categories
string | doi
string | id
string | year
float64 | venue
string | link
string | updated
string | published
string | title
string | abstract
string | authors
list |
|---|---|---|---|---|---|---|---|---|---|---|
null | null | 2404.09391 | null | null | http://arxiv.org/pdf/2404.09391v1 | 2024-04-15T00:23:41Z | 2024-04-15T00:23:41Z | Privacy at a Price: Exploring its Dual Impact on AI Fairness | The worldwide adoption of machine learning (ML) and deep learning models, particularly in critical sectors, such as healthcare and finance, presents substantial challenges in maintaining individual privacy and fairness. These two elements are vital to a trustworthy environment for learning systems. While numerous studies have concentrated on protecting individual privacy through differential privacy (DP) mechanisms, emerging research indicates that differential privacy in machine learning models can unequally impact separate demographic subgroups regarding prediction accuracy. This leads to a fairness concern, and manifests as biased performance. Although the prevailing view is that enhancing privacy intensifies fairness disparities, a smaller, yet significant, subset of research suggests the opposite view. In this article, with extensive evaluation results, we demonstrate that the impact of differential privacy on fairness is not monotonous. Instead, we observe that the accuracy disparity initially grows as more DP noise (enhanced privacy) is added to the ML process, but subsequently diminishes at higher privacy levels with even more noise. Moreover, implementing gradient clipping in the differentially private stochastic gradient descent ML method can mitigate the negative impact of DP noise on fairness. This mitigation is achieved by moderating the disparity growth through a lower clipping threshold. | [
"['Mengmeng Yang' 'Ming Ding' 'Youyang Qu' 'Wei Ni' 'David Smith'\n 'Thierry Rakotoarivelo']"
]
|
null | null | 2404.09392 | null | null | http://arxiv.org/pdf/2404.09392v1 | 2024-04-15T00:25:12Z | 2024-04-15T00:25:12Z | An Autoencoder-Based Constellation Design for AirComp in Wireless
Federated Learning | Wireless federated learning (FL) relies on efficient uplink communications to aggregate model updates across distributed edge devices. Over-the-air computation (a.k.a. AirComp) has emerged as a promising approach for addressing the scalability challenge of FL over wireless links with limited communication resources. Unlike conventional methods, AirComp allows multiple edge devices to transmit uplink signals simultaneously, enabling the parameter server to directly decode the average global model. However, existing AirComp solutions are intrinsically analog, while modern wireless systems predominantly adopt digital modulations. Consequently, careful constellation designs are necessary to accurately decode the sum model updates without ambiguity. In this paper, we propose an end-to-end communication system supporting AirComp with digital modulation, aiming to overcome the challenges associated with accurate decoding of the sum signal with constellation designs. We leverage autoencoder network structures and explore the joint optimization of transmitter and receiver components. Our approach fills an important gap in the context of accurately decoding the sum signal in digital modulation-based AirComp, which can advance the deployment of FL in contemporary wireless systems. | [
"['Yujia Mu' 'Xizixiang Wei' 'Cong Shen']"
]
|
null | null | 2404.09402 | null | null | http://arxiv.org/pdf/2404.09402v1 | 2024-04-15T01:28:16Z | 2024-04-15T01:28:16Z | Neural McKean-Vlasov Processes: Distributional Dependence in Diffusion
Processes | McKean-Vlasov stochastic differential equations (MV-SDEs) provide a mathematical description of the behavior of an infinite number of interacting particles by imposing a dependence on the particle density. As such, we study the influence of explicitly including distributional information in the parameterization of the SDE. We propose a series of semi-parametric methods for representing MV-SDEs, and corresponding estimators for inferring parameters from data based on the properties of the MV-SDE. We analyze the characteristics of the different architectures and estimators, and consider their applicability in relevant machine learning problems. We empirically compare the performance of the different architectures and estimators on real and synthetic datasets for time series and probabilistic modeling. The results suggest that explicitly including distributional dependence in the parameterization of the SDE is effective in modeling temporal data with interaction under an exchangeability assumption while maintaining strong performance for standard It^o-SDEs due to the richer class of probability flows associated with MV-SDEs. | [
"['Haoming Yang' 'Ali Hasan' 'Yuting Ng' 'Vahid Tarokh']"
]
|
null | null | 2404.09403 | null | null | http://arxiv.org/pdf/2404.09403v2 | 2024-04-22T20:50:53Z | 2024-04-15T01:34:44Z | Neuro-Inspired Information-Theoretic Hierarchical Perception for
Multimodal Learning | Integrating and processing information from various sources or modalities are critical for obtaining a comprehensive and accurate perception of the real world in autonomous systems and cyber-physical systems. Drawing inspiration from neuroscience, we develop the Information-Theoretic Hierarchical Perception (ITHP) model, which utilizes the concept of information bottleneck. Different from most traditional fusion models that incorporate all modalities identically in neural networks, our model designates a prime modality and regards the remaining modalities as detectors in the information pathway, serving to distill the flow of information. Our proposed perception model focuses on constructing an effective and compact information flow by achieving a balance between the minimization of mutual information between the latent state and the input modal state, and the maximization of mutual information between the latent states and the remaining modal states. This approach leads to compact latent state representations that retain relevant information while minimizing redundancy, thereby substantially enhancing the performance of multimodal representation learning. Experimental evaluations on the MUStARD, CMU-MOSI, and CMU-MOSEI datasets demonstrate that our model consistently distills crucial information in multimodal learning scenarios, outperforming state-of-the-art benchmarks. Remarkably, on the CMU-MOSI dataset, ITHP surpasses human-level performance in the multimodal sentiment binary classification task across all evaluation metrics (i.e., Binary Accuracy, F1 Score, Mean Absolute Error, and Pearson Correlation). | [
"['Xiongye Xiao' 'Gengshuo Liu' 'Gaurav Gupta' 'Defu Cao' 'Shixuan Li'\n 'Yaxing Li' 'Tianqing Fang' 'Mingxi Cheng' 'Paul Bogdan']"
]
|
null | null | 2404.09406 | null | null | http://arxiv.org/pdf/2404.09406v2 | 2024-04-16T05:58:39Z | 2024-04-15T01:47:44Z | Human-in-the-Loop Segmentation of Multi-species Coral Imagery | Broad-scale marine surveys performed by underwater vehicles significantly increase the availability of coral reef imagery, however it is costly and time-consuming for domain experts to label images. Point label propagation is an approach used to leverage existing image data labeled with sparse point labels. The resulting augmented ground truth generated is then used to train a semantic segmentation model. Here, we first demonstrate that recent advances in foundation models enable generation of multi-species coral augmented ground truth masks using denoised DINOv2 features and K-Nearest Neighbors (KNN), without the need for any pre-training or custom-designed algorithms. For extremely sparsely labeled images, we propose a labeling regime based on human-in-the-loop principles, resulting in significant improvement in annotation efficiency: If only 5 point labels per image are available, our proposed human-in-the-loop approach improves on the state-of-the-art by 17.3% for pixel accuracy and 22.6% for mIoU; and by 10.6% and 19.1% when 10 point labels per image are available. Even if the human-in-the-loop labeling regime is not used, the denoised DINOv2 features with a KNN outperforms the prior state-of-the-art by 3.5% for pixel accuracy and 5.7% for mIoU (5 grid points). We also provide a detailed analysis of how point labeling style and the quantity of points per image affects the point label propagation quality and provide general recommendations on maximizing point label efficiency. | [
"['Scarlett Raine' 'Ross Marchant' 'Brano Kusy' 'Frederic Maire'\n 'Niko Suenderhauf' 'Tobias Fischer']"
]
|
null | null | 2404.09411 | null | null | http://arxiv.org/pdf/2404.09411v4 | 2024-06-04T00:09:59Z | 2024-04-15T01:58:18Z | Wasserstein Wormhole: Scalable Optimal Transport Distance with
Transformers | Optimal transport (OT) and the related Wasserstein metric (W) are powerful and ubiquitous tools for comparing distributions. However, computing pairwise Wasserstein distances rapidly becomes intractable as cohort size grows. An attractive alternative would be to find an embedding space in which pairwise Euclidean distances map to OT distances, akin to standard multidimensional scaling (MDS). We present Wasserstein Wormhole, a transformer-based autoencoder that embeds empirical distributions into a latent space wherein Euclidean distances approximate OT distances. Extending MDS theory, we show that our objective function implies a bound on the error incurred when embedding non-Euclidean distances. Empirically, distances between Wormhole embeddings closely match Wasserstein distances, enabling linear time computation of OT distances. Along with an encoder that maps distributions to embeddings, Wasserstein Wormhole includes a decoder that maps embeddings back to distributions, allowing for operations in the embedding space to generalize to OT spaces, such as Wasserstein barycenter estimation and OT interpolation. By lending scalability and interpretability to OT approaches, Wasserstein Wormhole unlocks new avenues for data analysis in the fields of computational geometry and single-cell biology. | [
"['Doron Haviv' 'Russell Zhang Kunes' 'Thomas Dougherty'\n 'Cassandra Burdziak' 'Tal Nawy' 'Anna Gilbert' \"Dana Pe'er\"]"
]
|
null | null | 2404.09413 | null | null | http://arxiv.org/pdf/2404.09413v1 | 2024-04-15T02:00:24Z | 2024-04-15T02:00:24Z | On the Optimal Regret of Locally Private Linear Contextual Bandit | Contextual bandit with linear reward functions is among one of the most extensively studied models in bandit and online learning research. Recently, there has been increasing interest in designing emph{locally private} linear contextual bandit algorithms, where sensitive information contained in contexts and rewards is protected against leakage to the general public. While the classical linear contextual bandit algorithm admits cumulative regret upper bounds of $tilde O(sqrt{T})$ via multiple alternative methods, it has remained open whether such regret bounds are attainable in the presence of local privacy constraints, with the state-of-the-art result being $tilde O(T^{3/4})$. In this paper, we show that it is indeed possible to achieve an $tilde O(sqrt{T})$ regret upper bound for locally private linear contextual bandit. Our solution relies on several new algorithmic and analytical ideas, such as the analysis of mean absolute deviation errors and layered principal component regression in order to achieve small mean absolute deviation errors. | [
"['Jiachun Li' 'David Simchi-Levi' 'Yining Wang']"
]
|
null | null | 2404.09415 | null | null | http://arxiv.org/pdf/2404.09415v1 | 2024-04-15T02:02:15Z | 2024-04-15T02:02:15Z | A Review on Machine Learning Algorithms for Dust Aerosol Detection using
Satellite Data | Dust storms are associated with certain respiratory illnesses across different areas in the world. Researchers have devoted time and resources to study the elements surrounding dust storm phenomena. This paper reviews the efforts of those who have investigated dust aerosols using sensors onboard of satellites using machine learning-based approaches. We have reviewed the most common issues revolving dust aerosol modeling using different datasets and different sensors from a historical perspective. Our findings suggest that multi-spectral approaches based on linear and non-linear combinations of spectral bands are some of the most successful for visualization and quantitative analysis; however, when researchers have leveraged machine learning, performance has been improved and new opportunities to solve unique problems arise. | [
"['Nurul Rafi' 'Pablo Rivas']"
]
|
null | null | 2404.09430 | null | null | http://arxiv.org/pdf/2404.09430v1 | 2024-04-15T03:04:37Z | 2024-04-15T03:04:37Z | On the Efficiency of Privacy Attacks in Federated Learning | Recent studies have revealed severe privacy risks in federated learning, represented by Gradient Leakage Attacks. However, existing studies mainly aim at increasing the privacy attack success rate and overlook the high computation costs for recovering private data, making the privacy attack impractical in real applications. In this study, we examine privacy attacks from the perspective of efficiency and propose a framework for improving the Efficiency of Privacy Attacks in Federated Learning (EPAFL). We make three novel contributions. First, we systematically evaluate the computational costs for representative privacy attacks in federated learning, which exhibits a high potential to optimize efficiency. Second, we propose three early-stopping techniques to effectively reduce the computational costs of these privacy attacks. Third, we perform experiments on benchmark datasets and show that our proposed method can significantly reduce computational costs and maintain comparable attack success rates for state-of-the-art privacy attacks in federated learning. We provide the codes on GitHub at https://github.com/mlsysx/EPAFL. | [
"['Nawrin Tabassum' 'Ka-Ho Chow' 'Xuyu Wang' 'Wenbin Zhang' 'Yanzhao Wu']"
]
|
null | null | 2404.09432 | null | null | http://arxiv.org/pdf/2404.09432v1 | 2024-04-15T03:12:17Z | 2024-04-15T03:12:17Z | The 8th AI City Challenge | The eighth AI City Challenge highlighted the convergence of computer vision and artificial intelligence in areas like retail, warehouse settings, and Intelligent Traffic Systems (ITS), presenting significant research opportunities. The 2024 edition featured five tracks, attracting unprecedented interest from 726 teams in 47 countries and regions. Track 1 dealt with multi-target multi-camera (MTMC) people tracking, highlighting significant enhancements in camera count, character number, 3D annotation, and camera matrices, alongside new rules for 3D tracking and online tracking algorithm encouragement. Track 2 introduced dense video captioning for traffic safety, focusing on pedestrian accidents using multi-camera feeds to improve insights for insurance and prevention. Track 3 required teams to classify driver actions in a naturalistic driving analysis. Track 4 explored fish-eye camera analytics using the FishEye8K dataset. Track 5 focused on motorcycle helmet rule violation detection. The challenge utilized two leaderboards to showcase methods, with participants setting new benchmarks, some surpassing existing state-of-the-art achievements. | [
"['Shuo Wang' 'David C. Anastasiu' 'Zheng Tang' 'Ming-Ching Chang'\n 'Yue Yao' 'Liang Zheng' 'Mohammed Shaiqur Rahman' 'Meenakshi S. Arya'\n 'Anuj Sharma' 'Pranamesh Chakraborty' 'Sanjita Prajapati' 'Quan Kong'\n 'Norimasa Kobori' 'Munkhjargal Gochoo' 'Munkh-Erdene Otgonbold'\n 'Fady Alnajjar' 'Ganzorig Batnasan' 'Ping-Yang Chen' 'Jun-Wei Hsieh'\n 'Xunlei Wu' 'Sameer Satish Pusegaonkar' 'Yizhou Wang' 'Sujit Biswas'\n 'Rama Chellappa']"
]
|
null | null | 2404.09438 | null | null | http://arxiv.org/pdf/2404.09438v1 | 2024-04-15T03:50:47Z | 2024-04-15T03:50:47Z | Developing Lagrangian-based Methods for Nonsmooth Nonconvex Optimization | In this paper, we consider the minimization of a nonsmooth nonconvex objective function $f(x)$ over a closed convex subset $mathcal{X}$ of $mathbb{R}^n$, with additional nonsmooth nonconvex constraints $c(x) = 0$. We develop a unified framework for developing Lagrangian-based methods, which takes a single-step update to the primal variables by some subgradient methods in each iteration. These subgradient methods are ``embedded'' into our framework, in the sense that they are incorporated as black-box updates to the primal variables. We prove that our proposed framework inherits the global convergence guarantees from these embedded subgradient methods under mild conditions. In addition, we show that our framework can be extended to solve constrained optimization problems with expectation constraints. Based on the proposed framework, we show that a wide range of existing stochastic subgradient methods, including the proximal SGD, proximal momentum SGD, and proximal ADAM, can be embedded into Lagrangian-based methods. Preliminary numerical experiments on deep learning tasks illustrate that our proposed framework yields efficient variants of Lagrangian-based methods with convergence guarantees for nonconvex nonsmooth constrained optimization problems. | [
"['Nachuan Xiao' 'Kuangyu Ding' 'Xiaoyin Hu' 'Kim-Chuan Toh']"
]
|
null | null | 2404.09443 | null | null | http://arxiv.org/pdf/2404.09443v1 | 2024-04-15T04:02:39Z | 2024-04-15T04:02:39Z | Hybrid FedGraph: An efficient hybrid federated learning algorithm using
graph convolutional neural network | Federated learning is an emerging paradigm for decentralized training of machine learning models on distributed clients, without revealing the data to the central server. Most existing works have focused on horizontal or vertical data distributions, where each client possesses different samples with shared features, or each client fully shares only sample indices, respectively. However, the hybrid scheme is much less studied, even though it is much more common in the real world. Therefore, in this paper, we propose a generalized algorithm, FedGraph, that introduces a graph convolutional neural network to capture feature-sharing information while learning features from a subset of clients. We also develop a simple but effective clustering algorithm that aggregates features produced by the deep neural networks of each client while preserving data privacy. | [
"['Jaeyeon Jang' 'Diego Klabjan' 'Veena Mendiratta' 'Fanfei Meng']"
]
|
null | null | 2404.09445 | null | null | http://arxiv.org/pdf/2404.09445v1 | 2024-04-15T04:14:42Z | 2024-04-15T04:14:42Z | Exploring Text-to-Motion Generation with Human Preference | This paper presents an exploration of preference learning in text-to-motion generation. We find that current improvements in text-to-motion generation still rely on datasets requiring expert labelers with motion capture systems. Instead, learning from human preference data does not require motion capture systems; a labeler with no expertise simply compares two generated motions. This is particularly efficient because evaluating the model's output is easier than gathering the motion that performs a desired task (e.g. backflip). To pioneer the exploration of this paradigm, we annotate 3,528 preference pairs generated by MotionGPT, marking the first effort to investigate various algorithms for learning from preference data. In particular, our exploration highlights important design choices when using preference data. Additionally, our experimental results show that preference learning has the potential to greatly improve current text-to-motion generative models. Our code and dataset are publicly available at https://github.com/THU-LYJ-Lab/InstructMotion}{https://github.com/THU-LYJ-Lab/InstructMotion to further facilitate research in this area. | [
