitivity of reinforcement learning algorithms such as Proximal Policy Optimization (PPO) has led to new line work on Direct Policy Optimization (DPO), which treats RLHF in a supervised learning framework. The increased practical use of these RLHF methods warrants an analysis of their vulnerabilities. In this work, we investigate the vulnerabilities of DPO to poisoning attacks under different scenarios and compare the effectiveness of preference poisoning, a first of its kind. We comprehensively analyze DPO's vulnerabilities under different types of attacks, i.e., backdoor and non-backdoor attacks, and different poisoning methods across a wide array of language models, i.e., LLama 7B, Mistral 7B, and Gemma 7B. We find that unlike PPO-based methods, which, when it comes to backdoor attacks, require at least 4% of the data to be poisoned to elicit harmful behavior, we exploit the true vulnerabilities of DPO more simply so we can poison the model with only as much as 0.5% of the data. We further investigate the potential reasons behind the vulnerability and how well this vulnerability translates into backdoor vs non-backdoor attacks. | [
"['Pankayaraj Pathmanathan' 'Souradip Chakraborty' 'Xiangyu Liu'\n 'Yongyuan Liang' 'Furong Huang']"
] |
null | null | 2406.12100 | null | null | http://arxiv.org/pdf/2406.12100v1 | 2024-06-17T21:25:36Z | 2024-06-17T21:25:36Z | Adaptive Uncertainty Quantification for Trajectory Prediction Under
Distributional Shift | Trajectory prediction models that can infer both finite future trajectories and their associated uncertainties of the target vehicles in an online setting (e.g., real-world application scenarios) is crucial for ensuring the safe and robust navigation and path planning of autonomous vehicle motion. However, the majority of existing trajectory prediction models have neither considered reducing the uncertainty as one objective during the training stage nor provided reliable uncertainty quantification during inference stage under potential distribution shift. Therefore, in this paper, we propose the Conformal Uncertainty Quantification under Distribution Shift framework, CUQDS, to quantify the uncertainty of the predicted trajectories of existing trajectory prediction models under potential data distribution shift, while considering improving the prediction accuracy of the models and reducing the estimated uncertainty during the training stage. Specifically, CUQDS includes 1) a learning-based Gaussian process regression module that models the output distribution of the base model (any existing trajectory prediction or time series forecasting neural networks) and reduces the estimated uncertainty by additional loss term, and 2) a statistical-based Conformal P control module to calibrate the estimated uncertainty from the Gaussian process regression module in an online setting under potential distribution shift between training and testing data. | [
"['Huiqun Huang' 'Sihong He' 'Fei Miao']"
] |
null | null | 2406.12104 | null | null | http://arxiv.org/pdf/2406.12104v1 | 2024-06-17T21:33:01Z | 2024-06-17T21:33:01Z | End-to-end Text-to-SQL Generation within an Analytics Insight Engine | Recent advancements in Text-to-SQL have pushed database management systems towards greater democratization of data access. Today's language models are at the core of these advancements. They enable impressive Text-to-SQL generation as experienced in the development of Distyl AI's Analytics Insight Engine. Its early deployment with enterprise customers has highlighted three core challenges. First, data analysts expect support with authoring SQL queries of very high complexity. Second, requests are ad-hoc and, as such, require low latency. Finally, generation requires an understanding of domain-specific terminology and practices. The design and implementation of our Text-to-SQL generation pipeline, powered by large language models, tackles these challenges. The core tenants of our approach rely on external knowledge that we extract in a pre-processing phase, on retrieving the appropriate external knowledge at query generation time, and on decomposing SQL query generation following a hierarchical CTE-based structure. Finally, an adaptation framework leverages feedback to update the external knowledge, in turn improving query generation over time. We give an overview of our end-to-end approach and highlight the operators generating SQL during inference. | [
"['Karime Maamari' 'Amine Mhedhbi']"
] |
null | null | 2406.12112 | null | null | http://arxiv.org/pdf/2406.12112v1 | 2024-06-17T21:44:05Z | 2024-06-17T21:44:05Z | Thermodynamic Transferability in Coarse-Grained Force Fields using Graph
Neural Networks | Coarse-graining is a molecular modeling technique in which an atomistic system is represented in a simplified fashion that retains the most significant system features that contribute to a target output, while removing the degrees of freedom that are less relevant. This reduction in model complexity allows coarse-grained molecular simulations to reach increased spatial and temporal scales compared to corresponding all-atom models. A core challenge in coarse-graining is to construct a force field that represents the interactions in the new representation in a way that preserves the atomistic-level properties. Many approaches to building coarse-grained force fields have limited transferability between different thermodynamic conditions as a result of averaging over internal fluctuations at a specific thermodynamic state point. Here, we use a graph-convolutional neural network architecture, the Hierarchically Interacting Particle Neural Network with Tensor Sensitivity (HIP-NN-TS), to develop a highly automated training pipeline for coarse grained force fields which allows for studying the transferability of coarse-grained models based on the force-matching approach. We show that this approach not only yields highly accurate force fields, but also that these force fields are more transferable through a variety of thermodynamic conditions. These results illustrate the potential of machine learning techniques such as graph neural networks to improve the construction of transferable coarse-grained force fields. | [
"['Emily Shinkle' 'Aleksandra Pachalieva' 'Riti Bahl' 'Sakib Matin'\n 'Brendan Gifford' 'Galen T. Craven' 'Nicholas Lubbers']"
] |
null | null | 2406.12114 | null | null | http://arxiv.org/pdf/2406.12114v1 | 2024-06-17T21:45:48Z | 2024-06-17T21:45:48Z | Enhancing Text Classification through LLM-Driven Active Learning and
Human Annotation | In the context of text classification, the financial burden of annotation exercises for creating training data is a critical issue. Active learning techniques, particularly those rooted in uncertainty sampling, offer a cost-effective solution by pinpointing the most instructive samples for manual annotation. Similarly, Large Language Models (LLMs) such as GPT-3.5 provide an alternative for automated annotation but come with concerns regarding their reliability. This study introduces a novel methodology that integrates human annotators and LLMs within an Active Learning framework. We conducted evaluations on three public datasets. IMDB for sentiment analysis, a Fake News dataset for authenticity discernment, and a Movie Genres dataset for multi-label classification.The proposed framework integrates human annotation with the output of LLMs, depending on the model uncertainty levels. This strategy achieves an optimal balance between cost efficiency and classification performance. The empirical results show a substantial decrease in the costs associated with data annotation while either maintaining or improving model accuracy. | [
"['Hamidreza Rouzegar' 'Masoud Makrehchi']"
] |
null | null | 2406.12119 | null | null | http://arxiv.org/pdf/2406.12119v1 | 2024-06-17T21:59:44Z | 2024-06-17T21:59:44Z | Deploying scalable traffic prediction models for efficient management in
real-world large transportation networks during hurricane evacuations | Accurate traffic prediction is vital for effective traffic management during hurricane evacuation. This paper proposes a predictive modeling system that integrates Multilayer Perceptron (MLP) and Long-Short Term Memory (LSTM) models to capture both long-term congestion patterns and short-term speed patterns. Leveraging various input variables, including archived traffic data, spatial-temporal road network information, and hurricane forecast data, the framework is designed to address challenges posed by heterogeneous human behaviors, limited evacuation data, and hurricane event uncertainties. Deployed in a real-world traffic prediction system in Louisiana, the model achieved an 82% accuracy in predicting long-term congestion states over a 6-hour period during a 7-day hurricane-impacted duration. The short-term speed prediction model exhibited Mean Absolute Percentage Errors (MAPEs) ranging from 7% to 13% across evacuation horizons from 1 to 6 hours. Evaluation results underscore the model's potential to enhance traffic management during hurricane evacuations, and real-world deployment highlights its adaptability and scalability in diverse hurricane scenarios within extensive transportation networks. | [
"['Qinhua Jiang' 'Brian Yueshuai He' 'Changju Lee' 'Jiaqi Ma']"
] |
null | null | 2406.12120 | null | null | http://arxiv.org/pdf/2406.12120v1 | 2024-06-17T22:00:26Z | 2024-06-17T22:00:26Z | Adding Conditional Control to Diffusion Models with Reinforcement
Learning | Diffusion models are powerful generative models that allow for precise control over the characteristics of the generated samples. While these diffusion models trained on large datasets have achieved success, there is often a need to introduce additional controls in downstream fine-tuning processes, treating these powerful models as pre-trained diffusion models. This work presents a novel method based on reinforcement learning (RL) to add additional controls, leveraging an offline dataset comprising inputs and corresponding labels. We formulate this task as an RL problem, with the classifier learned from the offline dataset and the KL divergence against pre-trained models serving as the reward functions. We introduce our method, $textbf{CTRL}$ ($textbf{C}$onditioning pre-$textbf{T}$rained diffusion models with $textbf{R}$einforcement $textbf{L}$earning), which produces soft-optimal policies that maximize the abovementioned reward functions. We formally demonstrate that our method enables sampling from the conditional distribution conditioned on additional controls during inference. Our RL-based approach offers several advantages over existing methods. Compared to commonly used classifier-free guidance, our approach improves sample efficiency, and can greatly simplify offline dataset construction by exploiting conditional independence between the inputs and additional controls. Furthermore, unlike classifier guidance, we avoid the need to train classifiers from intermediate states to additional controls. | [
"['Yulai Zhao' 'Masatoshi Uehara' 'Gabriele Scalia' 'Tommaso Biancalani'\n 'Sergey Levine' 'Ehsan Hajiramezanali']"
] |
null | null | 2406.12123 | null | null | http://arxiv.org/pdf/2406.12123v1 | 2024-06-17T22:04:44Z | 2024-06-17T22:04:44Z | ChatEMG: Synthetic Data Generation to Control a Robotic Hand Orthosis
for Stroke | Intent inferral on a hand orthosis for stroke patients is challenging due to the difficulty of data collection from impaired subjects. Additionally, EMG signals exhibit significant variations across different conditions, sessions, and subjects, making it hard for classifiers to generalize. Traditional approaches require a large labeled dataset from the new condition, session, or subject to train intent classifiers; however, this data collection process is burdensome and time-consuming. In this paper, we propose ChatEMG, an autoregressive generative model that can generate synthetic EMG signals conditioned on prompts (i.e., a given sequence of EMG signals). ChatEMG enables us to collect only a small dataset from the new condition, session, or subject and expand it with synthetic samples conditioned on prompts from this new context. ChatEMG leverages a vast repository of previous data via generative training while still remaining context-specific via prompting. Our experiments show that these synthetic samples are classifier-agnostic and can improve intent inferral accuracy for different types of classifiers. We demonstrate that our complete approach can be integrated into a single patient session, including the use of the classifier for functional orthosis-assisted tasks. To the best of our knowledge, this is the first time an intent classifier trained partially on synthetic data has been deployed for functional control of an orthosis by a stroke survivor. Videos and additional information can be found at https://jxu.ai/chatemg. | [
"['Jingxi Xu' 'Runsheng Wang' 'Siqi Shang' 'Ava Chen' 'Lauren Winterbottom'\n 'To-Liang Hsu' 'Wenxi Chen' 'Khondoker Ahmed' 'Pedro Leandro La Rotta'\n 'Xinyue Zhu' 'Dawn M. Nilsen' 'Joel Stein' 'Matei Ciocarlie']"
] |
null | null | 2406.12125 | null | null | http://arxiv.org/pdf/2406.12125v1 | 2024-06-17T22:13:22Z | 2024-06-17T22:13:22Z | Efficient Sequential Decision Making with Large Language Models | This paper focuses on extending the success of large language models (LLMs) to sequential decision making. Existing efforts either (i) re-train or finetune LLMs for decision making, or (ii) design prompts for pretrained LLMs. The former approach suffers from the computational burden of gradient updates, and the latter approach does not show promising results. In this paper, we propose a new approach that leverages online model selection algorithms to efficiently incorporate LLMs agents into sequential decision making. Statistically, our approach significantly outperforms both traditional decision making algorithms and vanilla LLM agents. Computationally, our approach avoids the need for expensive gradient updates of LLMs, and throughout the decision making process, it requires only a small number of LLM calls. We conduct extensive experiments to verify the effectiveness of our proposed approach. As an example, on a large-scale Amazon dataset, our approach achieves more than a $6$x performance gain over baselines while calling LLMs in only $1.5$% of the time steps. | [
"['Dingyang Chen' 'Qi Zhang' 'Yinglun Zhu']"
] |
null | null | 2406.12140 | null | null | http://arxiv.org/pdf/2406.12140v1 | 2024-06-17T23:02:20Z | 2024-06-17T23:02:20Z | COT Flow: Learning Optimal-Transport Image Sampling and Editing by
Contrastive Pairs | Diffusion models have demonstrated strong performance in sampling and editing multi-modal data with high generation quality, yet they suffer from the iterative generation process which is computationally expensive and slow. In addition, most methods are constrained to generate data from Gaussian noise, which limits their sampling and editing flexibility. To overcome both disadvantages, we present Contrastive Optimal Transport Flow (COT Flow), a new method that achieves fast and high-quality generation with improved zero-shot editing flexibility compared to previous diffusion models. Benefiting from optimal transport (OT), our method has no limitation on the prior distribution, enabling unpaired image-to-image (I2I) translation and doubling the editable space (at both the start and end of the trajectory) compared to other zero-shot editing methods. In terms of quality, COT Flow can generate competitive results in merely one step compared to previous state-of-the-art unpaired image-to-image (I2I) translation methods. To highlight the advantages of COT Flow through the introduction of OT, we introduce the COT Editor to perform user-guided editing with excellent flexibility and quality. The code will be released at https://github.com/zuxinrui/cot_flow. | [
"['Xinrui Zu' 'Qian Tao']"
] |
null | null | 2406.12142 | null | null | http://arxiv.org/pdf/2406.12142v1 | 2024-06-17T23:08:46Z | 2024-06-17T23:08:46Z | Slicing Through Bias: Explaining Performance Gaps in Medical Image
