LeArning Abstract

Learning, in the context of these papers, encompasses a broad range of research focused on improving the efficiency, robustness, and adaptability of machine learning models across diverse applications. Current efforts concentrate on developing novel self-supervised learning techniques, particularly for structured data like tabular formats, and on leveraging low-rank adaptations for efficient fine-tuning of large language and other foundation models. These advancements are significant because they address key challenges in data efficiency, computational cost, and the generalization capabilities of machine learning systems, impacting fields ranging from personalized medicine to autonomous robotics.

2549papers

Papers - Page 7

March 11, 2025

Data-driven Nonlinear Modal Analysis with Physics-constrained Deep Learning: Numerical and Experimental Study
Abdolvahhab Rostamijavanani, Shanwu Li, Yongchao Yang
Michigan Technological University
Structural Dynamic Modal Analysis Numerical Result Dynamic Mode Decomposition LeArning Abstract Experimental Study Nonlinear Dynamic Vibration Data
Smoothing ADMM for Non-convex and Non-smooth Hierarchical Federated Learning
Reza Mirzaeifard, Stefan Werner
Norwegian University of Science and Technology
Non Convex Penalty Minimax Concave Penalty Alternating Direction Method of Multiplier LeArning Abstract Non Convex Regularization Based Method Objective Function Hierarchical Federated Learning
Extra Clients at No Extra Cost: Overcome Data Heterogeneity in Federated Learning with Filter Decomposition
Wei Chen, Qiang Qiu
Purdue University
Client Collaboration LeArning Abstract Convolutional Filter Data Heterogeneity Global Aggregation Decomposition Method Federated Prompt Cooperation
Learning to Match Unpaired Data with Minimum Entropy Coupling
Mustapha Bounoua, Giulio Franzese, Pietro Michiardi
Ampere Software Technology●EURECOM
Multimodal Data LeArning Abstract Unpaired Data Modality Interaction Min Entropy Single Cell Multi Omics
ExMAG: Learning of Maximally Ancestral Graphs
Petr Ryšavý, Pavel Rytíř, Xiaoyu He, Georgios Korpas, Jakub Mareček
Czech Technical University in Prague●HSBC Quantum Technologies Group●Archimedes Research Unit on AI
Mixed Type Causal Learning Dynamic Bayesian Network LeArning Abstract Ancestral Graph Causal Relationship
CL-MVSNet: Unsupervised Multi-view Stereo with Dual-level Contrastive Learning
Kaiqiang Xiong, Rui Peng, Zhe Zhang, Tianxing Feng, Jianbo Jiao, Feng Gao, Ronggang Wang
Peking University●Peng Cheng Laboratory●Migu Culture Technology Co.●University of Birmingham
LeArning Abstract Multi View Stereo Depth Estimation
Cooperative Bearing-Only Target Pursuit via Multiagent Reinforcement Learning: Design and Experiment
Jianan Li, Zhikun Wang, Susheng Ding, Shiliang Guo, Shiyu Zhao
Shanghai AI Laboratory●Westlake University
Product Design LeArning Abstract State Estimation Pursuit Strategy Optical Experiment Bearing Only Cooperative Autonomous Ground Vehicle Multi Agent Reinforcement Learning
Structural and Statistical Texture Knowledge Distillation and Learning for Segmentation
Deyi Ji, Feng Zhao, Hongtao Lu, Feng Wu, Jieping Ye
University of Science and Technology of China●Shanghai Jiao Tong University●Alibaba Group
Texture Knowledge LeArning Abstract Semantic Segmentation Inner Structure Texture Feature Segmentation Based Approach
Enhancing Traffic Signal Control through Model-based Reinforcement Learning and Policy Reuse
Yihong Li, Chengwei Zhang, Furui Zhan, Wanting Liu, Kailing Zhou, Longji Zheng
Dalian Maritime University
Multi Agent Reinforcement Learning LeArning Abstract Model Based Reinforcement Learning Traffic Pattern Traffic Signal Control Traffic Scenario
Learning second-order TVD flux limiters using differentiable solvers
Chenyang Huang, Amal S. Sebastian, Venkatasubramanian Viswanathan
University of Michigan
Numerical Flux Differentiable Simulation Differentiable Solver Finite Volume LeArning Abstract