"['Jenny Sheng' 'Matthieu Lin' 'Andrew Zhao' 'Kevin Pruvost' 'Yu-Hui Wen'\n 'Yangguang Li' 'Gao Huang' 'Yong-Jin Liu']"
]
|
null | null | 2404.09447 | null | null | http://arxiv.org/pdf/2404.09447v1 | 2024-04-15T04:20:01Z | 2024-04-15T04:20:01Z | kNN-CLIP: Retrieval Enables Training-Free Segmentation on Continually
Expanding Large Vocabularies | Rapid advancements in continual segmentation have yet to bridge the gap of scaling to large continually expanding vocabularies under compute-constrained scenarios. We discover that traditional continual training leads to catastrophic forgetting under compute constraints, unable to outperform zero-shot segmentation methods. We introduce a novel strategy for semantic and panoptic segmentation with zero forgetting, capable of adapting to continually growing vocabularies without the need for retraining or large memory costs. Our training-free approach, kNN-CLIP, leverages a database of instance embeddings to enable open-vocabulary segmentation approaches to continually expand their vocabulary on any given domain with a single-pass through data, while only storing embeddings minimizing both compute and memory costs. This method achieves state-of-the-art mIoU performance across large-vocabulary semantic and panoptic segmentation datasets. We hope kNN-CLIP represents a step forward in enabling more efficient and adaptable continual segmentation, paving the way for advances in real-world large-vocabulary continual segmentation methods. | [
"['Zhongrui Gui' 'Shuyang Sun' 'Runjia Li' 'Jianhao Yuan' 'Zhaochong An'\n 'Karsten Roth' 'Ameya Prabhu' 'Philip Torr']"
]
|
null | null | 2404.09453 | null | null | http://arxiv.org/pdf/2404.09453v1 | 2024-04-15T04:41:53Z | 2024-04-15T04:41:53Z | Towards Greener Nights: Exploring AI-Driven Solutions for Light
Pollution Management | This research endeavors to address the pervasive issue of light pollution through an interdisciplinary approach, leveraging data science and machine learning techniques. By analyzing extensive datasets and research findings, we aim to develop predictive models capable of estimating the degree of sky glow observed in various locations and times. Our research seeks to inform evidence-based interventions and promote responsible outdoor lighting practices to mitigate the adverse impacts of light pollution on ecosystems, energy consumption, and human well-being. | [
"['Paras Varshney' 'Niral Desai' 'Uzair Ahmed']"
]
|
null | null | 2404.09454 | null | null | http://arxiv.org/pdf/2404.09454v2 | 2024-04-24T00:08:42Z | 2024-04-15T04:43:53Z | Utility-Fairness Trade-Offs and How to Find Them | When building classification systems with demographic fairness considerations, there are two objectives to satisfy: 1) maximizing utility for the specific task and 2) ensuring fairness w.r.t. a known demographic attribute. These objectives often compete, so optimizing both can lead to a trade-off between utility and fairness. While existing works acknowledge the trade-offs and study their limits, two questions remain unanswered: 1) What are the optimal trade-offs between utility and fairness? and 2) How can we numerically quantify these trade-offs from data for a desired prediction task and demographic attribute of interest? This paper addresses these questions. We introduce two utility-fairness trade-offs: the Data-Space and Label-Space Trade-off. The trade-offs reveal three regions within the utility-fairness plane, delineating what is fully and partially possible and impossible. We propose U-FaTE, a method to numerically quantify the trade-offs for a given prediction task and group fairness definition from data samples. Based on the trade-offs, we introduce a new scheme for evaluating representations. An extensive evaluation of fair representation learning methods and representations from over 1000 pre-trained models revealed that most current approaches are far from the estimated and achievable fairness-utility trade-offs across multiple datasets and prediction tasks. | [
"['Sepehr Dehdashtian' 'Bashir Sadeghi' 'Vishnu Naresh Boddeti']"
]
|
null | null | 2404.09456 | null | null | http://arxiv.org/abs/2404.09456v1 | 2024-04-15T04:45:49Z | 2024-04-15T04:45:49Z | Hyperbolic Heterogeneous Graph Attention Networks | Most previous heterogeneous graph embedding models represent elements in a heterogeneous graph as vector representations in a low-dimensional Euclidean space. However, because heterogeneous graphs inherently possess complex structures, such as hierarchical or power-law structures, distortions can occur when representing them in Euclidean space. To overcome this limitation, we propose Hyperbolic Heterogeneous Graph Attention Networks (HHGAT) that learn vector representations in hyperbolic spaces with meta-path instances. We conducted experiments on three real-world heterogeneous graph datasets, demonstrating that HHGAT outperforms state-of-the-art heterogeneous graph embedding models in node classification and clustering tasks. | [
"['Jongmin Park' 'Seunghoon Han' 'Soohwan Jeong' 'Sungsu Lim']"
]
|
null | null | 2404.09461 | null | null | http://arxiv.org/pdf/2404.09461v1 | 2024-04-15T05:00:40Z | 2024-04-15T05:00:40Z | Improved Object-Based Style Transfer with Single Deep Network | This research paper proposes a novel methodology for image-to-image style transfer on objects utilizing a single deep convolutional neural network. The proposed approach leverages the You Only Look Once version 8 (YOLOv8) segmentation model and the backbone neural network of YOLOv8 for style transfer. The primary objective is to enhance the visual appeal of objects in images by seamlessly transferring artistic styles while preserving the original object characteristics. The proposed approach's novelty lies in combining segmentation and style transfer in a single deep convolutional neural network. This approach omits the need for multiple stages or models, thus resulting in simpler training and deployment of the model for practical applications. The results of this approach are shown on two content images by applying different style images. The paper also demonstrates the ability to apply style transfer on multiple objects in the same image. | [
"['Harshmohan Kulkarni' 'Om Khare' 'Ninad Barve' 'Sunil Mane']"
]
|
null | null | 2404.09463 | null | null | http://arxiv.org/pdf/2404.09463v1 | 2024-04-15T05:14:52Z | 2024-04-15T05:14:52Z | PRIME: A CyberGIS Platform for Resilience Inference Measurement and
Enhancement | In an era of increased climatic disasters, there is an urgent need to develop reliable frameworks and tools for evaluating and improving community resilience to climatic hazards at multiple geographical and temporal scales. Defining and quantifying resilience in the social domain is relatively subjective due to the intricate interplay of socioeconomic factors with disaster resilience. Meanwhile, there is a lack of computationally rigorous, user-friendly tools that can support customized resilience assessment considering local conditions. This study aims to address these gaps through the power of CyberGIS with three objectives: 1) To develop an empirically validated disaster resilience model - Customized Resilience Inference Measurement designed for multi-scale community resilience assessment and influential socioeconomic factors identification, 2) To implement a Platform for Resilience Inference Measurement and Enhancement module in the CyberGISX platform backed by high-performance computing, 3) To demonstrate the utility of PRIME through a representative study. CRIM generates vulnerability, adaptability, and overall resilience scores derived from empirical hazard parameters. Computationally intensive Machine Learning methods are employed to explain the intricate relationships between these scores and socioeconomic driving factors. PRIME provides a web-based notebook interface guiding users to select study areas, configure parameters, calculate and geo-visualize resilience scores, and interpret socioeconomic factors shaping resilience capacities. A representative study showcases the efficiency of the platform while explaining how the visual results obtained may be interpreted. The essence of this work lies in its comprehensive architecture that encapsulates the requisite data, analytical and geo-visualization functions, and ML models for resilience assessment. | [
"['Debayan Mandal' 'Dr. Lei Zou' 'Rohan Singh Wilkho' 'Joynal Abedin'\n 'Bing Zhou' 'Dr. Heng Cai' 'Dr. Furqan Baig' 'Dr. Nasir Gharaibeh'\n 'Dr. Nina Lam']"
]
|
null | null | 2404.09465 | null | null | http://arxiv.org/pdf/2404.09465v2 | 2024-07-10T02:43:14Z | 2024-04-15T05:29:23Z | PhyScene: Physically Interactable 3D Scene Synthesis for Embodied AI | With recent developments in Embodied Artificial Intelligence (EAI) research, there has been a growing demand for high-quality, large-scale interactive scene generation. While prior methods in scene synthesis have prioritized the naturalness and realism of the generated scenes, the physical plausibility and interactivity of scenes have been largely left unexplored. To address this disparity, we introduce PhyScene, a novel method dedicated to generating interactive 3D scenes characterized by realistic layouts, articulated objects, and rich physical interactivity tailored for embodied agents. Based on a conditional diffusion model for capturing scene layouts, we devise novel physics- and interactivity-based guidance mechanisms that integrate constraints from object collision, room layout, and object reachability. Through extensive experiments, we demonstrate that PhyScene effectively leverages these guidance functions for physically interactable scene synthesis, outperforming existing state-of-the-art scene synthesis methods by a large margin. Our findings suggest that the scenes generated by PhyScene hold considerable potential for facilitating diverse skill acquisition among agents within interactive environments, thereby catalyzing further advancements in embodied AI research. Project website: http://physcene.github.io. | [
"['Yandan Yang' 'Baoxiong Jia' 'Peiyuan Zhi' 'Siyuan Huang']"
]
|
null | null | 2404.09466 | null | null | http://arxiv.org/pdf/2404.09466v4 | 2024-05-24T02:20:54Z | 2024-04-15T05:35:09Z | Scoring Intervals using Non-Hierarchical Transformer For Automatic Piano
Transcription | The neural semi-Markov Conditional Random Field (semi-CRF) framework has demonstrated promise for event-based piano transcription. In this framework, all events (notes or pedals) are represented as closed intervals tied to specific event types. The neural semi-CRF approach requires an interval scoring matrix that assigns a score for every candidate interval. However, designing an efficient and expressive architecture for scoring intervals is not trivial. In this paper, we introduce a simple method for scoring intervals using scaled inner product operations that resemble how attention scoring is done in transformers. We show theoretically that, due to the special structure from encoding the non-overlapping intervals, under a mild condition, the inner product operations are expressive enough to represent an ideal scoring matrix that can yield the correct transcription result. We then demonstrate that an encoder-only non-hierarchical transformer backbone, operating only on a low-time-resolution feature map, is capable of transcribing piano notes and pedals with high accuracy and time precision. The experiment shows that our approach achieves the new state-of-the-art performance across all subtasks in terms of the F1 measure on the Maestro dataset. | [
"['Yujia Yan' 'Zhiyao Duan']"
]
|
null | null | 2404.09469 | null | null | http://arxiv.org/pdf/2404.09469v1 | 2024-04-15T05:44:03Z | 2024-04-15T05:44:03Z | Virtually Enriched NYU Depth V2 Dataset for Monocular Depth Estimation:
Do We Need Artificial Augmentation? | We present ANYU, a new virtually augmented version of the NYU depth v2 dataset, designed for monocular depth estimation. In contrast to the well-known approach where full 3D scenes of a virtual world are utilized to generate artificial datasets, ANYU was created by incorporating RGB-D representations of virtual reality objects into the original NYU depth v2 images. We specifically did not match each generated virtual object with an appropriate texture and a suitable location within the real-world image. Instead, an assignment of texture, location, lighting, and other rendering parameters was randomized to maximize a diversity of the training data, and to show that it is randomness that can improve the generalizing ability of a dataset. By conducting extensive experiments with our virtually modified dataset and validating on the original NYU depth v2 and iBims-1 benchmarks, we show that ANYU improves the monocular depth estimation performance and generalization of deep neural networks with considerably different architectures, especially for the current state-of-the-art VPD model. To the best of our knowledge, this is the first work that augments a real-world dataset with randomly generated virtual 3D objects for monocular depth estimation. We make our ANYU dataset publicly available in two training configurations with 10% and 100% additional synthetically enriched RGB-D pairs of training images, respectively, for efficient training and empirical exploration of virtual augmentation at https://github.com/ABrain-One/ANYU | [
"['Dmitry Ignatov' 'Andrey Ignatov' 'Radu Timofte']"
]
|
null | null | 2404.09470 | null | null | http://arxiv.org/pdf/2404.09470v2 | 2024-04-16T01:52:45Z | 2024-04-15T05:50:46Z | LatticeML: A data-driven application for predicting the effective Young
Modulus of high temperature graph based architected materials | Architected materials with their unique topology and geometry offer the potential to modify physical and mechanical properties. Machine learning can accelerate the design and optimization of these materials by identifying optimal designs and forecasting performance. This work presents LatticeML, a data-driven application for predicting the effective Young's Modulus of high-temperature graph-based architected materials. The study considers eleven graph-based lattice structures with two high-temperature alloys, Ti-6Al-4V and Inconel 625. Finite element simulations were used to compute the effective Young's Modulus of the 2x2x2 unit cell configurations. A machine learning framework was developed to predict Young's Modulus, involving data collection, preprocessing, implementation of regression models, and deployment of the best-performing model. Five supervised learning algorithms were evaluated, with the XGBoost Regressor achieving the highest accuracy (MSE = 2.7993, MAE = 1.1521, R-squared = 0.9875). The application uses the Streamlit framework to create an interactive web interface, allowing users to input material and geometric parameters and obtain predicted Young's Modulus values. | [
"['Akshansh Mishra']"
]
|
null | null | 2404.09481 | null | null | http://arxiv.org/pdf/2404.09481v1 | 2024-04-15T06:07:10Z | 2024-04-15T06:07:10Z | SpamDam: Towards Privacy-Preserving and Adversary-Resistant SMS Spam
Detection | In this study, we introduce SpamDam, a SMS spam detection framework designed to overcome key challenges in detecting and understanding SMS spam, such as the lack of public SMS spam datasets, increasing privacy concerns of collecting SMS data, and the need for adversary-resistant detection models. SpamDam comprises four innovative modules: an SMS spam radar that identifies spam messages from online social networks(OSNs); an SMS spam inspector for statistical analysis; SMS spam detectors(SSDs) that enable both central training and federated learning; and an SSD analyzer that evaluates model resistance against adversaries in realistic scenarios. Leveraging SpamDam, we have compiled over 76K SMS spam messages from Twitter and Weibo between 2018 and 2023, forming the largest dataset of its kind. This dataset has enabled new insights into recent spam campaigns and the training of high-performing binary and multi-label classifiers for spam detection. Furthermore, effectiveness of federated learning has been well demonstrated to enable privacy-preserving SMS spam detection. Additionally, we have rigorously tested the adversarial robustness of SMS spam detection models, introducing the novel reverse backdoor attack, which has shown effectiveness and stealthiness in practical tests. | [
"['Yekai Li' 'Rufan Zhang' 'Wenxin Rong' 'Xianghang Mi']"
]
|
null | null | 2404.09491 | null | null | http://arxiv.org/pdf/2404.09491v2 | 2024-05-06T08:00:00Z | 2024-04-15T06:26:08Z | Large Language Models Can Automatically Engineer Features for Few-Shot
Tabular Learning | Large Language Models (LLMs), with their remarkable ability to tackle challenging and unseen reasoning problems, hold immense potential for tabular learning, that is vital for many real-world applications. In this paper, we propose a novel in-context learning framework, FeatLLM, which employs LLMs as feature engineers to produce an input data set that is optimally suited for tabular predictions. The generated features are used to infer class likelihood with a simple downstream machine learning model, such as linear regression and yields high performance few-shot learning. The proposed FeatLLM framework only uses this simple predictive model with the discovered features at inference time. Compared to existing LLM-based approaches, FeatLLM eliminates the need to send queries to the LLM for each sample at inference time. Moreover, it merely requires API-level access to LLMs, and overcomes prompt size limitations. As demonstrated across numerous tabular datasets from a wide range of domains, FeatLLM generates high-quality rules, significantly (10% on average) outperforming alternatives such as TabLLM and STUNT. | [
"['Sungwon Han' 'Jinsung Yoon' 'Sercan O Arik' 'Tomas Pfister']"
]
|
null | null | 2404.09494 | null | null | http://arxiv.org/pdf/2404.09494v3 | 2024-05-22T02:07:41Z | 2024-04-15T06:32:28Z | On the Necessity of Collaboration in Online Model Selection with
Decentralized Data | We consider online model selection with decentralized data over $M$ clients, and study the necessity of collaboration among clients. Previous work proposed various federated algorithms without demonstrating their necessity, while we answer the question from a novel perspective of computational constraints. We prove lower bounds on the regret, and propose a federated algorithm and analyze the upper bound. Our results show (i) collaboration is unnecessary in the absence of computational constraints on clients; (ii) collaboration is necessary if the computational cost on each client is limited to $o(K)$, where $K$ is the number of candidate hypothesis spaces. We clarify the unnecessary nature of collaboration in previous federated algorithms for distributed online multi-kernel learning, and improve the regret bounds at a smaller computational and communication cost. Our algorithm relies on three new techniques including an improved Bernstein's inequality for martingale, a federated online mirror descent framework, and decoupling model selection and prediction, which might be of independent interest. | [