Analysis using Slice Discovery Methods | Machine learning models have achieved high overall accuracy in medical image analysis. However, performance disparities on specific patient groups pose challenges to their clinical utility, safety, and fairness. This can affect known patient groups - such as those based on sex, age, or disease subtype - as well as previously unknown and unlabeled groups. Furthermore, the root cause of such observed performance disparities is often challenging to uncover, hindering mitigation efforts. In this paper, to address these issues, we leverage Slice Discovery Methods (SDMs) to identify interpretable underperforming subsets of data and formulate hypotheses regarding the cause of observed performance disparities. We introduce a novel SDM and apply it in a case study on the classification of pneumothorax and atelectasis from chest x-rays. Our study demonstrates the effectiveness of SDMs in hypothesis formulation and yields an explanation of previously observed but unexplained performance disparities between male and female patients in widely used chest X-ray datasets and models. Our findings indicate shortcut learning in both classification tasks, through the presence of chest drains and ECG wires, respectively. Sex-based differences in the prevalence of these shortcut features appear to cause the observed classification performance gap, representing a previously underappreciated interaction between shortcut learning and model fairness analyses. | [
"['Vincent Olesen' 'Nina Weng' 'Aasa Feragen' 'Eike Petersen']"
] |
null | null | 2406.12150 | null | null | http://arxiv.org/pdf/2406.12150v1 | 2024-06-17T23:39:29Z | 2024-06-17T23:39:29Z | ChaosMining: A Benchmark to Evaluate Post-Hoc Local Attribution Methods
in Low SNR Environments | In this study, we examine the efficacy of post-hoc local attribution methods in identifying features with predictive power from irrelevant ones in domains characterized by a low signal-to-noise ratio (SNR), a common scenario in real-world machine learning applications. We developed synthetic datasets encompassing symbolic functional, image, and audio data, incorporating a benchmark on the {it (Model (times) Attribution(times) Noise Condition)} triplet. By rigorously testing various classic models trained from scratch, we gained valuable insights into the performance of these attribution methods in multiple conditions. Based on these findings, we introduce a novel extension to the notable recursive feature elimination (RFE) algorithm, enhancing its applicability for neural networks. Our experiments highlight its strengths in prediction and feature selection, alongside limitations in scalability. Further details and additional minor findings are included in the appendix, with extensive discussions. The codes and resources are available at href{https://github.com/geshijoker/ChaosMining/}{URL}. | [
"['Ge Shi' 'Ziwen Kan' 'Jason Smucny' 'Ian Davidson']"
] |
null | null | 2406.12159 | null | null | http://arxiv.org/pdf/2406.12159v1 | 2024-06-18T00:17:30Z | 2024-06-18T00:17:30Z | Exploring the Impact of a Transformer's Latent Space Geometry on
Downstream Task Performance | It is generally thought that transformer-based large language models benefit from pre-training by learning generic linguistic knowledge that can be focused on a specific task during fine-tuning. However, we propose that much of the benefit from pre-training may be captured by geometric characteristics of the latent space representations, divorced from any specific linguistic knowledge. In this work we explore the relationship between GLUE benchmarking task performance and a variety of measures applied to the latent space resulting from BERT-type contextual language models. We find that there is a strong linear relationship between a measure of quantized cell density and average GLUE performance and that these measures may be predictive of otherwise surprising GLUE performance for several non-standard BERT-type models from the literature. These results may be suggestive of a strategy for decreasing pre-training requirements, wherein model initialization can be informed by the geometric characteristics of the model's latent space. | [
"['Anna C. Marbut' 'John W. Chandler' 'Travis J. Wheeler']"
] |
null | null | 2406.12168 | null | null | http://arxiv.org/pdf/2406.12168v2 | 2024-06-19T05:25:27Z | 2024-06-18T00:41:40Z | BPO: Supercharging Online Preference Learning by Adhering to the
Proximity of Behavior LLM | Direct alignment from preferences (DAP) has emerged as a promising paradigm for aligning large language models (LLMs) to human desiderata from pre-collected, offline preference datasets. While recent studies indicate that existing offline DAP methods can directly benefit from online training samples, we highlight the need to develop specific online DAP algorithms to fully harness the power of online training. Specifically, we identify that the learned LLM should adhere to the proximity of the behavior LLM, which collects the training samples. To this end, we propose online Preference Optimization in proximity to the Behavior LLM (BPO), emphasizing the importance of constructing a proper trust region for LLM alignment. We conduct extensive experiments to validate the effectiveness and applicability of our approach by integrating it with various DAP methods, resulting in significant performance improvements across a wide range of tasks when training with the same amount of preference data. Even when only introducing one additional data collection phase, our online BPO improves its offline DAP baseline from 72.0% to 80.2% on TL;DR and from 82.2% to 89.1% on Anthropic Helpfulness in terms of win rate against human reference text. | [
"['Wenda Xu' 'Jiachen Li' 'William Yang Wang' 'Lei Li']"
] |
null | null | 2406.12177 | null | null | http://arxiv.org/pdf/2406.12177v1 | 2024-06-18T01:08:42Z | 2024-06-18T01:08:42Z | Location-based Radiology Report-Guided Semi-supervised Learning for
Prostate Cancer Detection | Prostate cancer is one of the most prevalent malignancies in the world. While deep learning has potential to further improve computer-aided prostate cancer detection on MRI, its efficacy hinges on the exhaustive curation of manually annotated images. We propose a novel methodology of semisupervised learning (SSL) guided by automatically extracted clinical information, specifically the lesion locations in radiology reports, allowing for use of unannotated images to reduce the annotation burden. By leveraging lesion locations, we refined pseudo labels, which were then used to train our location-based SSL model. We show that our SSL method can improve prostate lesion detection by utilizing unannotated images, with more substantial impacts being observed when larger proportions of unannotated images are used. | [
"['Alex Chen' 'Nathan Lay' 'Stephanie Harmon' 'Kutsev Ozyoruk'\n 'Enis Yilmaz' 'Brad J. Wood' 'Peter A. Pinto' 'Peter L. Choyke'\n 'Baris Turkbey']"
] |
null | null | 2406.12193 | null | null | http://arxiv.org/pdf/2406.12193v2 | 2024-06-25T07:25:23Z | 2024-06-18T01:47:38Z | Adaptive Collaborative Correlation Learning-based Semi-Supervised
Multi-Label Feature Selection | Semi-supervised multi-label feature selection has recently been developed to solve the curse of dimensionality problem in high-dimensional multi-label data with certain samples missing labels. Although many efforts have been made, most existing methods use a predefined graph approach to capture the sample similarity or the label correlation. In this manner, the presence of noise and outliers within the original feature space can undermine the reliability of the resulting sample similarity graph. It also fails to precisely depict the label correlation due to the existence of unknown labels. Besides, these methods only consider the discriminative power of selected features, while neglecting their redundancy. In this paper, we propose an Adaptive Collaborative Correlation lEarning-based Semi-Supervised Multi-label Feature Selection (Access-MFS) method to address these issues. Specifically, a generalized regression model equipped with an extended uncorrelated constraint is introduced to select discriminative yet irrelevant features and maintain consistency between predicted and ground-truth labels in labeled data, simultaneously. Then, the instance correlation and label correlation are integrated into the proposed regression model to adaptively learn both the sample similarity graph and the label similarity graph, which mutually enhance feature selection performance. Extensive experimental results demonstrate the superiority of the proposed Access-MFS over other state-of-the-art methods. | [
"['Yanyong Huang' 'Li Yang' 'Dongjie Wang' 'Ke Li' 'Xiuwen Yi' 'Fengmao Lv'\n 'Tianrui Li']"
] |
null | null | 2406.12195 | null | null | http://arxiv.org/pdf/2406.12195v1 | 2024-06-18T01:49:48Z | 2024-06-18T01:49:48Z | Quantum Compiling with Reinforcement Learning on a Superconducting
Processor | To effectively implement quantum algorithms on noisy intermediate-scale quantum (NISQ) processors is a central task in modern quantum technology. NISQ processors feature tens to a few hundreds of noisy qubits with limited coherence times and gate operations with errors, so NISQ algorithms naturally require employing circuits of short lengths via quantum compilation. Here, we develop a reinforcement learning (RL)-based quantum compiler for a superconducting processor and demonstrate its capability of discovering novel and hardware-amenable circuits with short lengths. We show that for the three-qubit quantum Fourier transformation, a compiled circuit using only seven CZ gates with unity circuit fidelity can be achieved. The compiler is also able to find optimal circuits under device topological constraints, with lengths considerably shorter than those by the conventional method. Our study exemplifies the codesign of the software with hardware for efficient quantum compilation, offering valuable insights for the advancement of RL-based compilers. | [
"['Z. T. Wang' 'Qiuhao Chen' 'Yuxuan Du' 'Z. H. Yang' 'Xiaoxia Cai'\n 'Kaixuan Huang' 'Jingning Zhang' 'Kai Xu' 'Jun Du' 'Yinan Li'\n 'Yuling Jiao' 'Xingyao Wu' 'Wu Liu' 'Xiliang Lu' 'Huikai Xu' 'Yirong Jin'\n 'Ruixia Wang' 'Haifeng Yu' 'S. P. Zhao']"
] |
null | null | 2406.12199 | null | null | http://arxiv.org/pdf/2406.12199v2 | 2024-06-27T05:18:57Z | 2024-06-18T01:55:37Z | Time Series Modeling for Heart Rate Prediction: From ARIMA to
Transformers | Cardiovascular disease (CVD) is a leading cause of death globally, necessitating precise forecasting models for monitoring vital signs like heart rate, blood pressure, and ECG. Traditional models, such as ARIMA and Prophet, are limited by their need for manual parameter tuning and challenges in handling noisy, sparse, and highly variable medical data. This study investigates advanced deep learning models, including LSTM, and transformer-based architectures, for predicting heart rate time series from the MIT-BIH Database. Results demonstrate that deep learning models, particularly PatchTST, significantly outperform traditional models across multiple metrics, capturing complex patterns and dependencies more effectively. This research underscores the potential of deep learning to enhance patient monitoring and CVD management, suggesting substantial clinical benefits. Future work should extend these findings to larger, more diverse datasets and real-world clinical applications to further validate and optimize model performance. | [
"['Haowei Ni' 'Shuchen Meng' 'Xieming Geng' 'Panfeng Li' 'Zhuoying Li'\n 'Xupeng Chen' 'Xiaotong Wang' 'Shiyao Zhang']"
] |
null | null | 2406.12200 | null | null | http://arxiv.org/pdf/2406.12200v1 | 2024-06-18T01:56:22Z | 2024-06-18T01:56:22Z | SFedCA: Credit Assignment-Based Active Client Selection Strategy for
Spiking Federated Learning | Spiking federated learning is an emerging distributed learning paradigm that allows resource-constrained devices to train collaboratively at low power consumption without exchanging local data. It takes advantage of both the privacy computation property in federated learning (FL) and the energy efficiency in spiking neural networks (SNN). Thus, it is highly promising to revolutionize the efficient processing of multimedia data. However, existing spiking federated learning methods employ a random selection approach for client aggregation, assuming unbiased client participation. This neglect of statistical heterogeneity affects the convergence and accuracy of the global model significantly. In our work, we propose a credit assignment-based active client selection strategy, the SFedCA, to judiciously aggregate clients that contribute to the global sample distribution balance. Specifically, the client credits are assigned by the firing intensity state before and after local model training, which reflects the local data distribution difference from the global model. Comprehensive experiments are conducted on various non-identical and independent distribution (non-IID) scenarios. The experimental results demonstrate that the SFedCA outperforms the existing state-of-the-art spiking federated learning methods, and requires fewer communication rounds. | [
"['Qiugang Zhan' 'Jinbo Cao' 'Xiurui Xie' 'Malu Zhang' 'Huajin Tang'\n 'Guisong Liu']"
] |
null | null | 2406.12204 | null | null | http://arxiv.org/pdf/2406.12204v1 | 2024-06-18T02:03:07Z | 2024-06-18T02:03:07Z | An Optimal Transport Approach for Network Regression | We study the problem of network regression, where one is interested in how the topology of a network changes as a function of Euclidean covariates. We build upon recent developments in generalized regression models on metric spaces based on Fr'echet means and propose a network regression method using the Wasserstein metric. We show that when representing graphs as multivariate Gaussian distributions, the network regression problem requires the computation of a Riemannian center of mass (i.e., Fr'echet means). Fr'echet means with non-negative weights translates into a barycenter problem and can be efficiently computed using fixed point iterations. Although the convergence guarantees of fixed-point iterations for the computation of Wasserstein affine averages remain an open problem, we provide evidence of convergence in a large number of synthetic and real-data scenarios. Extensive numerical results show that the proposed approach improves existing procedures by accurately accounting for graph size, topology, and sparsity in synthetic experiments. Additionally, real-world experiments using the proposed approach result in higher Coefficient of Determination ($R^{2}$) values and lower mean squared prediction error (MSPE), cementing improved prediction capabilities in practice. | [
"['Alex G. Zalles' 'Kai M. Hung' 'Ann E. Finneran' 'Lydia Beaudrot'\n 'César A. Uribe']"
] |
null | null | 2406.12205 | null | null | http://arxiv.org/pdf/2406.12205v1 | 2024-06-18T02:03:12Z | 2024-06-18T02:03:12Z | Order-Optimal Instance-Dependent Bounds for Offline Reinforcement
Learning with Preference Feedback | We consider offline reinforcement learning (RL) with preference feedback in which the implicit reward is a linear function of an unknown parameter. Given an offline dataset, our objective consists in ascertaining the optimal action for each state, with the ultimate goal of minimizing the {em simple regret}. We propose an algorithm, underline{RL} with underline{L}ocally underline{O}ptimal underline{W}eights or {sc RL-LOW}, which yields a simple regret of $exp ( - Omega(n/H) )$ where $n$ is the number of data samples and $H$ denotes an instance-dependent hardness quantity that depends explicitly on the suboptimality gap of each action. Furthermore, we derive a first-of-its-kind instance-dependent lower bound in offline RL with preference feedback. Interestingly, we observe that the lower and upper bounds on the simple regret match order-wise in the exponent, demonstrating order-wise optimality of {sc RL-LOW}. In view of privacy considerations in practical applications, we also extend {sc RL-LOW} to the setting of $(varepsilon,delta)$-differential privacy and show, somewhat surprisingly, that the hardness parameter $H$ is unchanged in the asymptotic regime as $n$ tends to infinity; this underscores the inherent efficiency of {sc RL-LOW} in terms of preserving the privacy of the observed rewards. Given our focus on establishing instance-dependent bounds, our work stands in stark contrast to previous works that focus on establishing worst-case regrets for offline RL with preference feedback. | [
"['Zhirui Chen' 'Vincent Y. F. Tan']"
] |
null | null | 2406.12220 | null | null | http://arxiv.org/pdf/2406.12220v1 | 2024-06-18T02:42:19Z | 2024-06-18T02:42:19Z | Hierarchical Associative Memory, Parallelized MLP-Mixer, and Symmetry