March 10, 2025

Learning and Evaluating Hierarchical Feature Representations
Depanshu Sani, Saket Anand
Indraprastha Institute of Information Technology
LeArning Abstract Hierarchical Representation Label Hierarchy Feature Embeddings Vision Transformer
Efficient Distributed Learning over Decentralized Networks with Convoluted Support Vector Machine
Canyi Chen, Nan Qiao, Liping Zhu
University of Michigan●Renmin University of China
High Efficiency Support Vector Machine Decentralized Network Sparse Regression LeArning Abstract ADMM Algorithm
Learning Physics-Based Full-Body Human Reaching and Grasping from Brief Walking References
Yitang Li, Mingxian Lin, Zhuo Lin, Yipeng Deng, Yue Cao, Li Yi
Tsinghua University●Shanghai Qi Zhi Institute●Galbot●The University of Hong Kong●Shanghai Artificial Intelligence Laboratory
Motion Pattern Seated Walk Motion Generation Brief Report LeArning Abstract
Learning and planning for optimal synergistic human-robot coordination in manufacturing contexts
Samuele Sandrini, Marco Faroni, Nicola Pedrocchi
National Research Council of Italy●Politecnico di Milano●Politecnico di Torino
Robotic Agent Task Planning Manufacturing Industry Task Allocation Collaborative Robot LeArning Abstract
Learning A Zero-shot Occupancy Network from Vision Foundation Models via Self-supervised Adaptation
Sihao Lin, Daqi Liu, Ruochong Fu, Dongrui Liu, Andy Song, Hongwei Xie, Zhihui Li, Bing Wang, Xiaojun Chang
RMIT University●Xiaomi Auto●SJTU●USTC●UTS
Adaptation Concern Vision Foundation Model Metric Depth Metric Depth Estimation 3D Supervision LeArning Abstract
Learning Energy-Based Models by Self-normalising the Likelihood
Hugo Senetaire, Paul Jeha, Pierre-Alexandre Mattei, Jes Frellsen
Technical University of Denmark●Université Côte d’Azur●Inria●LJAD●CNRS
Energy Based Model Normalizing Constant Log Likelihood LeArning Abstract Maximum Likelihood
Taking Notes Brings Focus? Towards Multi-Turn Multimodal Dialogue Learning
Jiazheng Liu, Sipeng Zheng, Börje F. Karlsson, Zongqing Lu
Peking University●Beijing Academy of Artificial Intelligence
Multi Turn Dialogue Object Grounding Multimodal Dialogue Selective Focus LeArning Abstract Multimodal Large Language Model

March 9, 2025

Agile Climate-Sensor Design and Calibration Algorithms Using Machine Learning: Experiments From Cape Point
Travis Barrett, Amit Kumar Mishra
University of Cape Town
Optical Experiment Regression Model Random Forest Sensor System Sensor Network Landmark Attention LeArning Abstract State of the Art Calibration Low Cost
Investigating Image Manifolds of 3D Objects: Learning, Shape Analysis, and Comparisons
Benjamin Beaudett, Shenyuan Liang, Anuj Srivastava
Florida State University
Preserving Transformation Image Manifold Consistent Comparison 3D Object Shape Analysis Manifold Learning LeArning Abstract Class Manifold Low Dimensional Manifold
Privacy Protection in Prosumer Energy Management Based on Federated Learning
Yunfeng Li, Xiaolin Li Zhitao Li, Gangqiang Li
Shenzhen University
Federated Prompt Cooperation Home Energy Management FedAvg Algorithm Residential Prosumers LeArning Abstract

LeArning Abstract

Papers - Page 7

Data-driven Nonlinear Modal Analysis with Physics-constrained Deep Learning: Numerical and Experimental Study

Smoothing ADMM for Non-convex and Non-smooth Hierarchical Federated Learning

Extra Clients at No Extra Cost: Overcome Data Heterogeneity in Federated Learning with Filter Decomposition

Learning to Match Unpaired Data with Minimum Entropy Coupling

ExMAG: Learning of Maximally Ancestral Graphs

CL-MVSNet: Unsupervised Multi-view Stereo with Dual-level Contrastive Learning

Cooperative Bearing-Only Target Pursuit via Multiagent Reinforcement Learning: Design and Experiment

Structural and Statistical Texture Knowledge Distillation and Learning for Segmentation

Enhancing Traffic Signal Control through Model-based Reinforcement Learning and Policy Reuse

Learning second-order TVD flux limiters using differentiable solvers

Learning and Evaluating Hierarchical Feature Representations

Efficient Distributed Learning over Decentralized Networks with Convoluted Support Vector Machine

Learning Physics-Based Full-Body Human Reaching and Grasping from Brief Walking References

Learning and planning for optimal synergistic human-robot coordination in manufacturing contexts

Learning A Zero-shot Occupancy Network from Vision Foundation Models via Self-supervised Adaptation

Learning Energy-Based Models by Self-normalising the Likelihood

Taking Notes Brings Focus? Towards Multi-Turn Multimodal Dialogue Learning

Agile Climate-Sensor Design and Calibration Algorithms Using Machine Learning: Experiments From Cape Point

Investigating Image Manifolds of 3D Objects: Learning, Shape Analysis, and Comparisons

Privacy Protection in Prosumer Energy Management Based on Federated Learning