"['Junfan Li' 'Zenglin Xu' 'Zheshun Wu' 'Irwin King']"
]
|
null | null | 2404.09510 | null | null | http://arxiv.org/pdf/2404.09510v1 | 2024-04-15T07:06:47Z | 2024-04-15T07:06:47Z | Listen to the Waves: Using a Neuronal Model of the Human Auditory System
to Predict Ocean Waves | Artificial neural networks (ANNs) have evolved from the 1940s primitive models of brain function to become tools for artificial intelligence. They comprise many units, artificial neurons, interlinked through weighted connections. ANNs are trained to perform tasks through learning rules that modify the connection weights. With these rules being in the focus of research, ANNs have become a branch of machine learning developing independently from neuroscience. Although likely required for the development of truly intelligent machines, the integration of neuroscience into ANNs has remained a neglected proposition. Here, we demonstrate that designing an ANN along biological principles results in drastically improved task performance. As a challenging real-world problem, we choose real-time ocean-wave prediction which is essential for various maritime operations. Motivated by the similarity of ocean waves measured at a single location to sound waves arriving at the eardrum, we redesign an echo state network to resemble the brain's auditory system. This yields a powerful predictive tool which is computationally lean, robust with respect to network parameters, and works efficiently across a wide range of sea states. Our results demonstrate the advantages of integrating neuroscience with machine learning and offer a tool for use in the production of green energy from ocean waves. | [
"['Artur Matysiak' 'Volker Roeber' 'Henrik Kalisch' 'Reinhard König'\n 'Patrick J. C. May']"
]
|
null | null | 2404.09516 | null | null | http://arxiv.org/pdf/2404.09516v1 | 2024-04-15T07:24:45Z | 2024-04-15T07:24:45Z | State Space Model for New-Generation Network Alternative to
Transformers: A Survey | In the post-deep learning era, the Transformer architecture has demonstrated its powerful performance across pre-trained big models and various downstream tasks. However, the enormous computational demands of this architecture have deterred many researchers. To further reduce the complexity of attention models, numerous efforts have been made to design more efficient methods. Among them, the State Space Model (SSM), as a possible replacement for the self-attention based Transformer model, has drawn more and more attention in recent years. In this paper, we give the first comprehensive review of these works and also provide experimental comparisons and analysis to better demonstrate the features and advantages of SSM. Specifically, we first give a detailed description of principles to help the readers quickly capture the key ideas of SSM. After that, we dive into the reviews of existing SSMs and their various applications, including natural language processing, computer vision, graph, multi-modal and multi-media, point cloud/event stream, time series data, and other domains. In addition, we give statistical comparisons and analysis of these models and hope it helps the readers to understand the effectiveness of different structures on various tasks. Then, we propose possible research points in this direction to better promote the development of the theoretical model and application of SSM. More related works will be continuously updated on the following GitHub: https://github.com/Event-AHU/Mamba_State_Space_Model_Paper_List. | [
"['Xiao Wang' 'Shiao Wang' 'Yuhe Ding' 'Yuehang Li' 'Wentao Wu' 'Yao Rong'\n 'Weizhe Kong' 'Ju Huang' 'Shihao Li' 'Haoxiang Yang' 'Ziwen Wang'\n 'Bo Jiang' 'Chenglong Li' 'Yaowei Wang' 'Yonghong Tian' 'Jin Tang']"
]
|
null | null | 2404.09519 | null | null | http://arxiv.org/pdf/2404.09519v1 | 2024-04-15T07:30:26Z | 2024-04-15T07:30:26Z | Nonlinear sparse variational Bayesian learning based model predictive
control with application to PEMFC temperature control | The accuracy of the underlying model predictions is crucial for the success of model predictive control (MPC) applications. If the model is unable to accurately analyze the dynamics of the controlled system, the performance and stability guarantees provided by MPC may not be achieved. Learning-based MPC can learn models from data, improving the applicability and reliability of MPC. This study develops a nonlinear sparse variational Bayesian learning based MPC (NSVB-MPC) for nonlinear systems, where the model is learned by the developed NSVB method. Variational inference is used by NSVB-MPC to assess the predictive accuracy and make the necessary corrections to quantify system uncertainty. The suggested approach ensures input-to-state (ISS) and the feasibility of recursive constraints in accordance with the concept of an invariant terminal region. Finally, a PEMFC temperature control model experiment confirms the effectiveness of the NSVB-MPC method. | [
"['Qi Zhang' 'Lei Wang' 'Weihua Xu' 'Hongye Su' 'Lei Xie']"
]
|
null | null | 2404.09521 | null | null | http://arxiv.org/pdf/2404.09521v1 | 2024-04-15T07:31:48Z | 2024-04-15T07:31:48Z | Inferring Behavior-Specific Context Improves Zero-Shot Generalization in
Reinforcement Learning | In this work, we address the challenge of zero-shot generalization (ZSG) in Reinforcement Learning (RL), where agents must adapt to entirely novel environments without additional training. We argue that understanding and utilizing contextual cues, such as the gravity level of the environment, is critical for robust generalization, and we propose to integrate the learning of context representations directly with policy learning. Our algorithm demonstrates improved generalization on various simulated domains, outperforming prior context-learning techniques in zero-shot settings. By jointly learning policy and context, our method acquires behavior-specific context representations, enabling adaptation to unseen environments and marks progress towards reinforcement learning systems that generalize across diverse real-world tasks. Our code and experiments are available at https://github.com/tidiane-camaret/contextual_rl_zero_shot. | [
"['Tidiane Camaret Ndir' 'André Biedenkapp' 'Noor Awad']"
]
|
null | null | 2404.09524 | null | null | http://arxiv.org/pdf/2404.09524v1 | 2024-04-15T07:41:35Z | 2024-04-15T07:41:35Z | Dynamic fault detection and diagnosis of industrial alkaline water
electrolyzer process with variational Bayesian dictionary learning | Alkaline Water Electrolysis (AWE) is one of the simplest green hydrogen production method using renewable energy. AWE system typically yields process variables that are serially correlated and contaminated by measurement uncertainty. A novel robust dynamic variational Bayesian dictionary learning (RDVDL) monitoring approach is proposed to improve the reliability and safety of AWE operation. RDVDL employs a sparse Bayesian dictionary learning to preserve the dynamic mechanism information of AWE process which allows the easy interpretation of fault detection results. To improve the robustness to measurement uncertainty, a low-rank vector autoregressive (VAR) method is derived to reliably extract the serial correlation from process variables. The effectiveness of the proposed approach is demonstrated with an industrial hydrogen production process, and RDVDL can efficiently detect and diagnose critical AWE faults. | [
"['Qi Zhang' 'Lei Xie' 'Weihua Xu' 'Hongye Su']"
]
|
null | null | 2404.09526 | null | null | http://arxiv.org/pdf/2404.09526v1 | 2024-04-15T07:45:04Z | 2024-04-15T07:45:04Z | LoongServe: Efficiently Serving Long-context Large Language Models with
Elastic Sequence Parallelism | The context window of large language models (LLMs) is rapidly increasing, leading to a huge variance in resource usage between different requests as well as between different phases of the same request. Restricted by static parallelism strategies, existing LLM serving systems cannot efficiently utilize the underlying resources to serve variable-length requests in different phases. To address this problem, we propose a new parallelism paradigm, elastic sequence parallelism (ESP), to elastically adapt to the variance between different requests and phases. Based on ESP, we design and build LoongServe, an LLM serving system that (1) improves computation efficiency by elastically adjusting the degree of parallelism in real-time, (2) improves communication efficiency by reducing key-value cache migration overhead and overlapping partial decoding communication with computation, and (3) improves GPU memory efficiency by reducing key-value cache fragmentation across instances. Our evaluation under diverse real-world datasets shows that LoongServe improves the maximum throughput by up to 3.85$times$ compared to the chunked prefill and 5.81$times$ compared to the prefill-decoding disaggregation. | [
"['Bingyang Wu' 'Shengyu Liu' 'Yinmin Zhong' 'Peng Sun' 'Xuanzhe Liu'\n 'Xin Jin']"
]
|
null | null | 2404.09529 | null | null | http://arxiv.org/pdf/2404.09529v1 | 2024-04-15T07:49:10Z | 2024-04-15T07:49:10Z | Prepacking: A Simple Method for Fast Prefilling and Increased Throughput
in Large Language Models | During inference for transformer-based large language models (LLM), prefilling is the computation of the key-value (KV) cache for input tokens in the prompt prior to autoregressive generation. For longer input prompt lengths, prefilling will incur a significant overhead on decoding time. In this work, we highlight the following pitfall of prefilling: for batches containing high-varying prompt lengths, significant computation is wasted by the standard practice of padding sequences to the maximum length. As LLMs increasingly support longer context lengths, potentially up to 10 million tokens, variations in prompt lengths within a batch become more pronounced. To address this, we propose Prepacking, a simple yet effective method to optimize prefilling computation. To avoid redundant computation on pad tokens, prepacking combines prompts of varying lengths into a sequence and packs multiple sequences into a compact batch using a bin-packing algorithm. It then modifies the attention mask and positional encoding to compute multiple prefilled KV-caches for multiple prompts within a single sequence. On standard curated dataset containing prompts with varying lengths, we obtain a significant speed and memory efficiency improvements as compared to the default padding-based prefilling computation within Huggingface across a range of base model configurations and inference serving scenarios. | [
"['Siyan Zhao' 'Daniel Israel' 'Guy Van den Broeck' 'Aditya Grover']"
]
|
null | null | 2404.09532 | null | null | http://arxiv.org/pdf/2404.09532v1 | 2024-04-15T07:51:40Z | 2024-04-15T07:51:40Z | TMPQ-DM: Joint Timestep Reduction and Quantization Precision Selection
for Efficient Diffusion Models | Diffusion models have emerged as preeminent contenders in the realm of generative models. Distinguished by their distinctive sequential generative processes, characterized by hundreds or even thousands of timesteps, diffusion models progressively reconstruct images from pure Gaussian noise, with each timestep necessitating full inference of the entire model. However, the substantial computational demands inherent to these models present challenges for deployment, quantization is thus widely used to lower the bit-width for reducing the storage and computing overheads. Current quantization methodologies primarily focus on model-side optimization, disregarding the temporal dimension, such as the length of the timestep sequence, thereby allowing redundant timesteps to continue consuming computational resources, leaving substantial scope for accelerating the generative process. In this paper, we introduce TMPQ-DM, which jointly optimizes timestep reduction and quantization to achieve a superior performance-efficiency trade-off, addressing both temporal and model optimization aspects. For timestep reduction, we devise a non-uniform grouping scheme tailored to the non-uniform nature of the denoising process, thereby mitigating the explosive combinations of timesteps. In terms of quantization, we adopt a fine-grained layer-wise approach to allocate varying bit-widths to different layers based on their respective contributions to the final generative performance, thus rectifying performance degradation observed in prior studies. To expedite the evaluation of fine-grained quantization, we further devise a super-network to serve as a precision solver by leveraging shared quantization results. These two design components are seamlessly integrated within our framework, enabling rapid joint exploration of the exponentially large decision space via a gradient-free evolutionary search algorithm. | [
"['Haojun Sun' 'Chen Tang' 'Zhi Wang' 'Yuan Meng' 'Jingyan jiang'\n 'Xinzhu Ma' 'Wenwu Zhu']"
]
|
null | null | 2404.09533 | null | null | http://arxiv.org/pdf/2404.09533v2 | 2024-04-29T04:58:20Z | 2024-04-15T07:53:07Z | WiTUnet: A U-Shaped Architecture Integrating CNN and Transformer for
Improved Feature Alignment and Local Information Fusion | Low-dose computed tomography (LDCT) has become the technology of choice for diagnostic medical imaging, given its lower radiation dose compared to standard CT, despite increasing image noise and potentially affecting diagnostic accuracy. To address this, advanced deep learning-based LDCT denoising algorithms have been developed, primarily using Convolutional Neural Networks (CNNs) or Transformer Networks with the Unet architecture. This architecture enhances image detail by integrating feature maps from the encoder and decoder via skip connections. However, current methods often overlook enhancements to the Unet architecture itself, focusing instead on optimizing encoder and decoder structures. This approach can be problematic due to the significant differences in feature map characteristics between the encoder and decoder, where simple fusion strategies may not effectively reconstruct images.In this paper, we introduce WiTUnet, a novel LDCT image denoising method that utilizes nested, dense skip pathways instead of traditional skip connections to improve feature integration. WiTUnet also incorporates a windowed Transformer structure to process images in smaller, non-overlapping segments, reducing computational load. Additionally, the integration of a Local Image Perception Enhancement (LiPe) module in both the encoder and decoder replaces the standard multi-layer perceptron (MLP) in Transformers, enhancing local feature capture and representation. Through extensive experimental comparisons, WiTUnet has demonstrated superior performance over existing methods in key metrics such as Peak Signal-to-Noise Ratio (PSNR), Structural Similarity (SSIM), and Root Mean Square Error (RMSE), significantly improving noise removal and image quality. | [
"['Bin Wang' 'Fei Deng' 'Peifan Jiang' 'Shuang Wang' 'Xiao Han'\n 'Zhixuan Zhang']"
]
|
null | null | 2404.09536 | null | null | http://arxiv.org/pdf/2404.09536v1 | 2024-04-15T07:59:11Z | 2024-04-15T07:59:11Z | Beyond Noise: Privacy-Preserving Decentralized Learning with Virtual
Nodes | Decentralized learning (DL) enables collaborative learning without a server and without training data leaving the users' devices. However, the models shared in DL can still be used to infer training data. Conventional privacy defenses such as differential privacy and secure aggregation fall short in effectively safeguarding user privacy in DL. We introduce Shatter, a novel DL approach in which nodes create virtual nodes (VNs) to disseminate chunks of their full model on their behalf. This enhances privacy by (i) preventing attackers from collecting full models from other nodes, and (ii) hiding the identity of the original node that produced a given model chunk. We theoretically prove the convergence of Shatter and provide a formal analysis demonstrating how Shatter reduces the efficacy of attacks compared to when exchanging full models between participating nodes. We evaluate the convergence and attack resilience of Shatter with existing DL algorithms, with heterogeneous datasets, and against three standard privacy attacks, including gradient inversion. Our evaluation shows that Shatter not only renders these privacy attacks infeasible when each node operates 16 VNs but also exhibits a positive impact on model convergence compared to standard DL. This enhanced privacy comes with a manageable increase in communication volume. | [
"['Sayan Biswas' 'Mathieu Even' 'Anne-Marie Kermarrec' 'Laurent Massoulie'\n 'Rafael Pires' 'Rishi Sharma' 'Martijn de Vos']"
]
|
null | null | 2404.09541 | null | null | http://arxiv.org/pdf/2404.09541v1 | 2024-04-15T08:06:54Z | 2024-04-15T08:06:54Z | Application of the representative measure approach to assess the
reliability of decision trees in dealing with unseen vehicle collision data | Machine learning algorithms are fundamental components of novel data-informed Artificial Intelligence architecture. In this domain, the imperative role of representative datasets is a cornerstone in shaping the trajectory of artificial intelligence (AI) development. Representative datasets are needed to train machine learning components properly. Proper training has multiple impacts: it reduces the final model's complexity, power, and uncertainties. In this paper, we investigate the reliability of the $varepsilon$-representativeness method to assess the dataset similarity from a theoretical perspective for decision trees. We decided to focus on the family of decision trees because it includes a wide variety of models known to be explainable. Thus, in this paper, we provide a result guaranteeing that if two datasets are related by $varepsilon$-representativeness, i.e., both of them have points closer than $varepsilon$, then the predictions by the classic decision tree are similar. Experimentally, we have also tested that $varepsilon$-representativeness presents a significant correlation with the ordering of the feature importance. Moreover, we extend the results experimentally in the context of unseen vehicle collision data for XGboost, a machine-learning component widely adopted for dealing with tabular data. | [
"['Javier Perera-Lago' 'Víctor Toscano-Durán' 'Eduardo Paluzo-Hidalgo'\n 'Sara Narteni' 'Matteo Rucco']"
]
|
null | null | 2404.09544 | null | null | http://arxiv.org/pdf/2404.09544v1 | 2024-04-15T08:11:21Z | 2024-04-15T08:11:21Z | GNNavigator: Towards Adaptive Training of Graph Neural Networks via