Breaking | Transformers have established themselves as the leading neural network model in natural language processing and are increasingly foundational in various domains. In vision, the MLP-Mixer model has demonstrated competitive performance, suggesting that attention mechanisms might not be indispensable. Inspired by this, recent research has explored replacing attention modules with other mechanisms, including those described by MetaFormers. However, the theoretical framework for these models remains underdeveloped. This paper proposes a novel perspective by integrating Krotov's hierarchical associative memory with MetaFormers, enabling a comprehensive representation of the entire Transformer block, encompassing token-/channel-mixing modules, layer normalization, and skip connections, as a single Hopfield network. This approach yields a parallelized MLP-Mixer derived from a three-layer Hopfield network, which naturally incorporates symmetric token-/channel-mixing modules and layer normalization. Empirical studies reveal that symmetric interaction matrices in the model hinder performance in image recognition tasks. Introducing symmetry-breaking effects transitions the performance of the symmetric parallelized MLP-Mixer to that of the vanilla MLP-Mixer. This indicates that during standard training, weight matrices of the vanilla MLP-Mixer spontaneously acquire a symmetry-breaking configuration, enhancing their effectiveness. These findings offer insights into the intrinsic properties of Transformers and MLP-Mixers and their theoretical underpinnings, providing a robust framework for future model design and optimization. | [
"['Ryo Karakida' 'Toshihiro Ota' 'Masato Taki']"
] |
null | null | 2406.12222 | null | null | http://arxiv.org/abs/2406.12222v1 | 2024-06-18T02:43:56Z | 2024-06-18T02:43:56Z | BadSampler: Harnessing the Power of Catastrophic Forgetting to Poison
Byzantine-robust Federated Learning | Federated Learning (FL) is susceptible to poisoning attacks, wherein compromised clients manipulate the global model by modifying local datasets or sending manipulated model updates. Experienced defenders can readily detect and mitigate the poisoning effects of malicious behaviors using Byzantine-robust aggregation rules. However, the exploration of poisoning attacks in scenarios where such behaviors are absent remains largely unexplored for Byzantine-robust FL. This paper addresses the challenging problem of poisoning Byzantine-robust FL by introducing catastrophic forgetting. To fill this gap, we first formally define generalization error and establish its connection to catastrophic forgetting, paving the way for the development of a clean-label data poisoning attack named BadSampler. This attack leverages only clean-label data (i.e., without poisoned data) to poison Byzantine-robust FL and requires the adversary to selectively sample training data with high loss to feed model training and maximize the model's generalization error. We formulate the attack as an optimization problem and present two elegant adversarial sampling strategies, Top-$kappa$ sampling, and meta-sampling, to approximately solve it. Additionally, our formal error upper bound and time complexity analysis demonstrate that our design can preserve attack utility with high efficiency. Extensive evaluations on two real-world datasets illustrate the effectiveness and performance of our proposed attacks. | [
"['Yi Liu' 'Cong Wang' 'Xingliang Yuan']"
] |
null | null | 2406.12229 | null | null | http://arxiv.org/pdf/2406.12229v1 | 2024-06-18T03:07:25Z | 2024-06-18T03:07:25Z | Spatially Resolved Gene Expression Prediction from Histology via
Multi-view Graph Contrastive Learning with HSIC-bottleneck Regularization | The rapid development of spatial transcriptomics(ST) enables the measurement of gene expression at spatial resolution, making it possible to simultaneously profile the gene expression, spatial locations of spots, and the matched histopathological images. However, the cost for collecting ST data is much higher than acquiring histopathological images, and thus several studies attempt to predict the gene expression on ST by leveraging their corresponding histopathological images. Most of the existing image-based gene prediction models treat the prediction task on each spot of ST data independently, which ignores the spatial dependency among spots. In addition, while the histology images share phenotypic characteristics with the ST data, it is still challenge to extract such common information to help align paired image and expression representations. To address the above issues, we propose a Multi-view Graph Contrastive Learning framework with HSIC-bottleneck Regularization(ST-GCHB) aiming at learning shared representation to help impute the gene expression of the queried imagingspots by considering their spatial dependency. | [
"['Changxi Chi' 'Hang Shi' 'Qi Zhu' 'Daoqiang Zhang' 'Wei Shao']"
] |
null | null | 2406.12241 | null | null | http://arxiv.org/pdf/2406.12241v1 | 2024-06-18T03:32:10Z | 2024-06-18T03:32:10Z | More Efficient Randomized Exploration for Reinforcement Learning via
Approximate Sampling | Thompson sampling (TS) is one of the most popular exploration techniques in reinforcement learning (RL). However, most TS algorithms with theoretical guarantees are difficult to implement and not generalizable to Deep RL. While the emerging approximate sampling-based exploration schemes are promising, most existing algorithms are specific to linear Markov Decision Processes (MDP) with suboptimal regret bounds, or only use the most basic samplers such as Langevin Monte Carlo. In this work, we propose an algorithmic framework that incorporates different approximate sampling methods with the recently proposed Feel-Good Thompson Sampling (FGTS) approach (Zhang, 2022; Dann et al., 2021), which was previously known to be computationally intractable in general. When applied to linear MDPs, our regret analysis yields the best known dependency of regret on dimensionality, surpassing existing randomized algorithms. Additionally, we provide explicit sampling complexity for each employed sampler. Empirically, we show that in tasks where deep exploration is necessary, our proposed algorithms that combine FGTS and approximate sampling perform significantly better compared to other strong baselines. On several challenging games from the Atari 57 suite, our algorithms achieve performance that is either better than or on par with other strong baselines from the deep RL literature. | [
"['Haque Ishfaq' 'Yixin Tan' 'Yu Yang' 'Qingfeng Lan' 'Jianfeng Lu'\n 'A. Rupam Mahmood' 'Doina Precup' 'Pan Xu']"
] |
null | null | 2406.12242 | null | null | http://arxiv.org/abs/2406.12242v1 | 2024-06-18T03:33:03Z | 2024-06-18T03:33:03Z | GMP-AR: Granularity Message Passing and Adaptive Reconciliation for
Temporal Hierarchy Forecasting | Time series forecasts of different temporal granularity are widely used in real-world applications, e.g., sales prediction in days and weeks for making different inventory plans. However, these tasks are usually solved separately without ensuring coherence, which is crucial for aligning downstream decisions. Previous works mainly focus on ensuring coherence with some straightforward methods, e.g., aggregation from the forecasts of fine granularity to the coarse ones, and allocation from the coarse granularity to the fine ones. These methods merely take the temporal hierarchical structure to maintain coherence without improving the forecasting accuracy. In this paper, we propose a novel granularity message-passing mechanism (GMP) that leverages temporal hierarchy information to improve forecasting performance and also utilizes an adaptive reconciliation (AR) strategy to maintain coherence without performance loss. Furthermore, we introduce an optimization module to achieve task-based targets while adhering to more real-world constraints. Experiments on real-world datasets demonstrate that our framework (GMP-AR) achieves superior performances on temporal hierarchical forecasting tasks compared to state-of-the-art methods. In addition, our framework has been successfully applied to a real-world task of payment traffic management in Alipay by integrating with the task-based optimization module. | [
"['Fan Zhou' 'Chen Pan' 'Lintao Ma' 'Yu Liu' 'James Zhang' 'Jun Zhou'\n 'Hongyuan Mei' 'Weitao Lin' 'Zi Zhuang' 'Wenxin Ning' 'Yunhua Hu'\n 'Siqiao Xue']"
] |
null | null | 2406.12246 | null | null | http://arxiv.org/pdf/2406.12246v2 | 2024-06-19T21:40:03Z | 2024-06-18T03:42:00Z | TroL: Traversal of Layers for Large Language and Vision Models | Large language and vision models (LLVMs) have been driven by the generalization power of large language models (LLMs) and the advent of visual instruction tuning. Along with scaling them up directly, these models enable LLVMs to showcase powerful vision language (VL) performances by covering diverse tasks via natural language instructions. However, existing open-source LLVMs that perform comparably to closed-source LLVMs such as GPT-4V are often considered too large (e.g., 26B, 34B, and 110B parameters), having a larger number of layers. These large models demand costly, high-end resources for both training and inference. To address this issue, we present a new efficient LLVM family with 1.8B, 3.8B, and 7B LLM model sizes, Traversal of Layers (TroL), which enables the reuse of layers in a token-wise manner. This layer traversing technique simulates the effect of looking back and retracing the answering stream while increasing the number of forward propagation layers without physically adding more layers. We demonstrate that TroL employs a simple layer traversing approach yet efficiently outperforms the open-source LLVMs with larger model sizes and rivals the performances of the closed-source LLVMs with substantial sizes. | [
"['Byung-Kwan Lee' 'Sangyun Chung' 'Chae Won Kim' 'Beomchan Park'\n 'Yong Man Ro']"
] |
null | null | 2406.12251 | null | null | http://arxiv.org/pdf/2406.12251v1 | 2024-06-18T03:57:49Z | 2024-06-18T03:57:49Z | Mitigate Negative Transfer with Similarity Heuristic Lifelong Prompt
Tuning | Lifelong prompt tuning has significantly advanced parameter-efficient lifelong learning with its efficiency and minimal storage demands on various tasks. Our empirical studies, however, highlights certain transferability constraints in the current methodologies: a universal algorithm that guarantees consistent positive transfer across all tasks is currently unattainable, especially when dealing dissimilar tasks that may engender negative transfer. Identifying the misalignment between algorithm selection and task specificity as the primary cause of negative transfer, we present the Similarity Heuristic Lifelong Prompt Tuning (SHLPT) framework. This innovative strategy partitions tasks into two distinct subsets by harnessing a learnable similarity metric, thereby facilitating fruitful transfer from tasks regardless of their similarity or dissimilarity. Additionally, SHLPT incorporates a parameter pool to combat catastrophic forgetting effectively. Our experiments shows that SHLPT outperforms state-of-the-art techniques in lifelong learning benchmarks and demonstrates robustness against negative transfer in diverse task sequences. | [
"['Chenyuan Wu' 'Gangwei Jiang' 'Defu Lian']"
] |
null | null | 2406.12255 | null | null | http://arxiv.org/pdf/2406.12255v1 | 2024-06-18T04:07:13Z | 2024-06-18T04:07:13Z | A Hopfieldian View-based Interpretation for Chain-of-Thought Reasoning | Chain-of-Thought (CoT) holds a significant place in augmenting the reasoning performance for large language models (LLMs). While some studies focus on improving CoT accuracy through methods like retrieval enhancement, yet a rigorous explanation for why CoT achieves such success remains unclear. In this paper, we analyze CoT methods under two different settings by asking the following questions: (1) For zero-shot CoT, why does prompting the model with "let's think step by step" significantly impact its outputs? (2) For few-shot CoT, why does providing examples before questioning the model could substantially improve its reasoning ability? To answer these questions, we conduct a top-down explainable analysis from the Hopfieldian view and propose a Read-and-Control approach for controlling the accuracy of CoT. Through extensive experiments on seven datasets for three different tasks, we demonstrate that our framework can decipher the inner workings of CoT, provide reasoning error localization, and control to come up with the correct reasoning path. | [
"['Lijie Hu' 'Liang Liu' 'Shu Yang' 'Xin Chen' 'Hongru Xiao' 'Mengdi Li'\n 'Pan Zhou' 'Muhammad Asif Ali' 'Di Wang']"
] |
null | null | 2406.12260 | null | null | http://arxiv.org/pdf/2406.12260v1 | 2024-06-18T04:25:56Z | 2024-06-18T04:25:56Z | Self-Supervised Time-Series Anomaly Detection Using Learnable Data
Augmentation | Continuous efforts are being made to advance anomaly detection in various manufacturing processes to increase the productivity and safety of industrial sites. Deep learning replaced rule-based methods and recently emerged as a promising method for anomaly detection in diverse industries. However, in the real world, the scarcity of abnormal data and difficulties in obtaining labeled data create limitations in the training of detection models. In this study, we addressed these shortcomings by proposing a learnable data augmentation-based time-series anomaly detection (LATAD) technique that is trained in a self-supervised manner. LATAD extracts discriminative features from time-series data through contrastive learning. At the same time, learnable data augmentation produces challenging negative samples to enhance learning efficiency. We measured anomaly scores of the proposed technique based on latent feature similarities. As per the results, LATAD exhibited comparable or improved performance to the state-of-the-art anomaly detection assessments on several benchmark datasets and provided a gradient-based diagnosis technique to help identify root causes. | [
"['Kukjin Choi' 'Jihun Yi' 'Jisoo Mok' 'Sungroh Yoon']"
] |
null | null | 2406.12262 | null | null | http://arxiv.org/pdf/2406.12262v1 | 2024-06-18T04:35:35Z | 2024-06-18T04:35:35Z | Investigating Data Usage for Inductive Conformal Predictors | Inductive conformal predictors (ICPs) are algorithms that are able to generate prediction sets, instead of point predictions, which are valid at a user-defined confidence level, only assuming exchangeability. These algorithms are useful for reliable machine learning and are increasing in popularity. The ICP development process involves dividing development data into three parts: training, calibration and test. With access to limited or expensive development data, it is an open question regarding the most efficient way to divide the data. This study provides several experiments to explore this question and consider the case for allowing overlap of examples between training and calibration sets. Conclusions are drawn that will be of value to academics and practitioners planning to use ICPs. | [
"['Yizirui Fang' 'Anthony Bellotti']"
] |
null | null | 2406.12264 | null | null | http://arxiv.org/pdf/2406.12264v1 | 2024-06-18T04:44:05Z | 2024-06-18T04:44:05Z | Projection Methods for Operator Learning and Universal Approximation | We obtain a new universal approximation theorem for continuous operators on arbitrary Banach spaces using the Leray-Schauder mapping. Moreover, we introduce and study a method for operator learning in Banach spaces $L^p$ of functions with multiple variables, based on orthogonal projections on polynomial bases. We derive a universal approximation result for operators where we learn a linear projection and a finite dimensional mapping under some additional assumptions. For the case of $p=2$, we give some sufficient conditions for the approximation results to hold. This article serves as the theoretical framework for a deep learning methodology whose implementation will be provided in subsequent work. | [
"['Emanuele Zappala']"
] |
null | null | 2406.12282 | null | null | http://arxiv.org/pdf/2406.12282v1 | 2024-06-18T05:19:51Z | 2024-06-18T05:19:51Z | SAGDFN: A Scalable Adaptive Graph Diffusion Forecasting Network for