Automatic Guideline Exploration | Graph Neural Networks (GNNs) succeed significantly in many applications recently. However, balancing GNNs training runtime cost, memory consumption, and attainable accuracy for various applications is non-trivial. Previous training methodologies suffer from inferior adaptability and lack a unified training optimization solution. To address the problem, this work proposes GNNavigator, an adaptive GNN training configuration optimization framework. GNNavigator meets diverse GNN application requirements due to our unified software-hardware co-abstraction, proposed GNNs training performance model, and practical design space exploration solution. Experimental results show that GNNavigator can achieve up to 3.1x speedup and 44.9% peak memory reduction with comparable accuracy to state-of-the-art approaches. | [
"['Tong Qiao' 'Jianlei Yang' 'Yingjie Qi' 'Ao Zhou' 'Chen Bai' 'Bei Yu'\n 'Weisheng Zhao' 'Chunming Hu']"
]
|
null | null | 2404.09562 | null | null | http://arxiv.org/pdf/2404.09562v2 | 2024-07-01T06:46:36Z | 2024-04-15T08:22:47Z | σ-GPTs: A New Approach to Autoregressive Models | Autoregressive models, such as the GPT family, use a fixed order, usually left-to-right, to generate sequences. However, this is not a necessity. In this paper, we challenge this assumption and show that by simply adding a positional encoding for the output, this order can be modulated on-the-fly per-sample which offers key advantageous properties. It allows for the sampling of and conditioning on arbitrary subsets of tokens, and it also allows sampling in one shot multiple tokens dynamically according to a rejection strategy, leading to a sub-linear number of model evaluations. We evaluate our method across various domains, including language modeling, path-solving, and aircraft vertical rate prediction, decreasing the number of steps required for generation by an order of magnitude. | [
"['Arnaud Pannatier' 'Evann Courdier' 'François Fleuret']"
]
|
null | null | 2404.09565 | null | null | http://arxiv.org/pdf/2404.09565v1 | 2024-04-15T08:27:47Z | 2024-04-15T08:27:47Z | Reliability Estimation of News Media Sources: Birds of a Feather Flock
Together | Evaluating the reliability of news sources is a routine task for journalists and organizations committed to acquiring and disseminating accurate information. Recent research has shown that predicting sources' reliability represents an important first-prior step in addressing additional challenges such as fake news detection and fact-checking. In this paper, we introduce a novel approach for source reliability estimation that leverages reinforcement learning strategies for estimating the reliability degree of news sources. Contrary to previous research, our proposed approach models the problem as the estimation of a reliability degree, and not a reliability label, based on how all the news media sources interact with each other on the Web. We validated the effectiveness of our method on a news media reliability dataset that is an order of magnitude larger than comparable existing datasets. Results show that the estimated reliability degrees strongly correlates with journalists-provided scores (Spearman=0.80) and can effectively predict reliability labels (macro-avg. F$_1$ score=81.05). We release our implementation and dataset, aiming to provide a valuable resource for the NLP community working on information verification. | [
"['Sergio Burdisso' 'Dairazalia Sánchez-Cortés' 'Esaú Villatoro-Tello'\n 'Petr Motlicek']"
]
|
null | null | 2404.09574 | null | null | http://arxiv.org/pdf/2404.09574v1 | 2024-04-15T08:36:40Z | 2024-04-15T08:36:40Z | Predicting and Analyzing Pedestrian Crossing Behavior at Unsignalized
Crossings | Understanding and predicting pedestrian crossing behavior is essential for enhancing automated driving and improving driving safety. Predicting gap selection behavior and the use of zebra crossing enables driving systems to proactively respond and prevent potential conflicts. This task is particularly challenging at unsignalized crossings due to the ambiguous right of way, requiring pedestrians to constantly interact with vehicles and other pedestrians. This study addresses these challenges by utilizing simulator data to investigate scenarios involving multiple vehicles and pedestrians. We propose and evaluate machine learning models to predict gap selection in non-zebra scenarios and zebra crossing usage in zebra scenarios. We investigate and discuss how pedestrians' behaviors are influenced by various factors, including pedestrian waiting time, walking speed, the number of unused gaps, the largest missed gap, and the influence of other pedestrians. This research contributes to the evolution of intelligent vehicles by providing predictive models and valuable insights into pedestrian crossing behavior. | [
"['Chi Zhang' 'Janis Sprenger' 'Zhongjun Ni' 'Christian Berger']"
]
|
null | null | 2404.09586 | null | null | http://arxiv.org/pdf/2404.09586v4 | 2024-06-15T11:14:36Z | 2024-04-15T08:54:33Z | Mitigating the Curse of Dimensionality for Certified Robustness via Dual
Randomized Smoothing | Randomized Smoothing (RS) has been proven a promising method for endowing an arbitrary image classifier with certified robustness. However, the substantial uncertainty inherent in the high-dimensional isotropic Gaussian noise imposes the curse of dimensionality on RS. Specifically, the upper bound of ${ell_2}$ certified robustness radius provided by RS exhibits a diminishing trend with the expansion of the input dimension $d$, proportionally decreasing at a rate of $1/sqrt{d}$. This paper explores the feasibility of providing ${ell_2}$ certified robustness for high-dimensional input through the utilization of dual smoothing in the lower-dimensional space. The proposed Dual Randomized Smoothing (DRS) down-samples the input image into two sub-images and smooths the two sub-images in lower dimensions. Theoretically, we prove that DRS guarantees a tight ${ell_2}$ certified robustness radius for the original input and reveal that DRS attains a superior upper bound on the ${ell_2}$ robustness radius, which decreases proportionally at a rate of $(1/sqrt m + 1/sqrt n )$ with $m+n=d$. Extensive experiments demonstrate the generalizability and effectiveness of DRS, which exhibits a notable capability to integrate with established methodologies, yielding substantial improvements in both accuracy and ${ell_2}$ certified robustness baselines of RS on the CIFAR-10 and ImageNet datasets. Code is available at https://github.com/xiasong0501/DRS. | [
"['Song Xia' 'Yi Yu' 'Xudong Jiang' 'Henghui Ding']"
]
|
null | null | 2404.09601 | null | null | http://arxiv.org/pdf/2404.09601v1 | 2024-04-15T09:16:49Z | 2024-04-15T09:16:49Z | Reactive Model Correction: Mitigating Harm to Task-Relevant Features via
Conditional Bias Suppression | Deep Neural Networks are prone to learning and relying on spurious correlations in the training data, which, for high-risk applications, can have fatal consequences. Various approaches to suppress model reliance on harmful features have been proposed that can be applied post-hoc without additional training. Whereas those methods can be applied with efficiency, they also tend to harm model performance by globally shifting the distribution of latent features. To mitigate unintended overcorrection of model behavior, we propose a reactive approach conditioned on model-derived knowledge and eXplainable Artificial Intelligence (XAI) insights. While the reactive approach can be applied to many post-hoc methods, we demonstrate the incorporation of reactivity in particular for P-ClArC (Projective Class Artifact Compensation), introducing a new method called R-ClArC (Reactive Class Artifact Compensation). Through rigorous experiments in controlled settings (FunnyBirds) and with a real-world dataset (ISIC2019), we show that introducing reactivity can minimize the detrimental effect of the applied correction while simultaneously ensuring low reliance on spurious features. | [
"['Dilyara Bareeva' 'Maximilian Dreyer' 'Frederik Pahde' 'Wojciech Samek'\n 'Sebastian Lapuschkin']"
]
|
null | null | 2404.09604 | null | null | http://arxiv.org/pdf/2404.09604v2 | 2024-05-17T08:58:24Z | 2024-04-15T09:22:46Z | Machine learning-based optimization workflow of the homogeneity of
spunbond nonwovens with human validation | In the last ten years, the average annual growth rate of nonwoven production was 4%. In 2020 and 2021, nonwoven production has increased even further due to the huge demand for nonwoven products needed for protective clothing such as FFP2 masks to combat the COVID19 pandemic. Optimizing the production process is still a challenge due to its high nonlinearity. In this paper, we present a machine learning-based optimization workflow aimed at improving the homogeneity of spunbond nonwovens. The optimization workflow is based on a mathematical model that simulates the microstructures of nonwovens. Based on trainingy data coming from this simulator, different machine learning algorithms are trained in order to find a surrogate model for the time-consuming simulator. Human validation is employed to verify the outputs of machine learning algorithms by assessing the aesthetics of the nonwovens. We include scientific and expert knowledge into the training data to reduce the computational costs involved in the optimization process. We demonstrate the necessity and effectiveness of our workflow in optimizing the homogeneity of nonwovens. | [
"['Viny Saajan Victor' 'Andre Schmeißer' 'Heike Leitte' 'Simone Gramsch']"
]
|
null | null | 2404.09606 | null | null | http://arxiv.org/pdf/2404.09606v1 | 2024-04-15T09:26:33Z | 2024-04-15T09:26:33Z | A Self-feedback Knowledge Elicitation Approach for Chemical Reaction
Predictions | The task of chemical reaction predictions (CRPs) plays a pivotal role in advancing drug discovery and material science. However, its effectiveness is constrained by the vast and uncertain chemical reaction space and challenges in capturing reaction selectivity, particularly due to existing methods' limitations in exploiting the data's inherent knowledge. To address these challenges, we introduce a data-curated self-feedback knowledge elicitation approach. This method starts from iterative optimization of molecular representations and facilitates the extraction of knowledge on chemical reaction types (RTs). Then, we employ adaptive prompt learning to infuse the prior knowledge into the large language model (LLM). As a result, we achieve significant enhancements: a 14.2% increase in retrosynthesis prediction accuracy, a 74.2% rise in reagent prediction accuracy, and an expansion in the model's capability for handling multi-task chemical reactions. This research offers a novel paradigm for knowledge elicitation in scientific research and showcases the untapped potential of LLMs in CRPs. | [
"['Pengfei Liu' 'Jun Tao' 'Zhixiang Ren']"
]
|
null | null | 2404.09610 | null | null | http://arxiv.org/pdf/2404.09610v1 | 2024-04-15T09:32:12Z | 2024-04-15T09:32:12Z | LoRA Dropout as a Sparsity Regularizer for Overfitting Control | Parameter-efficient fine-tuning methods, represented by LoRA, play an essential role in adapting large-scale pre-trained models to downstream tasks. However, fine-tuning LoRA-series models also faces the risk of overfitting on the training dataset, and yet there's still a lack of theoretical guidance and practical mechanism to control overfitting on LoRA-based PEFT methods. In this paper, we propose a LoRA Dropout mechanism for the LoRA-based methods by introducing random noises to the learnable low-rank matrices and increasing parameter sparsity. We then demonstrate the theoretical mechanism of our LoRA Dropout mechanism from the perspective of sparsity regularization by providing a generalization error bound under this framework. Theoretical results show that appropriate sparsity would help tighten the gap between empirical and generalization risks and thereby control overfitting. Furthermore, based on the LoRA Dropout framework, we introduce a test-time ensemble strategy and provide theoretical evidence demonstrating that the ensemble method can further compress the error bound, and lead to better performance during inference time. Extensive experiments on various NLP tasks provide practical validations of the effectiveness of our LoRA Dropout framework in improving model accuracy and calibration. | [
"['Yang Lin' 'Xinyu Ma' 'Xu Chu' 'Yujie Jin' 'Zhibang Yang' 'Yasha Wang'\n 'Hong Mei']"
]
|
null | null | 2404.09616 | null | null | http://arxiv.org/pdf/2404.09616v1 | 2024-04-15T09:40:44Z | 2024-04-15T09:40:44Z | A Review and Efficient Implementation of Scene Graph Generation Metrics | Scene graph generation has emerged as a prominent research field in computer vision, witnessing significant advancements in the recent years. However, despite these strides, precise and thorough definitions for the metrics used to evaluate scene graph generation models are lacking. In this paper, we address this gap in the literature by providing a review and precise definition of commonly used metrics in scene graph generation. Our comprehensive examination clarifies the underlying principles of these metrics and can serve as a reference or introduction to scene graph metrics. Furthermore, to facilitate the usage of these metrics, we introduce a standalone Python package called SGBench that efficiently implements all defined metrics, ensuring their accessibility to the research community. Additionally, we present a scene graph benchmarking web service, that enables researchers to compare scene graph generation methods and increase visibility of new methods in a central place. All of our code can be found at https://lorjul.github.io/sgbench/. | [
"['Julian Lorenz' 'Robin Schön' 'Katja Ludwig' 'Rainer Lienhart']"
]
|
null | null | 2404.09617 | null | null | http://arxiv.org/pdf/2404.09617v2 | 2024-05-13T06:01:27Z | 2024-04-15T09:46:12Z | Safeguarding adaptive methods: global convergence of Barzilai-Borwein
and other stepsize choices | Leveraging on recent advancements on adaptive methods for convex minimization problems, this paper provides a linesearch-free proximal gradient framework for globalizing the convergence of popular stepsize choices such as Barzilai-Borwein and one-dimensional Anderson acceleration. This framework can cope with problems in which the gradient of the differentiable function is merely locally H"older continuous. Our analysis not only encompasses but also refines existing results upon which it builds. The theory is corroborated by numerical evidence that showcases the synergetic interplay between fast stepsize selections and adaptive methods. | [
"['Hongjia Ou' 'Andreas Themelis']"
]
|
null | null | 2404.09625 | null | null | http://arxiv.org/pdf/2404.09625v1 | 2024-04-15T09:56:36Z | 2024-04-15T09:56:36Z | Privacy-Preserving Intrusion Detection using Convolutional Neural
Networks | Privacy-preserving analytics is designed to protect valuable assets. A common service provision involves the input data from the client and the model on the analyst's side. The importance of the privacy preservation is fuelled by legal obligations and intellectual property concerns. We explore the use case of a model owner providing an analytic service on customer's private data. No information about the data shall be revealed to the analyst and no information about the model shall be leaked to the customer. Current methods involve costs: accuracy deterioration and computational complexity. The complexity, in turn, results in a longer processing time, increased requirement on computing resources, and involves data communication between the client and the server. In order to deploy such service architecture, we need to evaluate the optimal setting that fits the constraints. And that is what this paper addresses. In this work, we enhance an attack detection system based on Convolutional Neural Networks with privacy-preserving technology based on PriMIA framework that is initially designed for medical data. | [
"['Martin Kodys' 'Zhongmin Dai' 'Vrizlynn L. L. Thing']"
]
|
null | null | 2404.09632 | null | null | http://arxiv.org/pdf/2404.09632v1 | 2024-04-15T10:04:15Z | 2024-04-15T10:04:15Z | Bridging Vision and Language Spaces with Assignment Prediction | This paper introduces VLAP, a novel approach that bridges pretrained vision models and large language models (LLMs) to make frozen LLMs understand the visual world. VLAP transforms the embedding space of pretrained vision models into the LLMs' word embedding space using a single linear layer for efficient and general-purpose visual and language understanding. Specifically, we harness well-established word embeddings to bridge two modality embedding spaces. The visual and text representations are simultaneously assigned to a set of word embeddings within pretrained LLMs by formulating the assigning procedure as an optimal transport problem. We predict the assignment of one modality from the representation of another modality data, enforcing consistent assignments for paired multimodal data. This allows vision and language representations to contain the same information, grounding the frozen LLMs' word embedding space in visual data. Moreover, a robust semantic taxonomy of LLMs can be preserved with visual data since the LLMs interpret and reason linguistic information from correlations between word embeddings. Experimental results show that VLAP achieves substantial improvements over the previous linear transformation-based approaches across a range of vision-language tasks, including image captioning, visual question answering, and cross-modal retrieval. We also demonstrate the learned visual representations hold a semantic taxonomy of LLMs, making visual semantic arithmetic possible. | [
"['Jungin Park' 'Jiyoung Lee' 'Kwanghoon Sohn']"
]
|
null | null | 2404.09636 | null | null | http://arxiv.org/pdf/2404.09636v3 | 2024-07-15T07:45:28Z | 2024-04-15T10:12:33Z | All-in-one simulation-based inference | Amortized Bayesian inference trains neural networks to solve stochastic inference problems using model simulations, thereby making it possible to rapidly perform Bayesian inference for any newly observed data. However, current simulation-based amortized inference methods are simulation-hungry and inflexible: They require the specification of a fixed parametric prior, simulator, and inference tasks ahead of time. Here, we present a new amortized inference method -- the Simformer -- which overcomes these limitations. By training a probabilistic diffusion model with transformer architectures, the Simformer outperforms current state-of-the-art amortized inference approaches on benchmark tasks and is substantially more flexible: It can be applied to models with function-valued parameters, it can handle inference scenarios with missing or unstructured data, and it can sample arbitrary conditionals of the joint distribution of parameters and data, including both posterior and likelihood. We showcase the performance and flexibility of the Simformer on simulators from ecology, epidemiology, and neuroscience, and demonstrate that it opens up new possibilities and application domains for amortized Bayesian inference on simulation-based models. | [
"['Manuel Gloeckler' 'Michael Deistler' 'Christian Weilbach' 'Frank Wood'\n 'Jakob H. Macke']"
]
|