Multivariate Time Series Forecasting | Time series forecasting is essential for our daily activities and precise modeling of the complex correlations and shared patterns among multiple time series is essential for improving forecasting performance. Spatial-Temporal Graph Neural Networks (STGNNs) are widely used in multivariate time series forecasting tasks and have achieved promising performance on multiple real-world datasets for their ability to model the underlying complex spatial and temporal dependencies. However, existing studies have mainly focused on datasets comprising only a few hundred sensors due to the heavy computational cost and memory cost of spatial-temporal GNNs. When applied to larger datasets, these methods fail to capture the underlying complex spatial dependencies and exhibit limited scalability and performance. To this end, we present a Scalable Adaptive Graph Diffusion Forecasting Network (SAGDFN) to capture complex spatial-temporal correlation for large-scale multivariate time series and thereby, leading to exceptional performance in multivariate time series forecasting tasks. The proposed SAGDFN is scalable to datasets of thousands of nodes without the need of prior knowledge of spatial correlation. Extensive experiments demonstrate that SAGDFN achieves comparable performance with state-of-the-art baselines on one real-world dataset of 207 nodes and outperforms all state-of-the-art baselines by a significant margin on three real-world datasets of 2000 nodes. | [
"['Yue Jiang' 'Xiucheng Li' 'Yile Chen' 'Shuai Liu' 'Weilong Kong'\n 'Antonis F. Lentzakis' 'Gao Cong']"
] |
null | null | 2406.12284 | null | null | http://arxiv.org/pdf/2406.12284v1 | 2024-06-18T05:23:29Z | 2024-06-18T05:23:29Z | Demystifying the Recency Heuristic in Temporal-Difference Learning | The recency heuristic in reinforcement learning is the assumption that stimuli that occurred closer in time to an acquired reward should be more heavily reinforced. The recency heuristic is one of the key assumptions made by TD($lambda$), which reinforces recent experiences according to an exponentially decaying weighting. In fact, all other widely used return estimators for TD learning, such as $n$-step returns, satisfy a weaker (i.e., non-monotonic) recency heuristic. Why is the recency heuristic effective for temporal credit assignment? What happens when credit is assigned in a way that violates this heuristic? In this paper, we analyze the specific mathematical implications of adopting the recency heuristic in TD learning. We prove that any return estimator satisfying this heuristic: 1) is guaranteed to converge to the correct value function, 2) has a relatively fast contraction rate, and 3) has a long window of effective credit assignment, yet bounded worst-case variance. We also give a counterexample where on-policy, tabular TD methods violating the recency heuristic diverge. Our results offer some of the first theoretical evidence that credit assignment based on the recency heuristic facilitates learning. | [
"['Brett Daley' 'Marlos C. Machado' 'Martha White']"
] |
null | null | 2406.12286 | null | null | http://arxiv.org/pdf/2406.12286v1 | 2024-06-18T05:30:26Z | 2024-06-18T05:30:26Z | VIRL: Volume-Informed Representation Learning towards Few-shot
Manufacturability Estimation | Designing for manufacturing poses significant challenges in part due to the computation bottleneck of Computer-Aided Manufacturing (CAM) simulations. Although deep learning as an alternative offers fast inference, its performance is dependently bounded by the need for abundant training data. Representation learning, particularly through pre-training, offers promise for few-shot learning, aiding in manufacturability tasks where data can be limited. This work introduces VIRL, a Volume-Informed Representation Learning approach to pre-train a 3D geometric encoder. The pretrained model is evaluated across four manufacturability indicators obtained from CAM simulations: subtractive machining (SM) time, additive manufacturing (AM) time, residual von Mises stress, and blade collisions during Laser Power Bed Fusion process. Across all case studies, the model pre-trained by VIRL shows substantial enhancements on demonstrating improved generalizability with limited data and superior performance with larger datasets. Regarding deployment strategy, case-specific phenomenon exists where finetuning VIRL-pretrained models adversely affects AM tasks with limited data but benefits SM time prediction. Moreover, the efficacy of Low-rank adaptation (LoRA), which balances between probing and finetuning, is explored. LoRA shows stable performance akin to probing with limited data, while achieving a higher upper bound than probing as data size increases, without the computational costs of finetuning. Furthermore, static normalization of manufacturing indicators consistently performs well across tasks, while dynamic normalization enhances performance when a reliable task dependent input is available. | [
"['Yu-hsuan Chen' 'Jonathan Cagan' 'Levent Burak kara']"
] |
null | null | 2406.12289 | null | null | http://arxiv.org/pdf/2406.12289v1 | 2024-06-18T05:49:54Z | 2024-06-18T05:49:54Z | Stability of Data-Dependent Ridge-Regularization for Inverse Problems | Theoretical guarantees for the robust solution of inverse problems have important implications for applications. To achieve both guarantees and high reconstruction quality, we propose to learn a pixel-based ridge regularizer with a data-dependent and spatially-varying regularization strength. For this architecture, we establish the existence of solutions to the associated variational problem and the stability of its solution operator. Further, we prove that the reconstruction forms a maximum-a-posteriori approach. Simulations for biomedical imaging and material sciences demonstrate that the approach yields high-quality reconstructions even if only a small instance-specific training set is available. | [
"['Sebastian Neumayer' 'Fabian Altekrüger']"
] |
null | null | 2406.12297 | null | null | http://arxiv.org/pdf/2406.12297v1 | 2024-06-18T06:05:45Z | 2024-06-18T06:05:45Z | Faithful Density-Peaks Clustering via Matrix Computations on MPI
Parallelization System | Density peaks clustering (DP) has the ability of detecting clusters of arbitrary shape and clustering non-Euclidean space data, but its quadratic complexity in both computing and storage makes it difficult to scale for big data. Various approaches have been proposed in this regard, including MapReduce based distribution computing, multi-core parallelism, presentation transformation (e.g., kd-tree, Z-value), granular computing, and so forth. However, most of these existing methods face two limitations. One is their target datasets are mostly constrained to be in Euclidian space, the other is they emphasize only on local neighbors while ignoring global data distribution due to restriction to cut-off kernel when computing density. To address the two issues, we present a faithful and parallel DP method that makes use of two types of vector-like distance matrices and an inverse leading-node-finding policy. The method is implemented on a message passing interface (MPI) system. Extensive experiments showed that our method is capable of clustering non-Euclidean data such as in community detection, while outperforming the state-of-the-art counterpart methods in accuracy when clustering large Euclidean data. Our code is publicly available at https://github.com/alanxuji/FaithPDP. | [
"['Ji Xu' 'Tianlong Xiao' 'Jinye Yang' 'Panpan Zhu']"
] |
null | null | 2406.12311 | null | null | http://arxiv.org/pdf/2406.12311v1 | 2024-06-18T06:32:23Z | 2024-06-18T06:32:23Z | Mixture of Scales: Memory-Efficient Token-Adaptive Binarization for
Large Language Models | Binarization, which converts weight parameters to binary values, has emerged as an effective strategy to reduce the size of large language models (LLMs). However, typical binarization techniques significantly diminish linguistic effectiveness of LLMs. To address this issue, we introduce a novel binarization technique called Mixture of Scales (BinaryMoS). Unlike conventional methods, BinaryMoS employs multiple scaling experts for binary weights, dynamically merging these experts for each token to adaptively generate scaling factors. This token-adaptive approach boosts the representational power of binarized LLMs by enabling contextual adjustments to the values of binary weights. Moreover, because this adaptive process only involves the scaling factors rather than the entire weight matrix, BinaryMoS maintains compression efficiency similar to traditional static binarization methods. Our experimental results reveal that BinaryMoS surpasses conventional binarization techniques in various natural language processing tasks and even outperforms 2-bit quantization methods, all while maintaining similar model size to static binarization techniques. | [
"['Dongwon Jo' 'Taesu Kim' 'Yulhwa Kim' 'Jae-Joon Kim']"
] |
null | null | 2406.12330 | null | null | http://arxiv.org/pdf/2406.12330v1 | 2024-06-18T06:54:15Z | 2024-06-18T06:54:15Z | Security and Privacy of 6G Federated Learning-enabled Dynamic Spectrum
Sharing | Spectrum sharing is increasingly vital in 6G wireless communication, facilitating dynamic access to unused spectrum holes. Recently, there has been a significant shift towards employing machine learning (ML) techniques for sensing spectrum holes. In this context, federated learning (FL)-enabled spectrum sensing technology has garnered wide attention, allowing for the construction of an aggregated ML model without disclosing the private spectrum sensing information of wireless user devices. However, the integrity of collaborative training and the privacy of spectrum information from local users have remained largely unexplored. This article first examines the latest developments in FL-enabled spectrum sharing for prospective 6G scenarios. It then identifies practical attack vectors in 6G to illustrate potential AI-powered security and privacy threats in these contexts. Finally, the study outlines future directions, including practical defense challenges and guidelines. | [
"['Viet Vo' 'Thusitha Dayaratne' 'Blake Haydon' 'Xingliang Yuan'\n 'Shangqi Lai' 'Sharif Abuadbba' 'Hajime Suzuki' 'Carsten Rudolph']"
] |
null | null | 2406.12334 | null | null | http://arxiv.org/pdf/2406.12334v1 | 2024-06-18T06:59:24Z | 2024-06-18T06:59:24Z | What Did I Do Wrong? Quantifying LLMs' Sensitivity and Consistency to
Prompt Engineering | Large Language Models (LLMs) changed the way we design and interact with software systems. Their ability to process and extract information from text has drastically improved productivity in a number of routine tasks. Developers that want to include these models in their software stack, however, face a dreadful challenge: debugging their inconsistent behavior across minor variations of the prompt. We therefore introduce two metrics for classification tasks, namely sensitivity and consistency, which are complementary to task performance. First, sensitivity measures changes of predictions across rephrasings of the prompt, and does not require access to ground truth labels. Instead, consistency measures how predictions vary across rephrasings for elements of the same class. We perform an empirical comparison of these metrics on text classification tasks, using them as guideline for understanding failure modes of the LLM. Our hope is that sensitivity and consistency will be powerful allies in automatic prompt engineering frameworks to obtain LLMs that balance robustness with performance. | [
"['Federico Errica' 'Giuseppe Siracusano' 'Davide Sanvito'\n 'Roberto Bifulco']"
] |
null | null | 2406.12335 | null | null | http://arxiv.org/pdf/2406.12335v1 | 2024-06-18T07:01:11Z | 2024-06-18T07:01:11Z | Attention Score is not All You Need for Token Importance Indicator in KV
Cache Reduction: Value Also Matters | Scaling the context size of large language models (LLMs) enables them to perform various new tasks, e.g., book summarization. However, the memory cost of the Key and Value (KV) cache in attention significantly limits the practical applications of LLMs. Recent works have explored token pruning for KV cache reduction in LLMs, relying solely on attention scores as a token importance indicator. However, our investigation into value vector norms revealed a notably non-uniform pattern questioning their reliance only on attention scores. Inspired by this, we propose a new method: Value-Aware Token Pruning (VATP) which uses both attention scores and the $ ell_{1} $ norm of value vectors to evaluate token importance. Extensive experiments on LLaMA2-7B-chat and Vicuna-v1.5-7B across 16 LongBench tasks demonstrate VATP's superior performance. | [
"['Zhiyu Guo' 'Hidetaka Kamigaito' 'Taro Watanabe']"
] |
null | null | 2406.12336 | null | null | http://arxiv.org/pdf/2406.12336v1 | 2024-06-18T07:03:34Z | 2024-06-18T07:03:34Z | A Compass for Navigating the World of Sentence Embeddings for the
Telecom Domain | A plethora of sentence embedding models makes it challenging to choose one, especially for domains such as telecom, rich with specialized vocabulary. We evaluate multiple embeddings obtained from publicly available models and their domain-adapted variants, on both point retrieval accuracies as well as their (95%) confidence intervals. We establish a systematic method to obtain thresholds for similarity scores for different embeddings. We observe that fine-tuning improves mean bootstrapped accuracies as well as tightens confidence intervals. The pre-training combined with fine-tuning makes confidence intervals even tighter. To understand these variations, we analyse and report significant correlations between the distributional overlap between top-$K$, correct and random sentence similarities with retrieval accuracies and similarity thresholds. Following current literature, we analyze if retrieval accuracy variations can be attributed to isotropy of embeddings. Our conclusions are that isotropy of embeddings (as measured by two independent state-of-the-art isotropy metric definitions) cannot be attributed to better retrieval performance. However, domain adaptation which improves retrieval accuracies also improves isotropy. We establish that domain adaptation moves domain specific embeddings further away from general domain embeddings. | [
"['Sujoy Roychowdhury' 'Sumit Soman' 'H. G. Ranjani' 'Vansh Chhabra'\n 'Neeraj Gunda' 'Subhadip Bandyopadhyay' 'Sai Krishna Bala']"
] |
null | null | 2406.12338 | null | null | http://arxiv.org/pdf/2406.12338v1 | 2024-06-18T07:05:31Z | 2024-06-18T07:05:31Z | PARAFAC2-based Coupled Matrix and Tensor Factorizations with Constraints | Data fusion models based on Coupled Matrix and Tensor Factorizations (CMTF) have been effective tools for joint analysis of data from multiple sources. While the vast majority of CMTF models are based on the strictly multilinear CANDECOMP/PARAFAC (CP) tensor model, recently also the more flexible PARAFAC2 model has been integrated into CMTF models. PARAFAC2 tensor models can handle irregular/ragged tensors and have shown to be especially useful for modelling dynamic data with unaligned or irregular time profiles. However, existing PARAFAC2-based CMTF models have limitations in terms of possible regularizations on the factors and/or types of coupling between datasets. To address these limitations, in this paper we introduce a flexible algorithmic framework that fits PARAFAC2-based CMTF models using Alternating Optimization (AO) and the Alternating Direction Method of Multipliers (ADMM). The proposed framework allows to impose various constraints on all modes and linear couplings to other matrix-, CP- or PARAFAC2-models. Experiments on various simulated and a real dataset demonstrate the utility and versatility of the proposed framework as well as its benefits in terms of accuracy and efficiency in comparison with state-of-the-art methods. | [
"['Carla Schenker' 'Xiulin Wang' 'David Horner' 'Morten A. Rasmussen'\n 'Evrim Acar']"
] |
null | null | 2406.12349 | null | null | http://arxiv.org/pdf/2406.12349v1 | 2024-06-18T07:33:05Z | 2024-06-18T07:33:05Z | Effective Generation of Feasible Solutions for Integer Programming via