null | null | 2404.09656 | null | null | http://arxiv.org/pdf/2404.09656v2 | 2024-05-21T15:04:12Z | 2024-04-15T10:44:31Z | Learn Your Reference Model for Real Good Alignment | The complexity of the alignment problem stems from the fact that existing methods are considered unstable. Reinforcement Learning from Human Feedback (RLHF) addresses this issue by minimizing the KL divergence between the trained policy and the initial supervised fine-tuned policy (SFT) to avoid generating out-of-domain samples for the reward model (RM). Recently, many methods have emerged that shift from online to offline optimization, reformulating the RLHF objective and removing the reward model (DPO, IPO, KTO). Despite eliminating the reward model and the challenges it posed, these algorithms are still constrained in terms of closeness of the trained policy to the SFT one. In our paper, we argue that this implicit limitation in the offline optimization methods leads to suboptimal results. To address this issue, we propose a class of new methods called Trust Region (TR-DPO, TR-IPO, TR-KTO), which update the reference policy during training. With this straightforward update approach, we demonstrate the effectiveness of the new paradigm of language model alignment against the classical one on the Anthropic-HH and Reddit TL;DR datasets. Most notably, when automatically comparing TR methods and baselines side by side using pretrained Pythia 6.9B models on the Reddit TL;DR task, the difference in win rates reaches 8.4% for DPO, 14.3% for IPO, and 15% for KTO. Finally, by assessing model response ratings grounded on criteria such as coherence, correctness, helpfulness, and harmlessness, we demonstrate that our proposed methods significantly outperform existing techniques. | [
"['Alexey Gorbatovski' 'Boris Shaposhnikov' 'Alexey Malakhov'\n 'Nikita Surnachev' 'Yaroslav Aksenov' 'Ian Maksimov' 'Nikita Balagansky'\n 'Daniil Gavrilov']"
]
|
null | null | 2404.09657 | null | null | http://arxiv.org/pdf/2404.09657v2 | 2024-07-09T14:31:07Z | 2024-04-15T10:45:12Z | Sampling for Model Predictive Trajectory Planning in Autonomous Driving
using Normalizing Flows | Alongside optimization-based planners, sampling-based approaches are often used in trajectory planning for autonomous driving due to their simplicity. Model predictive path integral control is a framework that builds upon optimization principles while incorporating stochastic sampling of input trajectories. This paper investigates several sampling approaches for trajectory generation. In this context, normalizing flows originating from the field of variational inference are considered for the generation of sampling distributions, as they model transformations of simple to more complex distributions. Accordingly, learning-based normalizing flow models are trained for a more efficient exploration of the input domain for the task at hand. The developed algorithm and the proposed sampling distributions are evaluated in two simulation scenarios. | [
"['Georg Rabenstein' 'Lars Ullrich' 'Knut Graichen']"
]
|
null | null | 2404.09664 | null | null | http://arxiv.org/pdf/2404.09664v1 | 2024-04-15T10:54:47Z | 2024-04-15T10:54:47Z | Closing the Gap in the Trade-off between Fair Representations and
Accuracy | The rapid developments of various machine learning models and their deployments in several applications has led to discussions around the importance of looking beyond the accuracies of these models. Fairness of such models is one such aspect that is deservedly gaining more attention. In this work, we analyse the natural language representations of documents and sentences (i.e., encodings) for any embedding-level bias that could potentially also affect the fairness of the downstream tasks that rely on them. We identify bias in these encodings either towards or against different sub-groups based on the difference in their reconstruction errors along various subsets of principal components. We explore and recommend ways to mitigate such bias in the encodings while also maintaining a decent accuracy in classification models that use them. | [
"['Biswajit Rout' 'Ananya B. Sai' 'Arun Rajkumar']"
]
|
null | null | 2404.09679 | null | null | http://arxiv.org/pdf/2404.09679v1 | 2024-04-15T11:20:44Z | 2024-04-15T11:20:44Z | AntDT: A Self-Adaptive Distributed Training Framework for Leader and
Straggler Nodes | Many distributed training techniques like Parameter Server and AllReduce have been proposed to take advantage of the increasingly large data and rich features. However, stragglers frequently occur in distributed training due to resource contention and hardware heterogeneity, which significantly hampers the training efficiency. Previous works only address part of the stragglers and could not adaptively solve various stragglers in practice. Additionally, it is challenging to use a systematic framework to address all stragglers because different stragglers require diverse data allocation and fault-tolerance mechanisms. Therefore, this paper proposes a unified distributed training framework called AntDT (Ant Distributed Training Framework) to adaptively solve the straggler problems. Firstly, the framework consists of four components, including the Stateful Dynamic Data Sharding service, Monitor, Controller, and Agent. These components work collaboratively to efficiently distribute workloads and provide a range of pre-defined straggler mitigation methods with fault tolerance, thereby hiding messy details of data allocation and fault handling. Secondly, the framework provides a high degree of flexibility, allowing for the customization of straggler mitigation solutions based on the specific circumstances of the cluster. Leveraging this flexibility, we introduce two straggler mitigation solutions, namely AntDT-ND for non-dedicated clusters and AntDT-DD for dedicated clusters, as practical examples to resolve various types of stragglers at Ant Group. Justified by our comprehensive experiments and industrial deployment statistics, AntDT outperforms other SOTA methods more than 3x in terms of training efficiency. Additionally, in Alipay's homepage recommendation scenario, using AntDT reduces the training duration of the ranking model from 27.8 hours to just 5.4 hours. | [
"['Youshao Xiao' 'Lin Ju' 'Zhenglei Zhou' 'Siyuan Li' 'Zhaoxin Huan'\n 'Dalong Zhang' 'Rujie Jiang' 'Lin Wang' 'Xiaolu Zhang' 'Lei Liang'\n 'Jun Zhou']"
]
|
null | null | 2404.09683 | null | null | http://arxiv.org/pdf/2404.09683v2 | 2024-04-18T14:51:55Z | 2024-04-15T11:36:31Z | Post-Training Network Compression for 3D Medical Image Segmentation:
Reducing Computational Efforts via Tucker Decomposition | We address the computational barrier of deploying advanced deep learning segmentation models in clinical settings by studying the efficacy of network compression through tensor decomposition. We propose a post-training Tucker factorization that enables the decomposition of pre-existing models to reduce computational requirements without impeding segmentation accuracy. We applied Tucker decomposition to the convolutional kernels of the TotalSegmentator (TS) model, an nnU-Net model trained on a comprehensive dataset for automatic segmentation of 117 anatomical structures. Our approach reduced the floating-point operations (FLOPs) and memory required during inference, offering an adjustable trade-off between computational efficiency and segmentation quality. This study utilized the publicly available TS dataset, employing various downsampling factors to explore the relationship between model size, inference speed, and segmentation performance. The application of Tucker decomposition to the TS model substantially reduced the model parameters and FLOPs across various compression rates, with limited loss in segmentation accuracy. We removed up to 88% of the model's parameters with no significant performance changes in the majority of classes after fine-tuning. Practical benefits varied across different graphics processing unit (GPU) architectures, with more distinct speed-ups on less powerful hardware. Post-hoc network compression via Tucker decomposition presents a viable strategy for reducing the computational demand of medical image segmentation models without substantially sacrificing accuracy. This approach enables the broader adoption of advanced deep learning technologies in clinical practice, offering a way to navigate the constraints of hardware capabilities. | [
"['Tobias Weber' 'Jakob Dexl' 'David Rügamer' 'Michael Ingrisch']"
]
|
null | null | 2404.09686 | null | null | http://arxiv.org/pdf/2404.09686v1 | 2024-04-15T11:37:40Z | 2024-04-15T11:37:40Z | AntBatchInfer: Elastic Batch Inference in the Kubernetes Cluster | Offline batch inference is a common task in the industry for deep learning applications, but it can be challenging to ensure stability and performance when dealing with large amounts of data and complicated inference pipelines. This paper demonstrated AntBatchInfer, an elastic batch inference framework, which is specially optimized for the non-dedicated cluster. AntBatchInfer addresses these challenges by providing multi-level fault-tolerant capabilities, enabling the stable execution of versatile and long-running inference tasks. It also improves inference efficiency by pipelining, intra-node, and inter-node scaling. It further optimizes the performance in complicated multiple-model batch inference scenarios. Through extensive experiments and real-world statistics, we demonstrate the superiority of our framework in terms of stability and efficiency. In the experiment, it outperforms the baseline by at least $2times$ and $6times$ in the single-model or multiple-model batch inference. Also, it is widely used at Ant Group, with thousands of daily jobs from various scenarios, including DLRM, CV, and NLP, which proves its practicability in the industry. | [
"['Siyuan Li' 'Youshao Xiao' 'Fanzhuang Meng' 'Lin Ju' 'Lei Liang'\n 'Lin Wang' 'Jun Zhou']"
]
|
null | null | 2404.09690 | null | null | http://arxiv.org/pdf/2404.09690v1 | 2024-04-15T11:45:30Z | 2024-04-15T11:45:30Z | Harnessing GPT-4V(ision) for Insurance: A Preliminary Exploration | The emergence of Large Multimodal Models (LMMs) marks a significant milestone in the development of artificial intelligence. Insurance, as a vast and complex discipline, involves a wide variety of data forms in its operational processes, including text, images, and videos, thereby giving rise to diverse multimodal tasks. Despite this, there has been limited systematic exploration of multimodal tasks specific to insurance, nor a thorough investigation into how LMMs can address these challenges. In this paper, we explore GPT-4V's capabilities in the insurance domain. We categorize multimodal tasks by focusing primarily on visual aspects based on types of insurance (e.g., auto, household/commercial property, health, and agricultural insurance) and insurance stages (e.g., risk assessment, risk monitoring, and claims processing). Our experiment reveals that GPT-4V exhibits remarkable abilities in insurance-related tasks, demonstrating not only a robust understanding of multimodal content in the insurance domain but also a comprehensive knowledge of insurance scenarios. However, there are notable shortcomings: GPT-4V struggles with detailed risk rating and loss assessment, suffers from hallucination in image understanding, and shows variable support for different languages. Through this work, we aim to bridge the insurance domain with cutting-edge LMM technology, facilitate interdisciplinary exchange and development, and provide a foundation for the continued advancement and evolution of future research endeavors. | [
"['Chenwei Lin' 'Hanjia Lyu' 'Jiebo Luo' 'Xian Xu']"
]
|
null | null | 2404.09695 | null | null | http://arxiv.org/pdf/2404.09695v1 | 2024-04-15T11:53:22Z | 2024-04-15T11:53:22Z | LoRAP: Transformer Sub-Layers Deserve Differentiated Structured
Compression for Large Language Models | Large language models (LLMs) show excellent performance in difficult tasks, but they often require massive memories and computational resources. How to reduce the parameter scale of LLMs has become research hotspots. In this study, we make an important observation that the multi-head self-attention (MHA) sub-layer of Transformer exhibits noticeable low-rank structure, while the feed-forward network (FFN) sub-layer does not. With this regard, we design a mixed compression model, which organically combines Low-Rank matrix approximation And structured Pruning (LoRAP). For the MHA sub-layer, we propose an input activation weighted singular value decomposition method to strengthen the low-rank characteristic. Furthermore, we discover that the weight matrices in MHA sub-layer have different low-rank degrees. Thus, a novel parameter allocation scheme according to the discrepancy of low-rank degrees is devised. For the FFN sub-layer, we propose a gradient-free structured channel pruning method. During the pruning, we get an interesting finding that the least important 1% of parameter actually play a vital role in model performance. Extensive evaluations on zero-shot perplexity and zero-shot task classification indicate that our proposal is superior to previous structured compression rivals under multiple compression ratios. | [
"['Guangyan Li' 'Yongqiang Tang' 'Wensheng Zhang']"
]
|
null | null | 2404.09703 | null | null | http://arxiv.org/pdf/2404.09703v1 | 2024-04-15T12:01:42Z | 2024-04-15T12:01:42Z | AI Competitions and Benchmarks: Dataset Development | Machine learning is now used in many applications thanks to its ability to predict, generate, or discover patterns from large quantities of data. However, the process of collecting and transforming data for practical use is intricate. Even in today's digital era, where substantial data is generated daily, it is uncommon for it to be readily usable; most often, it necessitates meticulous manual data preparation. The haste in developing new models can frequently result in various shortcomings, potentially posing risks when deployed in real-world scenarios (eg social discrimination, critical failures), leading to the failure or substantial escalation of costs in AI-based projects. This chapter provides a comprehensive overview of established methodological tools, enriched by our practical experience, in the development of datasets for machine learning. Initially, we develop the tasks involved in dataset development and offer insights into their effective management (including requirements, design, implementation, evaluation, distribution, and maintenance). Then, we provide more details about the implementation process which includes data collection, transformation, and quality evaluation. Finally, we address practical considerations regarding dataset distribution and maintenance. | [
"['Romain Egele' 'Julio C. S. Jacques Junior' 'Jan N. van Rijn'\n 'Isabelle Guyon' 'Xavier Baró' 'Albert Clapés' 'Prasanna Balaprakash'\n 'Sergio Escalera' 'Thomas Moeslund' 'Jun Wan']"
]
|
null | null | 2404.09707 | null | null | http://arxiv.org/pdf/2404.09707v1 | 2024-04-15T12:06:00Z | 2024-04-15T12:06:00Z | Adaptive Patching for High-resolution Image Segmentation with
Transformers | Attention-based models are proliferating in the space of image analytics, including segmentation. The standard method of feeding images to transformer encoders is to divide the images into patches and then feed the patches to the model as a linear sequence of tokens. For high-resolution images, e.g. microscopic pathology images, the quadratic compute and memory cost prohibits the use of an attention-based model, if we are to use smaller patch sizes that are favorable in segmentation. The solution is to either use custom complex multi-resolution models or approximate attention schemes. We take inspiration from Adapative Mesh Refinement (AMR) methods in HPC by adaptively patching the images, as a pre-processing step, based on the image details to reduce the number of patches being fed to the model, by orders of magnitude. This method has a negligible overhead, and works seamlessly with any attention-based model, i.e. it is a pre-processing step that can be adopted by any attention-based model without friction. We demonstrate superior segmentation quality over SoTA segmentation models for real-world pathology datasets while gaining a geomean speedup of $6.9times$ for resolutions up to $64K^2$, on up to $2,048$ GPUs. | [
"['Enzhi Zhang' 'Isaac Lyngaas' 'Peng Chen' 'Xiao Wang' 'Jun Igarashi'\n 'Yuankai Huo' 'Mohamed Wahib' 'Masaharu Munetomo']"
]
|
null | null | 2404.09708 | null | null | http://arxiv.org/pdf/2404.09708v1 | 2024-04-15T12:06:22Z | 2024-04-15T12:06:22Z | Kernel-based learning with guarantees for multi-agent applications | This paper addresses a kernel-based learning problem for a network of agents locally observing a latent multidimensional, nonlinear phenomenon in a noisy environment. We propose a learning algorithm that requires only mild a priori knowledge about the phenomenon under investigation and delivers a model with corresponding non-asymptotic high probability error bounds. Both non-asymptotic analysis of the method and numerical simulation results are presented and discussed in the paper. | [
"['Krzysztof Kowalczyk' 'Paweł Wachel' 'Cristian R. Rojas']"
]
|
null | null | 2404.09709 | null | null | http://arxiv.org/pdf/2404.09709v3 | 2024-04-30T01:48:18Z | 2024-04-15T12:08:44Z | Scenario-Adaptive Fine-Grained Personalization Network: Tailoring User
Behavior Representation to the Scenario Context | Existing methods often adjust representations adaptively only after aggregating user behavior sequences. This coarse-grained approach to re-weighting the entire user sequence hampers the model's ability to accurately model the user interest migration across different scenarios. To enhance the model's capacity to capture user interests from historical behavior sequences in each scenario, we develop a ranking framework named the Scenario-Adaptive Fine-Grained Personalization Network (SFPNet), which designs a kind of fine-grained method for multi-scenario personalized recommendations. Specifically, SFPNet comprises a series of blocks named as Scenario-Tailoring Block, stacked sequentially. Each block initially deploys a parameter personalization unit to integrate scenario information at a coarse-grained level by redefining fundamental features. Subsequently, we consolidate scenario-adaptively adjusted feature representations to serve as context information. By employing residual connection, we incorporate this context into the representation of each historical behavior, allowing for context-aware fine-grained customization of the behavior representations at the scenario-level, which in turn supports scenario-aware user interest modeling. | [
"['Moyu Zhang' 'Yongxiang Tang' 'Jinxin Hu' 'Yu Zhang']"
]
|
null | null | 2404.09715 | null | null | http://arxiv.org/pdf/2404.09715v1 | 2024-04-15T12:18:09Z | 2024-04-15T12:18:09Z | Higher Replay Ratio Empowers Sample-Efficient Multi-Agent Reinforcement