Guided Diffusion | Feasible solutions are crucial for Integer Programming (IP) since they can substantially speed up the solving process. In many applications, similar IP instances often exhibit similar structures and shared solution distributions, which can be potentially modeled by deep learning methods. Unfortunately, existing deep-learning-based algorithms, such as Neural Diving and Predict-and-search framework, are limited to generating only partial feasible solutions, and they must rely on solvers like SCIP and Gurobi to complete the solutions for a given IP problem. In this paper, we propose a novel framework that generates complete feasible solutions end-to-end. Our framework leverages contrastive learning to characterize the relationship between IP instances and solutions, and learns latent embeddings for both IP instances and their solutions. Further, the framework employs diffusion models to learn the distribution of solution embeddings conditioned on IP representations, with a dedicated guided sampling strategy that accounts for both constraints and objectives. We empirically evaluate our framework on four typical datasets of IP problems, and show that it effectively generates complete feasible solutions with a high probability (> 89.7 %) without the reliance of Solvers and the quality of solutions is comparable to the best heuristic solutions from Gurobi. Furthermore, by integrating our method's sampled partial solutions with the CompleteSol heuristic from SCIP, the resulting feasible solutions outperform those from state-of-the-art methods across all datasets, exhibiting a 3.7 to 33.7% improvement in the gap to optimal values, and maintaining a feasible ratio of over 99.7% for all datasets. | [
"['Hao Zeng' 'Jiaqi Wang' 'Avirup Das' 'Junying He' 'Kunpeng Han'\n 'Haoyuan Hu' 'Mingfei Sun']"
] |
null | null | 2406.12353 | null | null | http://arxiv.org/pdf/2406.12353v1 | 2024-06-18T07:36:45Z | 2024-06-18T07:36:45Z | Top-Down Bayesian Posterior Sampling for Sum-Product Networks | Sum-product networks (SPNs) are probabilistic models characterized by exact and fast evaluation of fundamental probabilistic operations. Its superior computational tractability has led to applications in many fields, such as machine learning with time constraints or accuracy requirements and real-time systems. The structural constraints of SPNs supporting fast inference, however, lead to increased learning-time complexity and can be an obstacle to building highly expressive SPNs. This study aimed to develop a Bayesian learning approach that can be efficiently implemented on large-scale SPNs. We derived a new full conditional probability of Gibbs sampling by marginalizing multiple random variables to expeditiously obtain the posterior distribution. The complexity analysis revealed that our sampling algorithm works efficiently even for the largest possible SPN. Furthermore, we proposed a hyperparameter tuning method that balances the diversity of the prior distribution and optimization efficiency in large-scale SPNs. Our method has improved learning-time complexity and demonstrated computational speed tens to more than one hundred times faster and superior predictive performance in numerical experiments on more than 20 datasets. | [
"['Soma Yokoi' 'Issei Sato']"
] |
null | null | 2406.12359 | null | null | http://arxiv.org/pdf/2406.12359v1 | 2024-06-18T07:41:40Z | 2024-06-18T07:41:40Z | Memory Sequence Length of Data Sampling Impacts the Adaptation of
Meta-Reinforcement Learning Agents | Fast adaptation to new tasks is extremely important for embodied agents in the real world. Meta-reinforcement learning (meta-RL) has emerged as an effective method to enable fast adaptation in unknown environments. Compared to on-policy meta-RL algorithms, off-policy algorithms rely heavily on efficient data sampling strategies to extract and represent the historical trajectories. However, little is known about how different data sampling methods impact the ability of meta-RL agents to represent unknown environments. Here, we investigate the impact of data sampling strategies on the exploration and adaptability of meta-RL agents. Specifically, we conducted experiments with two types of off-policy meta-RL algorithms based on Thompson sampling and Bayes-optimality theories in continuous control tasks within the MuJoCo environment and sparse reward navigation tasks. Our analysis revealed the long-memory and short-memory sequence sampling strategies affect the representation and adaptive capabilities of meta-RL agents. We found that the algorithm based on Bayes-optimality theory exhibited more robust and better adaptability than the algorithm based on Thompson sampling, highlighting the importance of appropriate data sampling strategies for the agent's representation of an unknown environment, especially in the case of sparse rewards. | [
"['Menglong Zhang' 'Fuyuan Qian' 'Quanying Liu']"
] |
null | null | 2406.12360 | null | null | http://arxiv.org/pdf/2406.12360v1 | 2024-06-18T07:41:42Z | 2024-06-18T07:41:42Z | UrbanLLM: Autonomous Urban Activity Planning and Management with Large
Language Models | Location-based services play an critical role in improving the quality of our daily lives. Despite the proliferation of numerous specialized AI models within spatio-temporal context of location-based services, these models struggle to autonomously tackle problems regarding complex urban planing and management. To bridge this gap, we introduce UrbanLLM, a fine-tuned large language model (LLM) designed to tackle diverse problems in urban scenarios. UrbanLLM functions as a problem-solver by decomposing urban-related queries into manageable sub-tasks, identifying suitable spatio-temporal AI models for each sub-task, and generating comprehensive responses to the given queries. Our experimental results indicate that UrbanLLM significantly outperforms other established LLMs, such as Llama and the GPT series, in handling problems concerning complex urban activity planning and management. UrbanLLM exhibits considerable potential in enhancing the effectiveness of solving problems in urban scenarios, reducing the workload and reliance for human experts. | [
"['Yue Jiang' 'Qin Chao' 'Yile Chen' 'Xiucheng Li' 'Shuai Liu' 'Gao Cong']"
] |
null | null | 2406.12366 | null | null | http://arxiv.org/pdf/2406.12366v1 | 2024-06-18T07:45:02Z | 2024-06-18T07:45:02Z | Structured Prediction in Online Learning | We study a theoretical and algorithmic framework for structured prediction in the online learning setting. The problem of structured prediction, i.e. estimating function where the output space lacks a vectorial structure, is well studied in the literature of supervised statistical learning. We show that our algorithm is a generalisation of optimal algorithms from the supervised learning setting, and achieves the same excess risk upper bound also when data are not i.i.d. Moreover, we consider a second algorithm designed especially for non-stationary data distributions, including adversarial data. We bound its stochastic regret in function of the variation of the data distributions. | [
"['Pierre Boudart' 'Alessandro Rudi' 'Pierre Gaillard']"
] |
null | null | 2406.12367 | null | null | http://arxiv.org/pdf/2406.12367v1 | 2024-06-18T07:45:57Z | 2024-06-18T07:45:57Z | Competitive Learning for Achieving Content-specific Filters in Video
Coding for Machines | This paper investigates the efficacy of jointly optimizing content-specific post-processing filters to adapt a human oriented video/image codec into a codec suitable for machine vision tasks. By observing that artifacts produced by video/image codecs are content-dependent, we propose a novel training strategy based on competitive learning principles. This strategy assigns training samples to filters dynamically, in a fuzzy manner, which further optimizes the winning filter on the given sample. Inspired by simulated annealing optimization techniques, we employ a softmax function with a temperature variable as the weight allocation function to mitigate the effects of random initialization. Our evaluation, conducted on a system utilizing multiple post-processing filters within a Versatile Video Coding (VVC) codec framework, demonstrates the superiority of content-specific filters trained with our proposed strategies, specifically, when images are processed in blocks. Using VVC reference software VTM 12.0 as the anchor, experiments on the OpenImages dataset show an improvement in the BD-rate reduction from -41.3% and -44.6% to -42.3% and -44.7% for object detection and instance segmentation tasks, respectively, compared to independently trained filters. The statistics of the filter usage align with our hypothesis and underscore the importance of jointly optimizing filters for both content and reconstruction quality. Our findings pave the way for further improving the performance of video/image codecs. | [
"['Honglei Zhang' 'Jukka I. Ahonen' 'Nam Le' 'Ruiying Yang'\n 'Francesco Cricri']"
] |
null | null | 2406.12373 | null | null | http://arxiv.org/pdf/2406.12373v2 | 2024-06-27T16:56:13Z | 2024-06-18T07:58:33Z | WebCanvas: Benchmarking Web Agents in Online Environments | For web agents to be practically useful, they must adapt to the continuously evolving web environment characterized by frequent updates to user interfaces and content. However, most existing benchmarks only capture the static aspects of the web. To bridge this gap, we introduce WebCanvas, an innovative online evaluation framework for web agents that effectively addresses the dynamic nature of web interactions. WebCanvas contains three main components to facilitate realistic assessments: (1) A novel evaluation metric which reliably capture critical intermediate actions or states necessary for task completions while disregarding noise caused by insignificant events or changed web-elements. (2) A benchmark dataset called Mind2Web-Live, a refined version of original Mind2Web static dataset containing 542 tasks with 2439 intermediate evaluation states; (3) Lightweight and generalizable annotation tools and testing pipelines that enables the community to collect and maintain the high-quality, up-to-date dataset. Building on WebCanvas, we open-source an agent framework with extensible modules for reasoning, providing a foundation for the community to conduct online inference and evaluations. Our best-performing agent achieves a task success rate of 23.1% and a task completion rate of 48.8% on the Mind2Web-Live test set. Additionally, we analyze the performance discrepancies across various websites, domains, and experimental environments. We encourage the community to contribute further insights on online agent evaluation, thereby advancing this field of research. | [
"['Yichen Pan' 'Dehan Kong' 'Sida Zhou' 'Cheng Cui' 'Yifei Leng'\n 'Bing Jiang' 'Hangyu Liu' 'Yanyi Shang' 'Shuyan Zhou' 'Tongshuang Wu'\n 'Zhengyang Wu']"
] |
null | null | 2406.12375 | null | null | http://arxiv.org/pdf/2406.12375v1 | 2024-06-18T08:03:51Z | 2024-06-18T08:03:51Z | GW-MoE: Resolving Uncertainty in MoE Router with Global Workspace Theory | Mixture-of-Experts (MoE) has been demonstrated as an efficient method to scale up models. By dynamically and sparsely selecting activated experts, MoE can effectively reduce computational costs. Despite the success, we observe that many tokens in the MoE models have uncertain routing results. These tokens have nearly equal scores for choosing each expert, and we demonstrate that this uncertainty can lead to incorrect selections. Inspired by the Global Workspace Theory (GWT), we propose a new fine-tuning method, GW-MoE, to address this issue. The core idea is to broadcast the uncertain tokens across experts during fine-tuning. Therefore, these tokens can acquire the necessary knowledge from any expert during inference and become less sensitive to the choice. GW-MoE does not introduce additional inference overhead. We validate that GW can mitigate the uncertain problem and consistently improve in different tasks (text classification, question answering, summarization, code generation, and mathematical problem solving) and model sizes (650M and 8B parameters). | [
"['Haoze Wu' 'Zihan Qiu' 'Zili Wang' 'Hang Zhao' 'Jie Fu']"
] |
null | null | 2406.12378 | null | null | http://arxiv.org/pdf/2406.12378v1 | 2024-06-18T08:05:04Z | 2024-06-18T08:05:04Z | Efficient mapping of phase diagrams with conditional normalizing flows | The accurate prediction of phase diagrams is of central importance for both the fundamental understanding of materials as well as for technological applications in material sciences. However, the computational prediction of the relative stability between phases based on their free energy is a daunting task, as traditional free energy estimators require a large amount of simulation data to obtain uncorrelated equilibrium samples over a grid of thermodynamic states. In this work, we develop deep generative machine learning models for entire phase diagrams, employing normalizing flows conditioned on the thermodynamic states, e.g., temperature and pressure, that they map to. By training a single normalizing flow to transform the equilibrium distribution sampled at only one reference thermodynamic state to a wide range of target temperatures and pressures, we can efficiently generate equilibrium samples across the entire phase diagram. Using a permutation-equivariant architecture allows us, thereby, to treat solid and liquid phases on the same footing. We demonstrate our approach by predicting the solid-liquid coexistence line for a Lennard-Jones system in excellent agreement with state-of-the-art free energy methods while significantly reducing the number of energy evaluations needed. | [
"['Maximilian Schebek' 'Michele Invernizzi' 'Frank Noé' 'Jutta Rogal']"
] |
null | null | 2406.12381 | null | null | http://arxiv.org/pdf/2406.12381v2 | 2024-07-02T09:21:03Z | 2024-06-18T08:09:58Z | QOG:Question and Options Generation based on Language Model | Question-Options Generation (QOG) is a task that involves generating a set of question-options pairs given context. This task has various applications, including fine-tuning large models, information retrieval, and automated multiple-choice question generation for education. In this paper, we develop QOG models using three different methods based on fine-tuning sequence-to-sequence language models (LMs). Experiments demonstrate that the end-to-end QOG model is computationally efficient and stable during both training and inference, outperforming other methods. Furthermore, our analysis indicates that our QOG models are competitive on the QOG task compared to the large language model Llama 3-8B. | [
"['Jincheng Zhou']"
] |
null | null | 2406.12406 | null | null | http://arxiv.org/pdf/2406.12406v1 | 2024-06-18T08:54:04Z | 2024-06-18T08:54:04Z | Fast Rates for Bandit PAC Multiclass Classification | We study multiclass PAC learning with bandit feedback, where inputs are classified into one of $K$ possible labels and feedback is limited to whether or not the predicted labels are correct. Our main contribution is in designing a novel learning algorithm for the agnostic $(varepsilon,delta)$-PAC version of the problem, with sample complexity of $Obig( (operatorname{poly}(K) + 1 / varepsilon^2) log (|H| / delta) big)$ for any finite hypothesis class $H$. In terms of the leading dependence on $varepsilon$, this improves upon existing bounds for the problem, that are of the form $O(K/varepsilon^2)$. We also provide an extension of this result to general classes and establish similar sample complexity bounds in which $log |H|$ is replaced by the Natarajan dimension. This matches the optimal rate in the full-information version of the problem and resolves an open question studied by Daniely, Sabato, Ben-David, and Shalev-Shwartz (2011) who demonstrated that the multiplicative price of bandit feedback in realizable PAC learning is $Theta(K)$. We complement this by revealing a stark contrast with the agnostic case, where the price of bandit feedback is only $O(1)$ as $varepsilon to 0$. Our algorithm utilizes a stochastic optimization technique to minimize a log-barrier potential based on Frank-Wolfe updates for computing a low-variance exploration distribution over the hypotheses, and is made computationally efficient provided access to an ERM oracle over $H$. | [
"['Liad Erez' 'Alon Cohen' 'Tomer Koren' 'Yishay Mansour' 'Shay Moran']"
] |
null | null | 2406.12409 | null | null | http://arxiv.org/pdf/2406.12409v1 | 2024-06-18T08:58:59Z | 2024-06-18T08:58:59Z | Translation Equivariant Transformer Neural Processes | The effectiveness of neural processes (NPs) in modelling posterior prediction maps -- the mapping from data to posterior predictive distributions -- has significantly improved since their inception. This improvement can be attributed to two principal factors: (1) advancements in the architecture of permutation invariant set functions, which are intrinsic to all NPs; and (2) leveraging symmetries present in the true posterior predictive map, which are problem dependent. Transformers are a notable development in permutation invariant set functions, and their utility within NPs has been demonstrated through the family of models we refer to as TNPs. Despite significant interest in TNPs, little attention has been given to incorporating symmetries. Notably, the posterior prediction maps for data that are stationary -- a common assumption in spatio-temporal modelling -- exhibit translation equivariance. In this paper, we introduce of a new family of translation equivariant TNPs that incorporate translation equivariance. Through an extensive range of experiments on synthetic and real-world spatio-temporal data, we demonstrate the effectiveness of TE-TNPs relative to their non-translation-equivariant counterparts and other NP baselines. | [
"['Matthew Ashman' 'Cristiana Diaconu' 'Junhyuck Kim' 'Lakee Sivaraya'\n 'Stratis Markou' 'James Requeima' 'Wessel P. Bruinsma'\n 'Richard E. Turner']"
] |
null | null | 2406.12411 | null | null | http://arxiv.org/pdf/2406.12411v1 | 2024-06-18T09:00:49Z | 2024-06-18T09:00:49Z | TADM: Temporally-Aware Diffusion Model for Neurodegenerative Progression
on Brain MRI | Generating realistic images to accurately predict changes in the structure of brain MRI is a crucial tool for clinicians. Such applications help assess patients' outcomes and analyze how diseases progress at the individual level. However, existing methods for this task present some limitations. Some approaches attempt to model the distribution of MRI scans directly by conditioning the model on patients' ages, but they fail to explicitly capture the relationship between structural changes in the brain and time intervals, especially on age-unbalanced datasets. Other approaches simply rely on interpolation between scans, which limits their clinical application as they do not predict future MRIs. To address these challenges, we propose a Temporally-Aware Diffusion Model (TADM), which introduces a novel approach to accurately infer progression in brain MRIs. TADM learns the distribution of structural changes in terms of intensity differences between scans and combines the prediction of these changes with the initial baseline scans to generate future MRIs. Furthermore, during training, we propose to leverage a pre-trained Brain-Age Estimator (BAE) to refine the model's training process, enhancing its ability to produce accurate MRIs that match the expected age gap between baseline and generated scans. Our assessment, conducted on the OASIS-3 dataset, uses similarity metrics and region sizes computed by comparing predicted and real follow-up scans on 3 relevant brain regions. TADM achieves large improvements over existing approaches, with an average decrease of 24% in region size error and an improvement of 4% in similarity metrics. These evaluations demonstrate the improvement of our model in mimicking temporal brain neurodegenerative progression compared to existing methods. Our approach will benefit applications, such as predicting patient outcomes or improving treatments for patients. | [
"['Mattia Litrico' 'Francesco Guarnera' 'Valerio Giuffirda' 'Daniele Ravì'\n 'Sebastiano Battiato']"
] |
null | null | 2406.12420 | null | null | http://arxiv.org/pdf/2406.12420v1 | 2024-06-18T09:14:17Z | 2024-06-18T09:14:17Z | MMUTF: Multimodal Multimedia Event Argument Extraction with Unified
Template Filling | With the advancement of multimedia technologies, news documents and user-generated content are often represented as multiple modalities, making Multimedia Event Extraction (MEE) an increasingly important challenge. However, recent MEE methods employ weak alignment strategies and data augmentation with simple classification models, which ignore the capabilities of natural language-formulated event templates for the challenging Event Argument Extraction (EAE) task. In this work, we focus on EAE and address this issue by introducing a unified template filling model that connects the textual and visual modalities via textual prompts. This approach enables the exploitation of cross-ontology transfer and the incorporation of event-specific semantics. Experiments on the M2E2 benchmark demonstrate the effectiveness of our approach. Our system surpasses the current SOTA on textual EAE by +7% F1, and performs generally better than the second-best systems for multimedia EAE. | [
"['Philipp Seeberger' 'Dominik Wagner' 'Korbinian Riedhammer']"
] |
null | null | 2406.12423 | null | null | http://arxiv.org/pdf/2406.12423v1 | 2024-06-18T09:16:11Z | 2024-06-18T09:16:11Z | Deep Temporal Deaggregation: Large-Scale Spatio-Temporal Generative
Models | Many of today's data is time-series data originating from various sources, such as sensors, transaction systems, or production systems. Major challenges with such data include privacy and business sensitivity. Generative time-series models have the potential to overcome these problems, allowing representative synthetic data, such as people's movement in cities, to be shared openly and be used to the benefit of society at large. However, contemporary approaches are limited to prohibitively short sequences and small scales. Aside from major memory limitations, the models generate less accurate and less representative samples the longer the sequences are. This issue is further exacerbated by the lack of a comprehensive and accessible benchmark. Furthermore, a common need in practical applications is what-if analysis and dynamic adaptation to data distribution changes, for usage in decision making and to manage a changing world: What if this road is temporarily blocked or another road is added? The focus of this paper is on mobility data, such as people's movement in cities, requiring all these issues to be addressed. To this end, we propose a transformer-based diffusion model, TDDPM, for time-series which outperforms and scales substantially better than state-of-the-art. This is evaluated in a new comprehensive benchmark across several sequence lengths, standard datasets, and evaluation measures. We also demonstrate how the model can be conditioned on a prior over spatial occupancy frequency information, allowing the model to generate mobility data for previously unseen environments and for hypothetical scenarios where the underlying road network and its usage changes. This is evaluated by training on mobility data from part of a city. Then, using only aggregate spatial information as prior, we demonstrate out-of-distribution generalization to the unobserved remainder of the city. | [
"['David Bergström' 'Mattias Tiger' 'Fredrik Heintz']"
] |
null | null | 2406.12428 | null | null | http://arxiv.org/pdf/2406.12428v1 | 2024-06-18T09:23:54Z | 2024-06-18T09:23:54Z | PSLM: Parallel Generation of Text and Speech with LLMs for Low-Latency
Spoken Dialogue Systems | Multimodal language models that process both text and speech have a potential for applications in spoken dialogue systems. However, current models face two major challenges in response generation latency: (1) generating a spoken response requires the prior generation of a written response, and (2) speech sequences are significantly longer than text sequences. This study addresses these issues by extending the input and output sequences of the language model to support the parallel generation of text and speech. Our experiments on spoken question answering tasks demonstrate that our approach improves latency while maintaining the quality of response content. Additionally, we show that latency can be further reduced by generating speech in multiple sequences. Demo samples are available at https://rinnakk.github.io/research/publications/PSLM. | [
"['Kentaro Mitsui' 'Koh Mitsuda' 'Toshiaki Wakatsuki' 'Yukiya Hono'\n 'Kei Sawada']"
] |
null | null | 2406.12430 | null | null | http://arxiv.org/pdf/2406.12430v1 | 2024-06-18T09:25:35Z | 2024-06-18T09:25:35Z | PlanRAG: A Plan-then-Retrieval Augmented Generation for Generative Large
Language Models as Decision Makers | In this paper, we conduct a study to utilize LLMs as a solution for decision making that requires complex data analysis. We define Decision QA as the task of answering the best decision, $d_{best}$, for a decision-making question $Q$, business rules $R$ and a database $D$. Since there is no benchmark that can examine Decision QA, we propose Decision QA benchmark, DQA. It has two scenarios, Locating and Building, constructed from two video games (Europa Universalis IV and Victoria 3) that have almost the same goal as Decision QA. To address Decision QA effectively, we also propose a new RAG technique called the iterative plan-then-retrieval augmented generation (PlanRAG). Our PlanRAG-based LM generates the plan for decision making as the first step, and the retriever generates the queries for data analysis as the second step. The proposed method outperforms the state-of-the-art iterative RAG method by 15.8% in the Locating scenario and by 7.4% in the Building scenario, respectively. We release our code and benchmark at https://github.com/myeon9h/PlanRAG. | [
"['Myeonghwa Lee' 'Seonho An' 'Min-Soo Kim']"
] |
null | null | 2406.12432 | null | null | http://arxiv.org/pdf/2406.12432v1 | 2024-06-18T09:28:23Z | 2024-06-18T09:28:23Z | Exploring Sensing Devices for Heart and Lung Sound Monitoring | This paper presents a comprehensive review of cardiorespiratory auscultation sensing devices which is useful for understanding the theoretical aspects of sensing devices, as well as practical notes to design novel sensing devices. One of the methods to design a stethoscope is using electret condenser microphones (ECM). In this paper, we first introduce the acoustic properties of the heart and lungs, as well as a brief history of stethoscope evolution. Then, we discuss the basic concept of ECM sensors and a recent stethoscope based on this technology. In response to the limitations of ECM-based systems, we explore the potential of microelectromechanical systems (MEMS), particularly focusing on piezoelectric transducer (PZT) sensors. This paper comprehensively reviews sensing technologies, emphasizing innovative MEMS-based designs for wearable cardiopulmonary auscultation in the past decade. To our knowledge, this is the first paper to summarize ECM and MEMS applications for heart and lung sound analysis. Keywords: Micro-electro-mechanical Systems (MEMS); Electret Condenser Microphone (ECM); Wearable Sensing Devices; Cardiorespiratory Auscultation; Phonocardiography (PCG); Heart Sound; Lung Sound | [
"['Yasaman Torabi' 'Shahram Shirani' 'James P. Reilly']"
] |
null | null | 2406.12434 | null | null | http://arxiv.org/pdf/2406.12434v2 | 2024-07-05T09:50:06Z | 2024-06-18T09:29:24Z | Towards Audio Codec-based Speech Separation | Recent improvements in neural audio codec (NAC) models have generated interest in adopting pre-trained codecs for a variety of speech processing applications to take advantage of the efficiencies gained from high compression, but these have yet been applied to the speech separation (SS) task. SS can benefit from high compression because the compute required for traditional SS models makes them impractical for many edge computing use cases. However, SS is a waveform-masking task where compression tends to introduce distortions that severely impact performance. Here we propose a novel task of Audio Codec-based SS, where SS is performed within the embedding space of a NAC, and propose a new model, Codecformer, to address this task. At inference, Codecformer achieves a 52x reduction in MAC while producing separation performance comparable to a cloud deployment of Sepformer. This method charts a new direction for performing efficient SS in practical scenarios. | [
"['Jia Qi Yip' 'Shengkui Zhao' 'Dianwen Ng' 'Eng Siong Chng' 'Bin Ma']"
] |
null | null | 2406.12435 | null | null | http://arxiv.org/pdf/2406.12435v1 | 2024-06-18T09:30:10Z | 2024-06-18T09:30:10Z | Federated Learning with Limited Node Labels | Subgraph federated learning (SFL) is a research methodology that has gained significant attention for its potential to handle distributed graph-structured data. In SFL, the local model comprises graph neural networks (GNNs) with a partial graph structure. However, some SFL models have overlooked the significance of missing cross-subgraph edges, which can lead to local GNNs being unable to message-pass global representations to other parties' GNNs. Moreover, existing SFL models require substantial labeled data, which limits their practical applications. To overcome these limitations, we present a novel SFL framework called FedMpa that aims to learn cross-subgraph node representations. FedMpa first trains a multilayer perceptron (MLP) model using a small amount of data and then propagates the federated feature to the local structures. To further improve the embedding representation of nodes with local subgraphs, we introduce the FedMpae method, which reconstructs the local graph structure with an innovation view that applies pooling operation to form super-nodes. Our extensive experiments on six graph datasets demonstrate that FedMpa is highly effective in node classification. Furthermore, our ablation experiments verify the effectiveness of FedMpa. | [
"['Bisheng Tang' 'Xiaojun Chen' 'Shaopu Wang' 'Yuexin Xuan' 'Zhendong Zhao']"
] |
null | null | 2406.12439 | null | null | http://arxiv.org/pdf/2406.12439v1 | 2024-06-18T09:41:40Z | 2024-06-18T09:41:40Z | A data-centric approach for assessing progress of Graph Neural Networks | Graph Neural Networks (GNNs) have achieved state-of-the-art results in node classification tasks. However, most improvements are in multi-class classification, with less focus on the cases where each node could have multiple labels. The first challenge in studying multi-label node classification is the scarcity of publicly available datasets. To address this, we collected and released three real-world biological datasets and developed a multi-label graph generator with tunable properties. We also argue that traditional notions of homophily and heterophily do not apply well to multi-label scenarios. Therefore, we define homophily and Cross-Class Neighborhood Similarity for multi-label classification and investigate $9$ collected multi-label datasets. Lastly, we conducted a large-scale comparative study with $8$ methods across nine datasets to evaluate current progress in multi-label node classification. We release our code at url{https://github.com/Tianqi-py/MLGNC}. | [
"['Tianqi Zhao' 'Ngan Thi Dong' 'Alan Hanjalic' 'Megha Khosla']"
] |
null | null | 2406.12452 | null | null | http://arxiv.org/pdf/2406.12452v1 | 2024-06-18T09:57:02Z | 2024-06-18T09:57:02Z | Insect Identification in the Wild: The AMI Dataset | Insects represent half of all global biodiversity, yet many of the world's insects are disappearing, with severe implications for ecosystems and agriculture. Despite this crisis, data on insect diversity and abundance remain woefully inadequate, due to the scarcity of human experts and the lack of scalable tools for monitoring. Ecologists have started to adopt camera traps to record and study insects, and have proposed computer vision algorithms as an answer for scalable data processing. However, insect monitoring in the wild poses unique challenges that have not yet been addressed within computer vision, including the combination of long-tailed data, extremely similar classes, and significant distribution shifts. We provide the first large-scale machine learning benchmarks for fine-grained insect recognition, designed to match real-world tasks faced by ecologists. Our contributions include a curated dataset of images from citizen science platforms and museums, and an expert-annotated dataset drawn from automated camera traps across multiple continents, designed to test out-of-distribution generalization under field conditions. We train and evaluate a variety of baseline algorithms and introduce a combination of data augmentation techniques that enhance generalization across geographies and hardware setups. Code and datasets are made publicly available. | [
"['Aditya Jain' 'Fagner Cunha' 'Michael James Bunsen'\n 'Juan Sebastián Cañas' 'Léonard Pasi' 'Nathan Pinoy' 'Flemming Helsing'\n 'JoAnne Russo' 'Marc Botham' 'Michael Sabourin' 'Jonathan Fréchette'\n 'Alexandre Anctil' 'Yacksecari Lopez' 'Eduardo Navarro'\n 'Filonila Perez Pimentel' 'Ana Cecilia Zamora'\n 'José Alejandro Ramirez Silva' 'Jonathan Gagnon' 'Tom August'\n 'Kim Bjerge' 'Alba Gomez Segura' 'Marc Bélisle' 'Yves Basset'\n 'Kent P. McFarland' 'David Roy' 'Toke Thomas Høye' 'Maxim Larrivée'\n 'David Rolnick']"
] |