Learning | One of the notorious issues for Reinforcement Learning (RL) is poor sample efficiency. Compared to single agent RL, the sample efficiency for Multi-Agent Reinforcement Learning (MARL) is more challenging because of its inherent partial observability, non-stationary training, and enormous strategy space. Although much effort has been devoted to developing new methods and enhancing sample efficiency, we look at the widely used episodic training mechanism. In each training step, tens of frames are collected, but only one gradient step is made. We argue that this episodic training could be a source of poor sample efficiency. To better exploit the data already collected, we propose to increase the frequency of the gradient updates per environment interaction (a.k.a. Replay Ratio or Update-To-Data ratio). To show its generality, we evaluate $3$ MARL methods on $6$ SMAC tasks. The empirical results validate that a higher replay ratio significantly improves the sample efficiency for MARL algorithms. The codes to reimplement the results presented in this paper are open-sourced at https://anonymous.4open.science/r/rr_for_MARL-0D83/. | [
"['Linjie Xu' 'Zichuan Liu' 'Alexander Dockhorn' 'Diego Perez-Liebana'\n 'Jinyu Wang' 'Lei Song' 'Jiang Bian']"
]
|
null | null | 2404.09717 | null | null | http://arxiv.org/pdf/2404.09717v1 | 2024-04-15T12:20:09Z | 2024-04-15T12:20:09Z | Unveiling Imitation Learning: Exploring the Impact of Data Falsity to
Large Language Model | Many recent studies endeavor to improve open-source language models through imitation learning, and re-training on the synthetic instruction data from state-of-the-art proprietary models like ChatGPT and GPT-4. However, the innate nature of synthetic data inherently contains noisy data, giving rise to a substantial presence of low-quality data replete with erroneous responses, and flawed reasoning. Although we intuitively grasp the potential harm of noisy data, we lack a quantitative understanding of its impact. To this end, this paper explores the correlation between the degree of noise and its impact on language models through instruction tuning. We first introduce the Falsity-Controllable (FACO) dataset, which comprises pairs of true answers with corresponding reasoning, as well as false pairs to manually control the falsity ratio of the dataset.Through our extensive experiments, we found multiple intriguing findings of the correlation between the factuality of the dataset and instruction tuning: Specifically, we verified falsity of the instruction is highly relevant to various benchmark scores. Moreover, when LLMs are trained with false instructions, they learn to lie and generate fake unfaithful answers, even though they know the correct answer for the user request. Additionally, we noted that once the language model is trained with a dataset contaminated by noise, restoring its original performance is possible, but it failed to reach full performance. | [
"['Hyunsoo Cho']"
]
|
null | null | 2404.09722 | null | null | http://arxiv.org/pdf/2404.09722v1 | 2024-04-15T12:25:41Z | 2024-04-15T12:25:41Z | VFLGAN: Vertical Federated Learning-based Generative Adversarial Network
for Vertically Partitioned Data Publication | In the current artificial intelligence (AI) era, the scale and quality of the dataset play a crucial role in training a high-quality AI model. However, good data is not a free lunch and is always hard to access due to privacy regulations like the General Data Protection Regulation (GDPR). A potential solution is to release a synthetic dataset with a similar distribution to that of the private dataset. Nevertheless, in some scenarios, it has been found that the attributes needed to train an AI model belong to different parties, and they cannot share the raw data for synthetic data publication due to privacy regulations. In PETS 2023, Xue et al. proposed the first generative adversary network-based model, VertiGAN, for vertically partitioned data publication. However, after thoroughly investigating, we found that VertiGAN is less effective in preserving the correlation among the attributes of different parties. This article proposes a Vertical Federated Learning-based Generative Adversarial Network, VFLGAN, for vertically partitioned data publication to address the above issues. Our experimental results show that compared with VertiGAN, VFLGAN significantly improves the quality of synthetic data. Taking the MNIST dataset as an example, the quality of the synthetic dataset generated by VFLGAN is 3.2 times better than that generated by VertiGAN w.r.t. the Fr'echet Distance. We also designed a more efficient and effective Gaussian mechanism for the proposed VFLGAN to provide the synthetic dataset with a differential privacy guarantee. On the other hand, differential privacy only gives the upper bound of the worst-case privacy guarantee. This article also proposes a practical auditing scheme that applies membership inference attacks to estimate privacy leakage through the synthetic dataset. | [
"['Xun Yuan' 'Yang Yang' 'Prosanta Gope' 'Aryan Pasikhani' 'Biplab Sikdar']"
]
|
null | null | 2404.09729 | null | null | http://arxiv.org/pdf/2404.09729v1 | 2024-04-15T12:29:16Z | 2024-04-15T12:29:16Z | Amplitude-Phase Fusion for Enhanced Electrocardiogram Morphological
Analysis | Considering the variability of amplitude and phase patterns in electrocardiogram (ECG) signals due to cardiac activity and individual differences, existing entropy-based studies have not fully utilized these two patterns and lack integration. To address this gap, this paper proposes a novel fusion entropy metric, morphological ECG entropy (MEE) for the first time, specifically designed for ECG morphology, to comprehensively describe the fusion of amplitude and phase patterns. MEE is computed based on beat-level samples, enabling detailed analysis of each cardiac cycle. Experimental results demonstrate that MEE achieves rapid, accurate, and label-free localization of abnormal ECG arrhythmia regions. Furthermore, MEE provides a method for assessing sample diversity, facilitating compression of imbalanced training sets (via representative sample selection), and outperforms random pruning. Additionally, MEE exhibits the ability to describe areas of poor quality. By discussing, it proves the robustness of MEE value calculation to noise interference and its low computational complexity. Finally, we integrate this method into a clinical interactive interface to provide a more convenient and intuitive user experience. These findings indicate that MEE serves as a valuable clinical descriptor for ECG characterization. The implementation code can be referenced at the following link: https://github.com/fdu-harry/ECG-MEE-metric. | [
"['Shuaicong Hu' 'Yanan Wang' 'Jian Liu' 'Jingyu Lin' 'Shengmei Qin'\n 'Zhenning Nie' 'Zhifeng Yao' 'Wenjie Cai' 'Cuiwei Yang']"
]
|
null | null | 2404.09730 | null | null | http://arxiv.org/pdf/2404.09730v1 | 2024-04-15T12:29:28Z | 2024-04-15T12:29:28Z | Convergence Analysis of Probability Flow ODE for Score-based Generative
Models | Score-based generative models have emerged as a powerful approach for sampling high-dimensional probability distributions. Despite their effectiveness, their theoretical underpinnings remain relatively underdeveloped. In this work, we study the convergence properties of deterministic samplers based on probability flow ODEs from both theoretical and numerical perspectives. Assuming access to $L^2$-accurate estimates of the score function, we prove the total variation between the target and the generated data distributions can be bounded above by $mathcal{O}(dsqrt{delta})$ in the continuous time level, where $d$ denotes the data dimension and $delta$ represents the $L^2$-score matching error. For practical implementations using a $p$-th order Runge-Kutta integrator with step size $h$, we establish error bounds of $mathcal{O}(d(sqrt{delta} + (dh)^p))$ at the discrete level. Finally, we present numerical studies on problems up to $128$ dimensions to verify our theory, which indicate a better score matching error and dimension dependence. | [
"['Daniel Zhengyu Huang' 'Jiaoyang Huang' 'Zhengjiang Lin']"
]
|
null | null | 2404.09737 | null | null | http://arxiv.org/pdf/2404.09737v1 | 2024-04-15T12:38:46Z | 2024-04-15T12:38:46Z | Quantization of Large Language Models with an Overdetermined Basis | In this paper, we introduce an algorithm for data quantization based on the principles of Kashin representation. This approach hinges on decomposing any given vector, matrix, or tensor into two factors. The first factor maintains a small infinity norm, while the second exhibits a similarly constrained norm when multiplied by an orthogonal matrix. Surprisingly, the entries of factors after decomposition are well-concentrated around several peaks, which allows us to efficiently replace them with corresponding centroids for quantization purposes. We study the theoretical properties of the proposed approach and rigorously evaluate our compression algorithm in the context of next-word prediction tasks and on a set of downstream tasks for text classification. Our findings demonstrate that Kashin Quantization achieves competitive or superior quality in model performance while ensuring data compression, marking a significant advancement in the field of data quantization. | [
"['Daniil Merkulov' 'Daria Cherniuk' 'Alexander Rudikov' 'Ivan Oseledets'\n 'Ekaterina Muravleva' 'Aleksandr Mikhalev' 'Boris Kashin']"
]
|
null | null | 2404.09752 | null | null | http://arxiv.org/pdf/2404.09752v1 | 2024-04-15T12:53:48Z | 2024-04-15T12:53:48Z | Can We Break Free from Strong Data Augmentations in Self-Supervised
Learning? | Self-supervised learning (SSL) has emerged as a promising solution for addressing the challenge of limited labeled data in deep neural networks (DNNs), offering scalability potential. However, the impact of design dependencies within the SSL framework remains insufficiently investigated. In this study, we comprehensively explore SSL behavior across a spectrum of augmentations, revealing their crucial role in shaping SSL model performance and learning mechanisms. Leveraging these insights, we propose a novel learning approach that integrates prior knowledge, with the aim of curtailing the need for extensive data augmentations and thereby amplifying the efficacy of learned representations. Notably, our findings underscore that SSL models imbued with prior knowledge exhibit reduced texture bias, diminished reliance on shortcuts and augmentations, and improved robustness against both natural and adversarial corruptions. These findings not only illuminate a new direction in SSL research, but also pave the way for enhancing DNN performance while concurrently alleviating the imperative for intensive data augmentation, thereby enhancing scalability and real-world problem-solving capabilities. | [
"['Shruthi Gowda' 'Elahe Arani' 'Bahram Zonooz']"
]
|
null | null | 2404.09753 | null | null | http://arxiv.org/pdf/2404.09753v1 | 2024-04-15T12:54:31Z | 2024-04-15T12:54:31Z | Personalized Collaborative Fine-Tuning for On-Device Large Language
Models | We explore on-device self-supervised collaborative fine-tuning of large language models with limited local data availability. Taking inspiration from the collaborative learning community, we introduce three distinct trust-weighted gradient aggregation schemes: weight similarity-based, prediction similarity-based and validation performance-based. To minimize communication overhead, we integrate Low-Rank Adaptation (LoRA) and only exchange LoRA weight updates. Our protocols, driven by prediction and performance metrics, surpass both FedAvg and local fine-tuning methods, which is particularly evident in realistic scenarios with more diverse local data distributions. The results underscore the effectiveness of our approach in addressing heterogeneity and scarcity within local datasets. | [
"['Nicolas Wagner' 'Dongyang Fan' 'Martin Jaggi']"
]
|
null | null | 2404.09760 | null | null | http://arxiv.org/pdf/2404.09760v1 | 2024-04-15T13:02:00Z | 2024-04-15T13:02:00Z | Effective Reinforcement Learning Based on Structural Information
Principles | Although Reinforcement Learning (RL) algorithms acquire sequential behavioral patterns through interactions with the environment, their effectiveness in noisy and high-dimensional scenarios typically relies on specific structural priors. In this paper, we propose a novel and general Structural Information principles-based framework for effective Decision-Making, namely SIDM, approached from an information-theoretic perspective. This paper presents a specific unsupervised partitioning method that forms vertex communities in the state and action spaces based on their feature similarities. An aggregation function, which utilizes structural entropy as the vertex weight, is devised within each community to obtain its embedding, thereby facilitating hierarchical state and action abstractions. By extracting abstract elements from historical trajectories, a directed, weighted, homogeneous transition graph is constructed. The minimization of this graph's high-dimensional entropy leads to the generation of an optimal encoding tree. An innovative two-layer skill-based learning mechanism is introduced to compute the common path entropy of each state transition as its identified probability, thereby obviating the requirement for expert knowledge. Moreover, SIDM can be flexibly incorporated into various single-agent and multi-agent RL algorithms, enhancing their performance. Finally, extensive evaluations on challenging benchmarks demonstrate that, compared with SOTA baselines, our framework significantly and consistently improves the policy's quality, stability, and efficiency up to 32.70%, 88.26%, and 64.86%, respectively. | [
"['Xianghua Zeng' 'Hao Peng' 'Dingli Su' 'Angsheng Li']"
]
|
null | null | 2404.09761 | null | null | http://arxiv.org/pdf/2404.09761v1 | 2024-04-15T13:03:42Z | 2024-04-15T13:03:42Z | Deep Learning-Based Segmentation of Tumors in PET/CT Volumes: Benchmark
of Different Architectures and Training Strategies | Cancer is one of the leading causes of death globally, and early diagnosis is crucial for patient survival. Deep learning algorithms have great potential for automatic cancer analysis. Artificial intelligence has achieved high performance in recognizing and segmenting single lesions. However, diagnosing multiple lesions remains a challenge. This study examines and compares various neural network architectures and training strategies for automatically segmentation of cancer lesions using PET/CT images from the head, neck, and whole body. The authors analyzed datasets from the AutoPET and HECKTOR challenges, exploring popular single-step segmentation architectures and presenting a two-step approach. The results indicate that the V-Net and nnU-Net models were the most effective for their respective datasets. The results for the HECKTOR dataset ranged from 0.75 to 0.76 for the aggregated Dice coefficient. Eliminating cancer-free cases from the AutoPET dataset was found to improve the performance of most models. In the case of AutoPET data, the average segmentation efficiency after training only on images containing cancer lesions increased from 0.55 to 0.66 for the classic Dice coefficient and from 0.65 to 0.73 for the aggregated Dice coefficient. The research demonstrates the potential of artificial intelligence in precise oncological diagnostics and may contribute to the development of more targeted and effective cancer assessment techniques. | [
"['Monika Górka' 'Daniel Jaworek' 'Marek Wodzinski']"
]
|
null | null | 2404.09774 | null | null | http://arxiv.org/pdf/2404.09774v1 | 2024-04-15T13:28:13Z | 2024-04-15T13:28:13Z | RandAlign: A Parameter-Free Method for Regularizing Graph Convolutional
Networks | Studies continually find that message-passing graph convolutional networks suffer from the over-smoothing issue. Basically, the issue of over-smoothing refers to the phenomenon that the learned embeddings for all nodes can become very similar to one another and therefore are uninformative after repeatedly applying message passing iterations. Intuitively, we can expect the generated embeddings become smooth asymptotically layerwisely, that is each layer of graph convolution generates a smoothed version of embeddings as compared to that generated by the previous layer. Based on this intuition, we propose RandAlign, a stochastic regularization method for graph convolutional networks. The idea of RandAlign is to randomly align the learned embedding for each node with that of the previous layer using randomly interpolation in each graph convolution layer. Through alignment, the smoothness of the generated embeddings is explicitly reduced. To better maintain the benefit yielded by the graph convolution, in the alignment step we introduce to first scale the embedding of the previous layer to the same norm as the generated embedding and then perform random interpolation for aligning the generated embedding. RandAlign is a parameter-free method and can be directly applied without introducing additional trainable weights or hyper-parameters. We experimentally evaluate RandAlign on different graph domain tasks on seven benchmark datasets. The experimental results show that RandAlign is a general method that improves the generalization performance of various graph convolutional network models and also improves the numerical stability of optimization, advancing the state of the art performance for graph representation learning. | [
"['Haimin Zhang' 'Min Xu']"
]
|
null | null | 2404.09779 | null | null | http://arxiv.org/pdf/2404.09779v2 | 2024-04-25T09:39:16Z | 2024-04-15T13:31:31Z | A replica analysis of under-bagging | Under-bagging (UB), which combines under sampling and bagging, is a popular ensemble learning method for training classifiers on an imbalanced data. Using bagging to reduce the increased variance caused by the reduction in sample size due to under sampling is a natural approach. However, it has recently been pointed out that in generalized linear models, naive bagging, which does not consider the class imbalance structure, and ridge regularization can produce the same results. Therefore, it is not obvious whether it is better to use UB, which requires an increased computational cost proportional to the number of under-sampled data sets, when training linear models. Given such a situation, in this study, we heuristically derive a sharp asymptotics of UB and use it to compare with several other standard methods for learning from imbalanced data, in the scenario where a linear classifier is trained from a two-component mixture data. The methods compared include the under-sampling (US) method, which trains a model using a single realization of the subsampled data, and the simple weighting (SW) method, which trains a model with a weighted loss on the entire data. It is shown that the performance of UB is improved by increasing the size of the majority class while keeping the size of the minority fixed, even though the class imbalance can be large, especially when the size of the minority class is small. This is in contrast to US, whose performance does not change as the size of the majority class increases, and SW, whose performance decreases as the imbalance increases. These results are different from the case of the naive bagging when training generalized linear models without considering the structure of the class imbalance, indicating the intrinsic difference between the ensembling and the direct regularization on the parameters. | [
"['Takashi Takahashi']"
]
|