null | null | 2406.12475 | null | null | http://arxiv.org/pdf/2406.12475v2 | 2024-06-26T10:57:40Z | 2024-06-18T10:28:12Z | Adversarial Multi-dueling Bandits | We introduce the problem of regret minimization in adversarial multi-dueling bandits. While adversarial preferences have been studied in dueling bandits, they have not been explored in multi-dueling bandits. In this setting, the learner is required to select $m geq 2$ arms at each round and observes as feedback the identity of the most preferred arm which is based on an arbitrary preference matrix chosen obliviously. We introduce a novel algorithm, MiDEX (Multi Dueling EXP3), to learn from such preference feedback that is assumed to be generated from a pairwise-subset choice model. We prove that the expected cumulative $T$-round regret of MiDEX compared to a Borda-winner from a set of $K$ arms is upper bounded by $O((K log K)^{1/3} T^{2/3})$. Moreover, we prove a lower bound of $Omega(K^{1/3} T^{2/3})$ for the expected regret in this setting which demonstrates that our proposed algorithm is near-optimal. | [
"['Pratik Gajane']"
] |
null | null | 2406.12478 | null | null | http://arxiv.org/pdf/2406.12478v1 | 2024-06-18T10:32:40Z | 2024-06-18T10:32:40Z | Accelerating Depthwise Separable Convolutions on Ultra-Low-Power Devices | Depthwise separable convolutions are a fundamental component in efficient Deep Neural Networks, as they reduce the number of parameters and operations compared to traditional convolutions while maintaining comparable accuracy. However, their low data reuse opportunities make deploying them notoriously difficult. In this work, we perform an extensive exploration of alternatives to fuse the depthwise and pointwise kernels that constitute the separable convolutional block. Our approach aims to minimize time-consuming memory transfers by combining different data layouts. When targeting a commercial ultra-low-power device with a three-level memory hierarchy, the GreenWaves GAP8 SoC, we reduce the latency of end-to-end network execution by up to 11.40%. Furthermore, our kernels reduce activation data movements between L2 and L1 memories by up to 52.97%. | [
"['Francesco Daghero' 'Alessio Burrello' 'Massimo Poncino' 'Enrico Macii'\n 'Daniele Jahier Pagliari']"
] |
null | null | 2406.12480 | null | null | http://arxiv.org/pdf/2406.12480v1 | 2024-06-18T10:36:21Z | 2024-06-18T10:36:21Z | The Power of LLM-Generated Synthetic Data for Stance Detection in Online
Political Discussions | Stance detection holds great potential for enhancing the quality of online political discussions, as it has shown to be useful for summarizing discussions, detecting misinformation, and evaluating opinion distributions. Usually, transformer-based models are used directly for stance detection, which require large amounts of data. However, the broad range of debate questions in online political discussion creates a variety of possible scenarios that the model is faced with and thus makes data acquisition for model training difficult. In this work, we show how to leverage LLM-generated synthetic data to train and improve stance detection agents for online political discussions:(i) We generate synthetic data for specific debate questions by prompting a Mistral-7B model and show that fine-tuning with the generated synthetic data can substantially improve the performance of stance detection. (ii) We examine the impact of combining synthetic data with the most informative samples from an unlabelled dataset. First, we use the synthetic data to select the most informative samples, second, we combine both these samples and the synthetic data for fine-tuning. This approach reduces labelling effort and consistently surpasses the performance of the baseline model that is trained with fully labeled data. Overall, we show in comprehensive experiments that LLM-generated data greatly improves stance detection performance for online political discussions. | [
"['Stefan Sylvius Wagner' 'Maike Behrendt' 'Marc Ziegele'\n 'Stefan Harmeling']"
] |
null | null | 2406.12499 | null | null | http://arxiv.org/abs/2406.12499v1 | 2024-06-18T11:00:55Z | 2024-06-18T11:00:55Z | Autonomous navigation of catheters and guidewires in mechanical
thrombectomy using inverse reinforcement learning | Purpose: Autonomous navigation of catheters and guidewires can enhance endovascular surgery safety and efficacy, reducing procedure times and operator radiation exposure. Integrating tele-operated robotics could widen access to time-sensitive emergency procedures like mechanical thrombectomy (MT). Reinforcement learning (RL) shows potential in endovascular navigation, yet its application encounters challenges without a reward signal. This study explores the viability of autonomous navigation in MT vasculature using inverse RL (IRL) to leverage expert demonstrations. Methods: This study established a simulation-based training and evaluation environment for MT navigation. We used IRL to infer reward functions from expert behaviour when navigating a guidewire and catheter. We utilized soft actor-critic to train models with various reward functions and compared their performance in silico. Results: We demonstrated feasibility of navigation using IRL. When evaluating single versus dual device (i.e. guidewire versus catheter and guidewire) tracking, both methods achieved high success rates of 95% and 96%, respectively. Dual-tracking, however, utilized both devices mimicking an expert. A success rate of 100% and procedure time of 22.6 s were obtained when training with a reward function obtained through reward shaping. This outperformed a dense reward function (96%, 24.9 s) and an IRL-derived reward function (48%, 59.2 s). Conclusions: We have contributed to the advancement of autonomous endovascular intervention navigation, particularly MT, by employing IRL. The results underscore the potential of using reward shaping to train models, offering a promising avenue for enhancing the accessibility and precision of MT. We envisage that future research can extend our methodology to diverse anatomical structures to enhance generalizability. | [
"['Harry Robertshaw' 'Lennart Karstensen' 'Benjamin Jackson'\n 'Alejandro Granados' 'Thomas C. Booth']"
] |
null | null | 2406.12507 | null | null | http://arxiv.org/pdf/2406.12507v1 | 2024-06-18T11:18:46Z | 2024-06-18T11:18:46Z | Improving the Evaluation and Actionability of Explanation Methods for
Multivariate Time Series Classification | Explanation for Multivariate Time Series Classification (MTSC) is an important topic that is under explored. There are very few quantitative evaluation methodologies and even fewer examples of actionable explanation, where the explanation methods are shown to objectively improve specific computational tasks on time series data. In this paper we focus on analyzing InterpretTime, a recent evaluation methodology for attribution methods applied to MTSC. We reproduce the original paper results, showcase some significant weaknesses of the methodology and propose ideas to improve both its accuracy and efficiency. Unlike related work, we go beyond evaluation and also showcase the actionability of the produced explainer ranking, by using the best attribution methods for the task of channel selection in MTSC. We find that perturbation-based methods such as SHAP and Feature Ablation work well across a set of datasets, classifiers and tasks and outperform gradient-based methods. We apply the best ranked explainers to channel selection for MTSC and show significant data size reduction and improved classifier accuracy. | [
"['Davide Italo Serramazza' 'Thach Le Nguyen' 'Georgiana Ifrim']"
] |
null | null | 2406.12516 | null | null | http://arxiv.org/pdf/2406.12516v1 | 2024-06-18T11:43:20Z | 2024-06-18T11:43:20Z | Update Selective Parameters: Federated Machine Unlearning Based on Model
Explanation | Federated learning is a promising privacy-preserving paradigm for distributed machine learning. In this context, there is sometimes a need for a specialized process called machine unlearning, which is required when the effect of some specific training samples needs to be removed from a learning model due to privacy, security, usability, and/or legislative factors. However, problems arise when current centralized unlearning methods are applied to existing federated learning, in which the server aims to remove all information about a class from the global model. Centralized unlearning usually focuses on simple models or is premised on the ability to access all training data at a central node. However, training data cannot be accessed on the server under the federated learning paradigm, conflicting with the requirements of the centralized unlearning process. Additionally, there are high computation and communication costs associated with accessing clients' data, especially in scenarios involving numerous clients or complex global models. To address these concerns, we propose a more effective and efficient federated unlearning scheme based on the concept of model explanation. Model explanation involves understanding deep networks and individual channel importance, so that this understanding can be used to determine which model channels are critical for classes that need to be unlearned. We select the most influential channels within an already-trained model for the data that need to be unlearned and fine-tune only influential channels to remove the contribution made by those data. In this way, we can simultaneously avoid huge consumption costs and ensure that the unlearned model maintains good performance. Experiments with different training models on various datasets demonstrate the effectiveness of the proposed approach. | [
"['Heng Xu' 'Tianqing Zhu' 'Lefeng Zhang' 'Wanlei Zhou' 'Philip S. Yu']"
] |
null | null | 2406.12531 | null | null | http://arxiv.org/pdf/2406.12531v1 | 2024-06-18T12:01:06Z | 2024-06-18T12:01:06Z | TREE: Tree Regularization for Efficient Execution | The rise of machine learning methods on heavily resource constrained devices requires not only the choice of a suitable model architecture for the target platform, but also the optimization of the chosen model with regard to execution time consumption for inference in order to optimally utilize the available resources. Random forests and decision trees are shown to be a suitable model for such a scenario, since they are not only heavily tunable towards the total model size, but also offer a high potential for optimizing their executions according to the underlying memory architecture. In addition to the straightforward strategy of enforcing shorter paths through decision trees and hence reducing the execution time for inference, hardware-aware implementations can optimize the execution time in an orthogonal manner. One particular hardware-aware optimization is to layout the memory of decision trees in such a way, that higher probably paths are less likely to be evicted from system caches. This works particularly well when splits within tree nodes are uneven and have a high probability to visit one of the child nodes. In this paper, we present a method to reduce path lengths by rewarding uneven probability distributions during the training of decision trees at the cost of a minimal accuracy degradation. Specifically, we regularize the impurity computation of the CART algorithm in order to favor not only low impurity, but also highly asymmetric distributions for the evaluation of split criteria and hence offer a high optimization potential for a memory architecture-aware implementation. We show that especially for binary classification data sets and data sets with many samples, this form of regularization can lead to an reduction of up to approximately four times in the execution time with a minimal accuracy degradation. | [
"['Lena Schmid' 'Daniel Biebert' 'Christian Hakert' 'Kuan-Hsun Chen'\n 'Michel Lang' 'Markus Pauly' 'Jian-Jia Chen']"
] |
null | null | 2406.12538 | null | null | http://arxiv.org/pdf/2406.12538v1 | 2024-06-18T12:15:05Z | 2024-06-18T12:15:05Z | Variational Distillation of Diffusion Policies into Mixture of Experts | This work introduces Variational Diffusion Distillation (VDD), a novel method that distills denoising diffusion policies into Mixtures of Experts (MoE) through variational inference. Diffusion Models are the current state-of-the-art in generative modeling due to their exceptional ability to accurately learn and represent complex, multi-modal distributions. This ability allows Diffusion Models to replicate the inherent diversity in human behavior, making them the preferred models in behavior learning such as Learning from Human Demonstrations (LfD). However, diffusion models come with some drawbacks, including the intractability of likelihoods and long inference times due to their iterative sampling process. The inference times, in particular, pose a significant challenge to real-time applications such as robot control. In contrast, MoEs effectively address the aforementioned issues while retaining the ability to represent complex distributions but are notoriously difficult to train. VDD is the first method that distills pre-trained diffusion models into MoE models, and hence, combines the expressiveness of Diffusion Models with the benefits of Mixture Models. Specifically, VDD leverages a decompositional upper bound of the variational objective that allows the training of each expert separately, resulting in a robust optimization scheme for MoEs. VDD demonstrates across nine complex behavior learning tasks, that it is able to: i) accurately distill complex distributions learned by the diffusion model, ii) outperform existing state-of-the-art distillation methods, and iii) surpass conventional methods for training MoE. | [
"['Hongyi Zhou' 'Denis Blessing' 'Ge Li' 'Onur Celik' 'Xiaogang Jia'\n 'Gerhard Neumann' 'Rudolf Lioutikov']"
] |
null | null | 2406.12539 | null | null | http://arxiv.org/pdf/2406.12539v1 | 2024-06-18T12:16:00Z | 2024-06-18T12:16:00Z | The Heterophilic Snowflake Hypothesis: Training and Empowering GNNs for
Heterophilic Graphs | Graph Neural Networks (GNNs) have become pivotal tools for a range of graph-based learning tasks. Notably, most current GNN architectures operate under the assumption of homophily, whether explicitly or implicitly. While this underlying assumption is frequently adopted, it is not universally applicable, which can result in potential shortcomings in learning effectiveness. In this paper, textbf{for the first time}, we transfer the prevailing concept of ``one node one receptive field" to the heterophilic graph. By constructing a proxy label predictor, we enable each node to possess a latent prediction distribution, which assists connected nodes in determining whether they should aggregate their associated neighbors. Ultimately, every node can have its own unique aggregation hop and pattern, much like each snowflake is unique and possesses its own characteristics. Based on observations, we innovatively introduce the Heterophily Snowflake Hypothesis and provide an effective solution to guide and facilitate research on heterophilic graphs and beyond. We conduct comprehensive experiments including (1) main results on 10 graphs with varying heterophily ratios across 10 backbones; (2) scalability on various deep GNN backbones (SGC, JKNet, etc.) across various large number of layers (2,4,6,8,16,32 layers); (3) comparison with conventional snowflake hypothesis; (4) efficiency comparison with existing graph pruning algorithms. Our observations show that our framework acts as a versatile operator for diverse tasks. It can be integrated into various GNN frameworks, boosting performance in-depth and offering an explainable approach to choosing the optimal network depth. The source code is available at url{https://github.com/bingreeky/HeteroSnoH}. | [
"['Kun Wang' 'Guibin Zhang' 'Xinnan Zhang' 'Junfeng Fang' 'Xun Wu'\n 'Guohao Li' 'Shirui Pan' 'Wei Huang' 'Yuxuan Liang']"
] |