null | null | 2404.09788 | null | null | http://arxiv.org/pdf/2404.09788v1 | 2024-04-15T13:44:01Z | 2024-04-15T13:44:01Z | Shape Arithmetic Expressions: Advancing Scientific Discovery Beyond
Closed-Form Equations | Symbolic regression has excelled in uncovering equations from physics, chemistry, biology, and related disciplines. However, its effectiveness becomes less certain when applied to experimental data lacking inherent closed-form expressions. Empirically derived relationships, such as entire stress-strain curves, may defy concise closed-form representation, compelling us to explore more adaptive modeling approaches that balance flexibility with interpretability. In our pursuit, we turn to Generalized Additive Models (GAMs), a widely used class of models known for their versatility across various domains. Although GAMs can capture non-linear relationships between variables and targets, they cannot capture intricate feature interactions. In this work, we investigate both of these challenges and propose a novel class of models, Shape Arithmetic Expressions (SHAREs), that fuses GAM's flexible shape functions with the complex feature interactions found in mathematical expressions. SHAREs also provide a unifying framework for both of these approaches. We also design a set of rules for constructing SHAREs that guarantee transparency of the found expressions beyond the standard constraints based on the model's size. | [
"['Krzysztof Kacprzyk' 'Mihaela van der Schaar']"
]
|
null | null | 2404.09794 | null | null | http://arxiv.org/pdf/2404.09794v1 | 2024-04-15T13:51:20Z | 2024-04-15T13:51:20Z | Taper-based scattering formulation of the Helmholtz equation to improve
the training process of Physics-Informed Neural Networks | This work addresses the scattering problem of an incident wave at a junction connecting two semi-infinite waveguides, which we intend to solve using Physics-Informed Neural Networks (PINNs). As with other deep learning-based approaches, PINNs are known to suffer from a spectral bias and from the hyperbolic nature of the Helmholtz equation. This makes the training process challenging, especially for higher wave numbers. We show an example where these limitations are present. In order to improve the learning capability of our model, we suggest an equivalent formulation of the Helmholtz Boundary Value Problem (BVP) that is based on splitting the total wave into a tapered continuation of the incoming wave and a remaining scattered wave. This allows the introduction of an inhomogeneity in the BVP, leveraging the information transmitted during back-propagation, thus, enhancing and accelerating the training process of our PINN model. The presented numerical illustrations are in accordance with the expected behavior, paving the way to a possible alternative approach to predicting scattering problems using PINNs. | [
"['W. Dörfler' 'M. Elasmi' 'T. Laufer']"
]
|
null | null | 2404.09802 | null | null | http://arxiv.org/pdf/2404.09802v1 | 2024-04-15T13:58:22Z | 2024-04-15T13:58:22Z | The Performance of Sequential Deep Learning Models in Detecting Phishing
Websites Using Contextual Features of URLs | Cyber attacks continue to pose significant threats to individuals and organizations, stealing sensitive data such as personally identifiable information, financial information, and login credentials. Hence, detecting malicious websites before they cause any harm is critical to preventing fraud and monetary loss. To address the increasing number of phishing attacks, protective mechanisms must be highly responsive, adaptive, and scalable. Fortunately, advances in the field of machine learning, coupled with access to vast amounts of data, have led to the adoption of various deep learning models for timely detection of these cyber crimes. This study focuses on the detection of phishing websites using deep learning models such as Multi-Head Attention, Temporal Convolutional Network (TCN), BI-LSTM, and LSTM where URLs of the phishing websites are treated as a sequence. The results demonstrate that Multi-Head Attention and BI-LSTM model outperform some other deep learning-based algorithms such as TCN and LSTM in producing better precision, recall, and F1-scores. | [
"['Saroj Gopali' 'Akbar S. Namin' 'Faranak Abri' 'Keith S. Jones']"
]
|
null | null | 2404.09809 | null | null | http://arxiv.org/pdf/2404.09809v1 | 2024-04-15T14:07:33Z | 2024-04-15T14:07:33Z | Neighbour-level Message Interaction Encoding for Improved Representation
Learning on Graphs | Message passing has become the dominant framework in graph representation learning. The essential idea of the message-passing framework is to update node embeddings based on the information aggregated from local neighbours. However, most existing aggregation methods have not encoded neighbour-level message interactions into the aggregated message, resulting in an information lost in embedding generation. And this information lost could be accumulated and become more serious as more layers are added to the graph network model. To address this issue, we propose a neighbour-level message interaction information encoding method for improving graph representation learning. For messages that are aggregated at a node, we explicitly generate an encoding between each message and the rest messages using an encoding function. Then we aggregate these learned encodings and take the sum of the aggregated encoding and the aggregated message to update the embedding for the node. By this way, neighbour-level message interaction information is integrated into the generated node embeddings. The proposed encoding method is a generic method which can be integrated into message-passing graph convolutional networks. Extensive experiments are conducted on six popular benchmark datasets across four highly-demanded tasks. The results show that integrating neighbour-level message interactions achieves improved performance of the base models, advancing the state of the art results for representation learning over graphs. | [
"['Haimin Zhang' 'Min Xu']"
]
|
null | null | 2404.09812 | null | null | http://arxiv.org/pdf/2404.09812v2 | 2024-06-13T09:20:40Z | 2024-04-15T14:10:06Z | Solving the Tree Containment Problem Using Graph Neural Networks | Tree Containment is a fundamental problem in phylogenetics useful for verifying a proposed phylogenetic network, representing the evolutionary history of certain species. Tree Containment asks whether the given phylogenetic tree (for instance, constructed from a DNA fragment showing tree-like evolution) is contained in the given phylogenetic network. In the general case, this is an NP-complete problem. We propose to solve it approximately using Graph Neural Networks. In particular, we propose to combine the given network and the tree and apply a Graph Neural Network to this network-tree graph. This way, we achieve the capability of solving the tree containment instances representing a larger number of species than the instances contained in the training dataset (i.e., our algorithm has the inductive learning ability). Our algorithm demonstrates an accuracy of over $95%$ in solving the tree containment problem on instances with up to 100 leaves. | [
"['Arkadiy Dushatskiy' 'Esther Julien' 'Leen Stougie' 'Leo van Iersel']"
]
|
null | null | 2404.09816 | null | null | http://arxiv.org/pdf/2404.09816v1 | 2024-04-15T14:14:05Z | 2024-04-15T14:14:05Z | FedP3: Federated Personalized and Privacy-friendly Network Pruning under
Model Heterogeneity | The interest in federated learning has surged in recent research due to its unique ability to train a global model using privacy-secured information held locally on each client. This paper pays particular attention to the issue of client-side model heterogeneity, a pervasive challenge in the practical implementation of FL that escalates its complexity. Assuming a scenario where each client possesses varied memory storage, processing capabilities and network bandwidth - a phenomenon referred to as system heterogeneity - there is a pressing need to customize a unique model for each client. In response to this, we present an effective and adaptable federated framework FedP3, representing Federated Personalized and Privacy-friendly network Pruning, tailored for model heterogeneity scenarios. Our proposed methodology can incorporate and adapt well-established techniques to its specific instances. We offer a theoretical interpretation of FedP3 and its locally differential-private variant, DP-FedP3, and theoretically validate their efficiencies. | [
"['Kai Yi' 'Nidham Gazagnadou' 'Peter Richtárik' 'Lingjuan Lyu']"
]
|
null | null | 2404.09821 | null | null | http://arxiv.org/pdf/2404.09821v1 | 2024-04-15T14:21:01Z | 2024-04-15T14:21:01Z | A provable control of sensitivity of neural networks through a direct
parameterization of the overall bi-Lipschitzness | While neural networks can enjoy an outstanding flexibility and exhibit unprecedented performance, the mechanism behind their behavior is still not well-understood. To tackle this fundamental challenge, researchers have tried to restrict and manipulate some of their properties in order to gain new insights and better control on them. Especially, throughout the past few years, the concept of emph{bi-Lipschitzness} has been proved as a beneficial inductive bias in many areas. However, due to its complexity, the design and control of bi-Lipschitz architectures are falling behind, and a model that is precisely designed for bi-Lipschitzness realizing a direct and simple control of the constants along with solid theoretical analysis is lacking. In this work, we investigate and propose a novel framework for bi-Lipschitzness that can achieve such a clear and tight control based on convex neural networks and the Legendre-Fenchel duality. Its desirable properties are illustrated with concrete experiments. We also apply this framework to uncertainty estimation and monotone problem settings to illustrate its broad range of applications. | [
"['Yuri Kinoshita' 'Taro Toyoizumi']"
]
|
null | null | 2404.09828 | null | null | http://arxiv.org/pdf/2404.09828v1 | 2024-04-15T14:26:00Z | 2024-04-15T14:26:00Z | Interaction as Explanation: A User Interaction-based Method for
Explaining Image Classification Models | In computer vision, explainable AI (xAI) methods seek to mitigate the 'black-box' problem by making the decision-making process of deep learning models more interpretable and transparent. Traditional xAI methods concentrate on visualizing input features that influence model predictions, providing insights primarily suited for experts. In this work, we present an interaction-based xAI method that enhances user comprehension of image classification models through their interaction. Thus, we developed a web-based prototype allowing users to modify images via painting and erasing, thereby observing changes in classification results. Our approach enables users to discern critical features influencing the model's decision-making process, aligning their mental models with the model's logic. Experiments conducted with five images demonstrate the potential of the method to reveal feature importance through user interaction. Our work contributes a novel perspective to xAI by centering on end-user engagement and understanding, paving the way for more intuitive and accessible explainability in AI systems. | [
"['Hyeonggeun Yun']"
]
|
null | null | 2404.09832 | null | null | http://arxiv.org/pdf/2404.09832v1 | 2024-04-15T14:31:53Z | 2024-04-15T14:31:53Z | No-Regret Algorithms in non-Truthful Auctions with Budget and ROI
Constraints | Advertisers increasingly use automated bidding to optimize their ad campaigns on online advertising platforms. Autobidding optimizes an advertiser's objective subject to various constraints, e.g. average ROI and budget constraints. In this paper, we study the problem of designing online autobidding algorithms to optimize value subject to ROI and budget constraints when the platform is running any mixture of first and second price auction. We consider the following stochastic setting: There is an item for sale in each of $T$ rounds. In each round, buyers submit bids and an auction is run to sell the item. We focus on one buyer, possibly with budget and ROI constraints. We assume that the buyer's value and the highest competing bid are drawn i.i.d. from some unknown (joint) distribution in each round. We design a low-regret bidding algorithm that satisfies the buyer's constraints. Our benchmark is the objective value achievable by the best possible Lipschitz function that maps values to bids, which is rich enough to best respond to many different correlation structures between value and highest competing bid. Our main result is an algorithm with full information feedback that guarantees a near-optimal $tilde O(sqrt T)$ regret with respect to the best Lipschitz function. Our result applies to a wide range of auctions, most notably any mixture of first and second price auctions (price is a convex combination of the first and second price). In addition, our result holds for both value-maximizing buyers and quasi-linear utility-maximizing buyers. We also study the bandit setting, where we show an $Omega(T^{2/3})$ lower bound on the regret for first-price auctions, showing a large disparity between the full information and bandit settings. We also design an algorithm with $tilde O(T^{3/4})$ regret, when the value distribution is known and is independent of the highest competing bid. | [
"['Gagan Aggarwal' 'Giannis Fikioris' 'Mingfei Zhao']"
]
|
null | null | 2404.09841 | null | null | http://arxiv.org/pdf/2404.09841v2 | 2024-04-16T14:55:13Z | 2024-04-15T14:48:43Z | Anatomy of Industrial Scale Multilingual ASR | This paper describes AssemblyAI's industrial-scale automatic speech recognition (ASR) system, designed to meet the requirements of large-scale, multilingual ASR serving various application needs. Our system leverages a diverse training dataset comprising unsupervised (12.5M hours), supervised (188k hours), and pseudo-labeled (1.6M hours) data across four languages. We provide a detailed description of our model architecture, consisting of a full-context 600M-parameter Conformer encoder pre-trained with BEST-RQ and an RNN-T decoder fine-tuned jointly with the encoder. Our extensive evaluation demonstrates competitive word error rates (WERs) against larger and more computationally expensive models, such as Whisper large and Canary-1B. Furthermore, our architectural choices yield several key advantages, including an improved code-switching capability, a 5x inference speedup compared to an optimized Whisper baseline, a 30% reduction in hallucination rate on speech data, and a 90% reduction in ambient noise compared to Whisper, along with significantly improved time-stamp accuracy. Throughout this work, we adopt a system-centric approach to analyzing various aspects of fully-fledged ASR models to gain practically relevant insights useful for real-world services operating at scale. | [
"['Francis McCann Ramirez' 'Luka Chkhetiani' 'Andrew Ehrenberg'\n 'Robert McHardy' 'Rami Botros' 'Yash Khare' 'Andrea Vanzo'\n 'Taufiquzzaman Peyash' 'Gabriel Oexle' 'Michael Liang' 'Ilya Sklyar'\n 'Enver Fakhan' 'Ahmed Etefy' 'Daniel McCrystal' 'Sam Flamini'\n 'Domenic Donato' 'Takuya Yoshioka']"
]
|
null | null | 2404.09847 | null | null | http://arxiv.org/pdf/2404.09847v1 | 2024-04-15T14:59:21Z | 2024-04-15T14:59:21Z | Statistical learning for constrained functional parameters in
infinite-dimensional models with applications in fair machine learning | Constrained learning has become increasingly important, especially in the realm of algorithmic fairness and machine learning. In these settings, predictive models are developed specifically to satisfy pre-defined notions of fairness. Here, we study the general problem of constrained statistical machine learning through a statistical functional lens. We consider learning a function-valued parameter of interest under the constraint that one or several pre-specified real-valued functional parameters equal zero or are otherwise bounded. We characterize the constrained functional parameter as the minimizer of a penalized risk criterion using a Lagrange multiplier formulation. We show that closed-form solutions for the optimal constrained parameter are often available, providing insight into mechanisms that drive fairness in predictive models. Our results also suggest natural estimators of the constrained parameter that can be constructed by combining estimates of unconstrained parameters of the data generating distribution. Thus, our estimation procedure for constructing fair machine learning algorithms can be applied in conjunction with any statistical learning approach and off-the-shelf software. We demonstrate the generality of our method by explicitly considering a number of examples of statistical fairness constraints and implementing the approach using several popular learning approaches. | [
"['Razieh Nabi' 'Nima S. Hejazi' 'Mark J. van der Laan' 'David Benkeser']"
]
|
null | null | 2404.09848 | null | null | http://arxiv.org/pdf/2404.09848v1 | 2024-04-15T15:00:17Z | 2024-04-15T15:00:17Z | HyperMono: A Monotonicity-aware Approach to Hyper-Relational Knowledge
Representation | In a hyper-relational knowledge graph (HKG), each fact is composed of a main triple associated with attribute-value qualifiers, which express additional factual knowledge. The hyper-relational knowledge graph completion (HKGC) task aims at inferring plausible missing links in a HKG. Most existing approaches to HKGC focus on enhancing the communication between qualifier pairs and main triples, while overlooking two important properties that emerge from the monotonicity of the hyper-relational graphs representation regime. Stage Reasoning allows for a two-step reasoning process, facilitating the integration of coarse-grained inference results derived solely from main triples and fine-grained inference results obtained from hyper-relational facts with qualifiers. In the initial stage, coarse-grained results provide an upper bound for correct predictions, which are subsequently refined in the fine-grained step. More generally, Qualifier Monotonicity implies that by attaching more qualifier pairs to a main triple, we may only narrow down the answer set, but never enlarge it. This paper proposes the HyperMono model for hyper-relational knowledge graph completion, which realizes stage reasoning and qualifier monotonicity. To implement qualifier monotonicity HyperMono resorts to cone embeddings. Experiments on three real-world datasets with three different scenario conditions demonstrate the strong performance of HyperMono when compared to the SoTA. | [
"['Zhiwei Hu' 'Víctor Gutiérrez-Basulto' 'Zhiliang Xiang' 'Ru Li'\n 'Jeff Z. Pan']"
]
|
null | null | 2404.09861 | null | null | http://arxiv.org/pdf/2404.09861v1 | 2024-04-15T15:17:38Z | 2024-04-15T15:17:38Z | Unsupervised Federated Optimization at the Edge: D2D-Enabled Learning