null | null | 2406.12550 | null | null | http://arxiv.org/pdf/2406.12550v1 | 2024-06-18T12:27:02Z | 2024-06-18T12:27:02Z | Offline Imitation Learning with Model-based Reverse Augmentation | In offline Imitation Learning (IL), one of the main challenges is the textit{covariate shift} between the expert observations and the actual distribution encountered by the agent, because it is difficult to determine what action an agent should take when outside the state distribution of the expert demonstrations. Recently, the model-free solutions introduce the supplementary data and identify the latent expert-similar samples to augment the reliable samples during learning. Model-based solutions build forward dynamic models with conservatism quantification and then generate additional trajectories in the neighborhood of expert demonstrations. However, without reward supervision, these methods are often over-conservative in the out-of-expert-support regions, because only in states close to expert-observed states can there be a preferred action enabling policy optimization. To encourage more exploration on expert-unobserved states, we propose a novel model-based framework, called offline Imitation Learning with Self-paced Reverse Augmentation (SRA). Specifically, we build a reverse dynamic model from the offline demonstrations, which can efficiently generate trajectories leading to the expert-observed states in a self-paced style. Then, we use the subsequent reinforcement learning method to learn from the augmented trajectories and transit from expert-unobserved states to expert-observed states. This framework not only explores the expert-unobserved states but also guides maximizing long-term returns on these states, ultimately enabling generalization beyond the expert data. Empirical results show that our proposal could effectively mitigate the covariate shift and achieve the state-of-the-art performance on the offline imitation learning benchmarks. Project website: url{https://www.lamda.nju.edu.cn/shaojj/KDD24_SRA/}. | [
"['Jie-Jing Shao' 'Hao-Sen Shi' 'Lan-Zhe Guo' 'Yu-Feng Li']"
] |
null | null | 2406.12560 | null | null | http://arxiv.org/pdf/2406.12560v2 | 2024-06-24T08:27:13Z | 2024-06-18T12:40:15Z | Towards Bayesian Data Selection | A wide range of machine learning algorithms iteratively add data to the training sample. Examples include semi-supervised learning, active learning, multi-armed bandits, and Bayesian optimization. We embed this kind of data addition into decision theory by framing data selection as a decision problem. This paves the way for finding Bayes-optimal selections of data. For the illustrative case of self-training in semi-supervised learning, we derive the respective Bayes criterion. We further show that deploying this criterion mitigates the issue of confirmation bias by empirically assessing our method for generalized linear models, semi-parametric generalized additive models, and Bayesian neural networks on simulated and real-world data. | [
"['Julian Rodemann']"
] |
null | null | 2406.12563 | null | null | http://arxiv.org/pdf/2406.12563v1 | 2024-06-18T12:49:27Z | 2024-06-18T12:49:27Z | A Super-human Vision-based Reinforcement Learning Agent for Autonomous
Racing in Gran Turismo | Racing autonomous cars faster than the best human drivers has been a longstanding grand challenge for the fields of Artificial Intelligence and robotics. Recently, an end-to-end deep reinforcement learning agent met this challenge in a high-fidelity racing simulator, Gran Turismo. However, this agent relied on global features that require instrumentation external to the car. This paper introduces, to the best of our knowledge, the first super-human car racing agent whose sensor input is purely local to the car, namely pixels from an ego-centric camera view and quantities that can be sensed from on-board the car, such as the car's velocity. By leveraging global features only at training time, the learned agent is able to outperform the best human drivers in time trial (one car on the track at a time) races using only local input features. The resulting agent is evaluated in Gran Turismo 7 on multiple tracks and cars. Detailed ablation experiments demonstrate the agent's strong reliance on visual inputs, making it the first vision-based super-human car racing agent. | [
"['Miguel Vasco' 'Takuma Seno' 'Kenta Kawamoto' 'Kaushik Subramanian'\n 'Peter R. Wurman' 'Peter Stone']"
] |
null | null | 2406.12564 | null | null | http://arxiv.org/pdf/2406.12564v1 | 2024-06-18T12:50:00Z | 2024-06-18T12:50:00Z | Low-Resource Machine Translation through the Lens of Personalized
Federated Learning | We present a new approach based on the Personalized Federated Learning algorithm MeritFed that can be applied to Natural Language Tasks with heterogeneous data. We evaluate it on the Low-Resource Machine Translation task, using the dataset from the Large-Scale Multilingual Machine Translation Shared Task (Small Track #2) and the subset of Sami languages from the multilingual benchmark for Finno-Ugric languages. In addition to its effectiveness, MeritFed is also highly interpretable, as it can be applied to track the impact of each language used for training. Our analysis reveals that target dataset size affects weight distribution across auxiliary languages, that unrelated languages do not interfere with the training, and auxiliary optimizer parameters have minimal impact. Our approach is easy to apply with a few lines of code, and we provide scripts for reproducing the experiments at https://github.com/VityaVitalich/MeritFed | [
"['Viktor Moskvoretskii' 'Nazarii Tupitsa' 'Chris Biemann' 'Samuel Horváth'\n 'Eduard Gorbunov' 'Irina Nikishina']"
] |
null | null | 2406.12569 | null | null | http://arxiv.org/pdf/2406.12569v2 | 2024-06-28T07:23:16Z | 2024-06-18T12:57:33Z | MOYU: A Theoretical Study on Massive Over-activation Yielded Uplifts in
LLMs | Massive Over-activation Yielded Uplifts(MOYU) is an inherent property of large language models, and dynamic activation(DA) based on the MOYU property is a clever yet under-explored strategy designed to accelerate inference in these models. Existing methods that utilize MOYU often face a significant 'Impossible Trinity': struggling to simultaneously maintain model performance, enhance inference speed, and extend applicability across various architectures. Due to the theoretical ambiguities surrounding MOYU, this paper elucidates the root cause of the MOYU property and outlines the mechanisms behind two primary limitations encountered by current DA methods: 1) history-related activation uncertainty, and 2) semantic-irrelevant activation inertia. Our analysis not only underscores the limitations of current dynamic activation strategies within large-scale LLaMA models but also proposes opportunities for refining the design of future sparsity schemes. | [
"['Chi Ma' 'Mincong Huang' 'Chao Wang' 'Yujie Wang' 'Lei Yu']"
] |
null | null | 2406.12572 | null | null | http://arxiv.org/pdf/2406.12572v2 | 2024-06-19T12:28:10Z | 2024-06-18T13:02:12Z | Mathador-LM: A Dynamic Benchmark for Mathematical Reasoning on Large
Language Models | We introduce Mathador-LM, a new benchmark for evaluating the mathematical reasoning on large language models (LLMs), combining ruleset interpretation, planning, and problem-solving. This benchmark is inspired by the Mathador game, where the objective is to reach a target number using basic arithmetic operations on a given set of base numbers, following a simple set of rules. We show that, across leading LLMs, we obtain stable average performance while generating benchmark instances dynamically, following a target difficulty level. Thus, our benchmark alleviates concerns about test-set leakage into training data, an issue that often undermines popular benchmarks. Additionally, we conduct a comprehensive evaluation of both open and closed-source state-of-the-art LLMs on Mathador-LM. Our findings reveal that contemporary models struggle with Mathador-LM, scoring significantly lower than average 3rd graders. This stands in stark contrast to their strong performance on popular mathematical reasoning benchmarks. | [
"['Eldar Kurtic' 'Amir Moeini' 'Dan Alistarh']"
] |
null | null | 2406.12575 | null | null | http://arxiv.org/pdf/2406.12575v1 | 2024-06-18T13:02:48Z | 2024-06-18T13:02:48Z | Training Diffusion Models with Federated Learning | The training of diffusion-based models for image generation is predominantly controlled by a select few Big Tech companies, raising concerns about privacy, copyright, and data authority due to their lack of transparency regarding training data. To ad-dress this issue, we propose a federated diffusion model scheme that enables the independent and collaborative training of diffusion models without exposing local data. Our approach adapts the Federated Averaging (FedAvg) algorithm to train a Denoising Diffusion Model (DDPM). Through a novel utilization of the underlying UNet backbone, we achieve a significant reduction of up to 74% in the number of parameters exchanged during training,compared to the naive FedAvg approach, whilst simultaneously maintaining image quality comparable to the centralized setting, as evaluated by the FID score. | [
"['Matthijs de Goede' 'Bart Cox' 'Jérémie Decouchant']"
] |
null | null | 2406.12588 | null | null | http://arxiv.org/pdf/2406.12588v1 | 2024-06-18T13:18:52Z | 2024-06-18T13:18:52Z | UIFV: Data Reconstruction Attack in Vertical Federated Learning | Vertical Federated Learning (VFL) facilitates collaborative machine learning without the need for participants to share raw private data. However, recent studies have revealed privacy risks where adversaries might reconstruct sensitive features through data leakage during the learning process. Although data reconstruction methods based on gradient or model information are somewhat effective, they reveal limitations in VFL application scenarios. This is because these traditional methods heavily rely on specific model structures and/or have strict limitations on application scenarios. To address this, our study introduces the Unified InverNet Framework into VFL, which yields a novel and flexible approach (dubbed UIFV) that leverages intermediate feature data to reconstruct original data, instead of relying on gradients or model details. The intermediate feature data is the feature exchanged by different participants during the inference phase of VFL. Experiments on four datasets demonstrate that our methods significantly outperform state-of-the-art techniques in attack precision. Our work exposes severe privacy vulnerabilities within VFL systems that pose real threats to practical VFL applications and thus confirms the necessity of further enhancing privacy protection in the VFL architecture. | [
"['Jirui Yang' 'Peng Chen' 'Zhihui Lu' 'Qiang Duan' 'Yubing Bao']"
] |
null | null | 2406.12589 | null | null | http://arxiv.org/pdf/2406.12589v1 | 2024-06-18T13:19:26Z | 2024-06-18T13:19:26Z | Discovering Minimal Reinforcement Learning Environments | Reinforcement learning (RL) agents are commonly trained and evaluated in the same environment. In contrast, humans often train in a specialized environment before being evaluated, such as studying a book before taking an exam. The potential of such specialized training environments is still vastly underexplored, despite their capacity to dramatically speed up training. The framework of synthetic environments takes a first step in this direction by meta-learning neural network-based Markov decision processes (MDPs). The initial approach was limited to toy problems and produced environments that did not transfer to unseen RL algorithms. We extend this approach in three ways: Firstly, we modify the meta-learning algorithm to discover environments invariant towards hyperparameter configurations and learning algorithms. Secondly, by leveraging hardware parallelism and introducing a curriculum on an agent's evaluation episode horizon, we can achieve competitive results on several challenging continuous control problems. Thirdly, we surprisingly find that contextual bandits enable training RL agents that transfer well to their evaluation environment, even if it is a complex MDP. Hence, we set up our experiments to train synthetic contextual bandits, which perform on par with synthetic MDPs, yield additional insights into the evaluation environment, and can speed up downstream applications. | [
"['Jarek Liesen' 'Chris Lu' 'Andrei Lupu' 'Jakob N. Foerster'\n 'Henning Sprekeler' 'Robert T. Lange']"
] |
null | null | 2406.12593 | null | null | http://arxiv.org/pdf/2406.12593v1 | 2024-06-18T13:25:18Z | 2024-06-18T13:25:18Z | PromptDSI: Prompt-based Rehearsal-free Instance-wise Incremental
Learning for Document Retrieval | Differentiable Search Index (DSI) utilizes Pre-trained Language Models (PLMs) for efficient document retrieval without relying on external indexes. However, DSIs need full re-training to handle updates in dynamic corpora, causing significant computational inefficiencies. We introduce PromptDSI, a rehearsal-free, prompt-based approach for instance-wise incremental learning in document retrieval. PromptDSI attaches prompts to the frozen PLM's encoder of DSI, leveraging its powerful representation to efficiently index new corpora while maintaining a balance between stability and plasticity. We eliminate the initial forward pass of prompt-based continual learning methods that doubles training and inference time. Moreover, we propose a topic-aware prompt pool that employs neural topic embeddings as fixed keys. This strategy ensures diverse and effective prompt usage, addressing the challenge of parameter underutilization caused by the collapse of the query-key matching mechanism. Our empirical evaluations demonstrate that PromptDSI matches IncDSI in managing forgetting while significantly enhancing recall by over 4% on new corpora. | [
"['Tuan-Luc Huynh' 'Thuy-Trang Vu' 'Weiqing Wang' 'Yinwei Wei' 'Trung Le'\n 'Dragan Gasevic' 'Yuan-Fang Li' 'Thanh-Toan Do']"
] |
null | null | 2406.12600 | null | null | http://arxiv.org/pdf/2406.12600v1 | 2024-06-18T13:31:15Z | 2024-06-18T13:31:15Z | Generalization bounds for mixing processes via delayed online-to-PAC
conversions | We study the generalization error of statistical learning algorithms in a non-i.i.d. setting, where the training data is sampled from a stationary mixing process. We develop an analytic framework for this scenario based on a reduction to online learning with delayed feedback. In particular, we show that the existence of an online learning algorithm with bounded regret (against a fixed statistical learning algorithm in a specially constructed game of online learning with delayed feedback) implies low generalization error of said statistical learning method even if the data sequence is sampled from a mixing time series. The rates demonstrate a trade-off between the amount of delay in the online learning game and the degree of dependence between consecutive data points, with near-optimal rates recovered in a number of well-studied settings when the delay is tuned appropriately as a function of the mixing time of the process. | [
"['Baptiste Abeles' 'Eugenio Clerico' 'Gergely Neu']"
] |
null | null | 2406.12602 | null | null | http://arxiv.org/pdf/2406.12602v2 | 2024-06-21T14:35:08Z | 2024-06-18T13:32:12Z | Reinforcement-Learning based routing for packet-optical networks with
hybrid telemetry | This article provides a methodology and open-source implementation of Reinforcement Learning algorithms for finding optimal routes in a packet-optical network scenario. The algorithm uses measurements provided by the physical layer (pre-FEC bit error rate and propagation delay) and the link layer (link load) to configure a set of latency-based rewards and penalties based on such measurements. Then, the algorithm executes Q-learning based on this set of rewards for finding the optimal routing strategies. It is further shown that the algorithm dynamically adapts to changing network conditions by re-calculating optimal policies upon either link load changes or link degradation as measured by pre-FEC BER. | [
"['A. L. García Navarro' 'Nataliia Koneva' 'Alfonso Sánchez-Macián'\n 'José Alberto Hernández' 'Óscar González de Dios' 'J. M. Rivas-Moscoso']"
] |
null | null | 2406.12605 | null | null | http://arxiv.org/pdf/2406.12605v1 | 2024-06-18T13:34:02Z | 2024-06-18T13:34:02Z | Attack and Defense of Deep Learning Models in the Field of Web Attack
Detection | The challenge of WAD (web attack detection) is growing as hackers continuously refine their methods to evade traditional detection. Deep learning models excel in handling complex unknown attacks due to their strong generalization and adaptability. However, they are vulnerable to backdoor attacks, where contextually irrelevant fragments are inserted into requests, compromising model stability. While backdoor attacks are well studied in image recognition, they are largely unexplored in WAD. This paper introduces backdoor attacks in WAD, proposing five methods and corresponding defenses. Testing on textCNN, biLSTM, and tinybert models shows an attack success rate over 87%, reducible through fine-tuning. Future research should focus on backdoor defenses in WAD. All the code and data of this paper can be obtained at https://anonymous.4open.science/r/attackDefenceinDL-7E05 | [
"['Lijia Shi' 'Shihao Dong']"
] |
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