without Labels | Federated learning (FL) is a popular solution for distributed machine learning (ML). While FL has traditionally been studied for supervised ML tasks, in many applications, it is impractical to assume availability of labeled data across devices. To this end, we develop Cooperative Federated unsupervised Contrastive Learning ({tt CF-CL)} to facilitate FL across edge devices with unlabeled datasets. {tt CF-CL} employs local device cooperation where either explicit (i.e., raw data) or implicit (i.e., embeddings) information is exchanged through device-to-device (D2D) communications to improve local diversity. Specifically, we introduce a textit{smart information push-pull} methodology for data/embedding exchange tailored to FL settings with either soft or strict data privacy restrictions. Information sharing is conducted through a probabilistic importance sampling technique at receivers leveraging a carefully crafted reserve dataset provided by transmitters. In the implicit case, embedding exchange is further integrated into the local ML training at the devices via a regularization term incorporated into the contrastive loss, augmented with a dynamic contrastive margin to adjust the volume of latent space explored. Numerical evaluations demonstrate that {tt CF-CL} leads to alignment of latent spaces learned across devices, results in faster and more efficient global model training, and is effective in extreme non-i.i.d. data distribution settings across devices. | [
"['Satyavrat Wagle' 'Seyyedali Hosseinalipour' 'Naji Khosravan'\n 'Christopher G. Brinton']"
]
|
null | null | 2404.09871 | null | null | http://arxiv.org/pdf/2404.09871v3 | 2024-06-25T09:10:46Z | 2024-04-15T15:42:12Z | Explainable Online Unsupervised Anomaly Detection for Cyber-Physical
Systems via Causal Discovery from Time Series | Online unsupervised detection of anomalies is crucial to guarantee the correct operation of cyber-physical systems and the safety of humans interacting with them. State-of-the-art approaches based on deep learning via neural networks achieve outstanding performance at anomaly recognition, evaluating the discrepancy between a normal model of the system (with no anomalies) and the real-time stream of sensor time series. However, large training data and time are typically required, and explainability is still a challenge to identify the root of the anomaly and implement predictive maintainance. In this paper, we use causal discovery to learn a normal causal graph of the system, and we evaluate the persistency of causal links during real-time acquisition of sensor data to promptly detect anomalies. On two benchmark anomaly detection datasets, we show that our method has higher training efficiency, outperforms the accuracy of state-of-the-art neural architectures and correctly identifies the sources of >10 different anomalies. The code is at https://github.com/Isla-lab/causal_anomaly_detection. | [
"['Daniele Meli']"
]
|
null | null | 2404.09884 | null | null | http://arxiv.org/pdf/2404.09884v1 | 2024-04-15T15:53:23Z | 2024-04-15T15:53:23Z | Map-Relative Pose Regression for Visual Re-Localization | Pose regression networks predict the camera pose of a query image relative to a known environment. Within this family of methods, absolute pose regression (APR) has recently shown promising accuracy in the range of a few centimeters in position error. APR networks encode the scene geometry implicitly in their weights. To achieve high accuracy, they require vast amounts of training data that, realistically, can only be created using novel view synthesis in a days-long process. This process has to be repeated for each new scene again and again. We present a new approach to pose regression, map-relative pose regression (marepo), that satisfies the data hunger of the pose regression network in a scene-agnostic fashion. We condition the pose regressor on a scene-specific map representation such that its pose predictions are relative to the scene map. This allows us to train the pose regressor across hundreds of scenes to learn the generic relation between a scene-specific map representation and the camera pose. Our map-relative pose regressor can be applied to new map representations immediately or after mere minutes of fine-tuning for the highest accuracy. Our approach outperforms previous pose regression methods by far on two public datasets, indoor and outdoor. Code is available: https://nianticlabs.github.io/marepo | [
"['Shuai Chen' 'Tommaso Cavallari' 'Victor Adrian Prisacariu'\n 'Eric Brachmann']"
]
|
null | null | 2404.09886 | null | null | http://arxiv.org/pdf/2404.09886v1 | 2024-04-15T15:54:30Z | 2024-04-15T15:54:30Z | ReffAKD: Resource-efficient Autoencoder-based Knowledge Distillation | In this research, we propose an innovative method to boost Knowledge Distillation efficiency without the need for resource-heavy teacher models. Knowledge Distillation trains a smaller ``student'' model with guidance from a larger ``teacher'' model, which is computationally costly. However, the main benefit comes from the soft labels provided by the teacher, helping the student grasp nuanced class similarities. In our work, we propose an efficient method for generating these soft labels, thereby eliminating the need for a large teacher model. We employ a compact autoencoder to extract essential features and calculate similarity scores between different classes. Afterward, we apply the softmax function to these similarity scores to obtain a soft probability vector. This vector serves as valuable guidance during the training of the student model. Our extensive experiments on various datasets, including CIFAR-100, Tiny Imagenet, and Fashion MNIST, demonstrate the superior resource efficiency of our approach compared to traditional knowledge distillation methods that rely on large teacher models. Importantly, our approach consistently achieves similar or even superior performance in terms of model accuracy. We also perform a comparative study with various techniques recently developed for knowledge distillation showing our approach achieves competitive performance with using significantly less resources. We also show that our approach can be easily added to any logit based knowledge distillation method. This research contributes to making knowledge distillation more accessible and cost-effective for practical applications, making it a promising avenue for improving the efficiency of model training. The code for this work is available at, https://github.com/JEKimLab/ReffAKD. | [
"['Divyang Doshi' 'Jung-Eun Kim']"
]
|
null | null | 2404.09896 | null | null | http://arxiv.org/pdf/2404.09896v1 | 2024-04-15T16:10:27Z | 2024-04-15T16:10:27Z | Accelerating Ensemble Error Bar Prediction with Single Models Fits | Ensemble models can be used to estimate prediction uncertainties in machine learning models. However, an ensemble of N models is approximately N times more computationally demanding compared to a single model when it is used for inference. In this work, we explore fitting a single model to predicted ensemble error bar data, which allows us to estimate uncertainties without the need for a full ensemble. Our approach is based on three models: Model A for predictive accuracy, Model $A_{E}$ for traditional ensemble-based error bar prediction, and Model B, fit to data from Model $A_{E}$, to be used for predicting the values of $A_{E}$ but with only one model evaluation. Model B leverages synthetic data augmentation to estimate error bars efficiently. This approach offers a highly flexible method of uncertainty quantification that can approximate that of ensemble methods but only requires a single extra model evaluation over Model A during inference. We assess this approach on a set of problems in materials science. | [
"['Vidit Agrawal' 'Shixin Zhang' 'Lane E. Schultz' 'Dane Morgan']"
]
|
null | null | 2404.09897 | null | null | http://arxiv.org/pdf/2404.09897v1 | 2024-04-15T16:16:59Z | 2024-04-15T16:16:59Z | Progressive Knowledge Graph Completion | Knowledge Graph Completion (KGC) has emerged as a promising solution to address the issue of incompleteness within Knowledge Graphs (KGs). Traditional KGC research primarily centers on triple classification and link prediction. Nevertheless, we contend that these tasks do not align well with real-world scenarios and merely serve as surrogate benchmarks. In this paper, we investigate three crucial processes relevant to real-world construction scenarios: (a) the verification process, which arises from the necessity and limitations of human verifiers; (b) the mining process, which identifies the most promising candidates for verification; and (c) the training process, which harnesses verified data for subsequent utilization; in order to achieve a transition toward more realistic challenges. By integrating these three processes, we introduce the Progressive Knowledge Graph Completion (PKGC) task, which simulates the gradual completion of KGs in real-world scenarios. Furthermore, to expedite PKGC processing, we propose two acceleration modules: Optimized Top-$k$ algorithm and Semantic Validity Filter. These modules significantly enhance the efficiency of the mining procedure. Our experiments demonstrate that performance in link prediction does not accurately reflect performance in PKGC. A more in-depth analysis reveals the key factors influencing the results and provides potential directions for future research. | [
"['Jiayi Li' 'Ruilin Luo' 'Jiaqi Sun' 'Jing Xiao' 'Yujiu Yang']"
]
|
null | null | 2404.09916 | null | null | http://arxiv.org/pdf/2404.09916v1 | 2024-04-15T16:43:13Z | 2024-04-15T16:43:13Z | Comprehensive Library of Variational LSE Solvers | Linear systems of equations can be found in various mathematical domains, as well as in the field of machine learning. By employing noisy intermediate-scale quantum devices, variational solvers promise to accelerate finding solutions for large systems. Although there is a wealth of theoretical research on these algorithms, only fragmentary implementations exist. To fill this gap, we have developed the variational-lse-solver framework, which realizes existing approaches in literature, and introduces several enhancements. The user-friendly interface is designed for researchers that work at the abstraction level of identifying and developing end-to-end applications. | [
"['Nico Meyer' 'Martin Röhn' 'Jakob Murauer' 'Axel Plinge'\n 'Christopher Mutschler' 'Daniel D. Scherer']"
]
|
null | null | 2404.09927 | null | null | http://arxiv.org/pdf/2404.09927v1 | 2024-04-15T16:52:53Z | 2024-04-15T16:52:53Z | Autonomous Path Planning for Intercostal Robotic Ultrasound Imaging
Using Reinforcement Learning | Ultrasound (US) has been widely used in daily clinical practice for screening internal organs and guiding interventions. However, due to the acoustic shadow cast by the subcutaneous rib cage, the US examination for thoracic application is still challenging. To fully cover and reconstruct the region of interest in US for diagnosis, an intercostal scanning path is necessary. To tackle this challenge, we present a reinforcement learning (RL) approach for planning scanning paths between ribs to monitor changes in lesions on internal organs, such as the liver and heart, which are covered by rib cages. Structured anatomical information of the human skeleton is crucial for planning these intercostal paths. To obtain such anatomical insight, an RL agent is trained in a virtual environment constructed using computational tomography (CT) templates with randomly initialized tumors of various shapes and locations. In addition, task-specific state representation and reward functions are introduced to ensure the convergence of the training process while minimizing the effects of acoustic attenuation and shadows during scanning. To validate the effectiveness of the proposed approach, experiments have been carried out on unseen CTs with randomly defined single or multiple scanning targets. The results demonstrate the efficiency of the proposed RL framework in planning non-shadowed US scanning trajectories in areas with limited acoustic access. | [
"['Yuan Bi' 'Cheng Qian' 'Zhicheng Zhang' 'Nassir Navab' 'Zhongliang Jiang']"
]
|
null | null | 2404.09932 | null | null | http://arxiv.org/pdf/2404.09932v1 | 2024-04-15T16:58:28Z | 2024-04-15T16:58:28Z | Foundational Challenges in Assuring Alignment and Safety of Large
Language Models | This work identifies 18 foundational challenges in assuring the alignment and safety of large language models (LLMs). These challenges are organized into three different categories: scientific understanding of LLMs, development and deployment methods, and sociotechnical challenges. Based on the identified challenges, we pose $200+$ concrete research questions. | [
"['Usman Anwar' 'Abulhair Saparov' 'Javier Rando' 'Daniel Paleka'\n 'Miles Turpin' 'Peter Hase' 'Ekdeep Singh Lubana' 'Erik Jenner'\n 'Stephen Casper' 'Oliver Sourbut' 'Benjamin L. Edelman' 'Zhaowei Zhang'\n 'Mario Günther' 'Anton Korinek' 'Jose Hernandez-Orallo' 'Lewis Hammond'\n 'Eric Bigelow' 'Alexander Pan' 'Lauro Langosco' 'Tomasz Korbak'\n 'Heidi Zhang' 'Ruiqi Zhong' 'Seán Ó hÉigeartaigh' 'Gabriel Recchia'\n 'Giulio Corsi' 'Alan Chan' 'Markus Anderljung' 'Lilian Edwards'\n 'Yoshua Bengio' 'Danqi Chen' 'Samuel Albanie' 'Tegan Maharaj'\n 'Jakob Foerster' 'Florian Tramer' 'He He' 'Atoosa Kasirzadeh'\n 'Yejin Choi' 'David Krueger']"
]
|
null | null | 2404.09937 | null | null | http://arxiv.org/pdf/2404.09937v1 | 2024-04-15T17:03:41Z | 2024-04-15T17:03:41Z | Compression Represents Intelligence Linearly | There is a belief that learning to compress well will lead to intelligence. Recently, language modeling has been shown to be equivalent to compression, which offers a compelling rationale for the success of large language models (LLMs): the development of more advanced language models is essentially enhancing compression which facilitates intelligence. Despite such appealing discussions, little empirical evidence is present for the interplay between compression and intelligence. In this work, we examine their relationship in the context of LLMs, treating LLMs as data compressors. Given the abstract concept of "intelligence", we adopt the average downstream benchmark scores as a surrogate, specifically targeting intelligence related to knowledge and commonsense, coding, and mathematical reasoning. Across 12 benchmarks, our study brings together 30 public LLMs that originate from diverse organizations. Remarkably, we find that LLMs' intelligence -- reflected by average benchmark scores -- almost linearly correlates with their ability to compress external text corpora. These results provide concrete evidence supporting the belief that superior compression indicates greater intelligence. Furthermore, our findings suggest that compression efficiency, as an unsupervised metric derived from raw text corpora, serves as a reliable evaluation measure that is linearly associated with the model capabilities. We open-source our compression datasets as well as our data collection pipelines to facilitate future researchers to assess compression properly. | [
"['Yuzhen Huang' 'Jinghan Zhang' 'Zifei Shan' 'Junxian He']"
]
|
null | null | 2404.09946 | null | null | http://arxiv.org/pdf/2404.09946v1 | 2024-04-15T17:15:18Z | 2024-04-15T17:15:18Z | A Note on Loss Functions and Error Compounding in Model-based
Reinforcement Learning | This note clarifies some confusions (and perhaps throws out more) around model-based reinforcement learning and their theoretical understanding in the context of deep RL. Main topics of discussion are (1) how to reconcile model-based RL's bad empirical reputation on error compounding with its superior theoretical properties, and (2) the limitations of empirically popular losses. For the latter, concrete counterexamples for the "MuZero loss" are constructed to show that it not only fails in stochastic environments, but also suffers exponential sample complexity in deterministic environments when data provides sufficient coverage. | [
"['Nan Jiang']"
]
|
null | null | 2404.09953 | null | null | http://arxiv.org/pdf/2404.09953v1 | 2024-04-15T17:27:00Z | 2024-04-15T17:27:00Z | Classification Tree-based Active Learning: A Wrapper Approach | Supervised machine learning often requires large training sets to train accurate models, yet obtaining large amounts of labeled data is not always feasible. Hence, it becomes crucial to explore active learning methods for reducing the size of training sets while maintaining high accuracy. The aim is to select the optimal subset of data for labeling from an initial unlabeled set, ensuring precise prediction of outcomes. However, conventional active learning approaches are comparable to classical random sampling. This paper proposes a wrapper active learning method for classification, organizing the sampling process into a tree structure, that improves state-of-the-art algorithms. A classification tree constructed on an initial set of labeled samples is considered to decompose the space into low-entropy regions. Input-space based criteria are used thereafter to sub-sample from these regions, the total number of points to be labeled being decomposed into each region. This adaptation proves to be a significant enhancement over existing active learning methods. Through experiments conducted on various benchmark data sets, the paper demonstrates the efficacy of the proposed framework by being effective in constructing accurate classification models, even when provided with a severely restricted labeled data set. | [
"['Ashna Jose' 'Emilie Devijver' 'Massih-Reza Amini' 'Noel Jakse'\n 'Roberta Poloni']"
]
|
null | null | 2404.09957 | null | null | http://arxiv.org/pdf/2404.09957v2 | 2024-05-13T04:29:48Z | 2024-04-15T17:31:32Z | How to build the best medical image segmentation algorithm using
foundation models: a comprehensive empirical study with Segment Anything
Model | Automated segmentation is a fundamental medical image analysis task, which enjoys significant advances due to the advent of deep learning. While foundation models have been useful in natural language processing and some vision tasks for some time, the foundation model developed with image segmentation in mind - Segment Anything Model (SAM) - has been developed only recently and has shown similar promise. However, there are still no systematic analyses or "best-practice" guidelines for optimal fine-tuning of SAM for medical image segmentation. This work summarizes existing fine-tuning strategies with various backbone architectures, model components, and fine-tuning algorithms across 18 combinations, and evaluates them on 17 datasets covering all common radiology modalities. Our study reveals that (1) fine-tuning SAM leads to slightly better performance than previous segmentation methods, (2) fine-tuning strategies that use parameter-efficient learning in both the encoder and decoder are superior to other strategies, (3) network architecture has a small impact on final performance, (4) further training SAM with self-supervised learning can improve final model performance. We also demonstrate the ineffectiveness of some methods popular in the literature and further expand our experiments into few-shot and prompt-based settings. Lastly, we released our code and MRI-specific fine-tuned weights, which consistently obtained superior performance over the original SAM, at https://github.com/mazurowski-lab/finetune-SAM. | [
"['Hanxue Gu' 'Haoyu Dong' 'Jichen Yang' 'Maciej A. Mazurowski']"
]
|
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