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.

Papers

November 10, 2024

A Variance Minimization Approach to Temporal-Difference Learning
Xingguo Chen, Yu Gong, Shangdong Yang, Wenhao Wang
LeArning Abstract Value Based Reinforcement Learning Error Minimizing Noise
Layer-Wise Feature Metric of Semantic-Pixel Matching for Few-Shot Learning
Hao Tang, Junhao Lu, Guoheng Huang, Ming Li, Xuhang Chen, Guo Zhong, Zhengguang Tan, Zinuo Li
LeArning Abstract Pixel Level Image Pair Layer Wise Semantic Embeddings
Activation Map Compression through Tensor Decomposition for Deep Learning
Le-Trung Nguyen, Aël Quélennec, Enzo Tartaglione, Samuel Tardieu, Van-Tam Nguyen
Deep Learning LeArning Abstract Back Propagation Tensor Decomposition Device Inference Tensor Decomposition Method Activation Compression Higher Order Singular Value Decomposition

November 9, 2024

SEEKR: Selective Attention-Guided Knowledge Retention for Continual Learning of Large Language Models
Jinghan He, Haiyun Guo, Kuan Zhu, Zihan Zhao, Ming Tang, Jinqiao Wang
LeArning Abstract Continual LEArning Continual Learning Benchmark Federat\textbf{T}ed Cl\textbf{A}ss Continual Lea\textbf{R}nin\textbf{G Replay Based Continual Learning Knowledge Retention Fine Grained Knowledge
CGLearn: Consistent Gradient-Based Learning for Out-of-Distribution Generalization
Jawad Chowdhury, Gabriel Terejanu
LeArning Abstract Strong Generalization Strong Consistency Causal Structure Distribution Data Causal Direction Invariant Predictor Gradient Consistency

November 8, 2024

November 7, 2024

Learning in Budgeted Auctions with Spacing Objectives
Giannis Fikioris, Robert Kleinberg, Yoav Kolumbus, Raunak Kumar, Yishay Mansour, Éva Tardos
LeArning Abstract Optimal Policy Price Auction Optimal Bidding Strategy Online Bayesian
Learning from Demonstration with Hierarchical Policy Abstractions Toward High-Performance and Courteous Autonomous Racing
Chanyoung Chung, Hyunki Seong, David Hyunchul Shim
LeArning Abstract Multi Agent Trajectory Planning Textual Demonstration Autonomous Racing High Performance Policy Architecture
Boosting the Efficiency of Metaheuristics Through Opposition-Based Learning in Optimum Locating of Control Systems in Tall Buildings
Salar Farahmand-Tabar, Sina Shirgir
LeArning Abstract High Efficiency Metaheuristic Algorithm Residential Building Facility Location Problem Control System Engineering Problem Metaheuristic Optimization Algorithm
Constrained Latent Action Policies for Model-Based Offline Reinforcement Learning
Marvin Alles, Philip Becker-Ehmck, Patrick van der Smagt, Maximilian Karl
Reinforcement Learning LeArning Abstract Offline Reinforcement Learning Model Based Offline Reinforcement Learning Latent Action Latent Action Space
Hypercube Policy Regularization Framework for Offline Reinforcement Learning
Yi Shen, Hanyan Huang
LeArning Abstract Offline Reinforcement Learning Constrained Policy Optimization Policy Regularization
FedDP: Privacy-preserving method based on federated learning for histopathology image segmentation
Liangrui Pan, Mao Huang, Lian Wang, Pinle Qin, Shaoliang Peng
LeArning Abstract Differential Privacy Practical Method Image Segmentation Medical Image Data Deep Learning Technology Federated Learning Framework

November 6, 2024

Quantum Diffusion Models for Few-Shot Learning
Ruhan Wang, Ye Wang, Jing Liu, Toshiaki Koike-Akino
LeArning Abstract Diffusion Model Shot Learning Quantum Machine Learning Parameterized Quantum Circuit
Calibrating for the Future:Enhancing Calorimeter Longevity with Deep Learning
S. Ali, A.S. Ryzhikov, D.A. Derkach, F.D. Ratnikov, V.O. Bocharnikov
LeArning Abstract Future Reasoning High Energy Physic Wasserstein Gan Calorimeter Simulation Particle Physic
Overcoming label shift in targeted federated learning
Edvin Listo Zec, Adam Breitholtz, Fredrik D. Johansson
LeArning Abstract Label Distribution Label Shift Distribution Mismatch
UnityGraph: Unified Learning of Spatio-temporal features for Multi-person Motion Prediction
Kehua Qu, Rui Ding, Jin Tang
LeArning Abstract Temporal Feature Graph Node Multi Person Dual Path Multivariate Statistical Network Monitoring
Imagined Potential Games: A Framework for Simulating, Learning and Evaluating Interactive Behaviors
Lingfeng Sun, Yixiao Wang, Pin-Yun Hung, Changhao Wang, Xiang Zhang, Zhuo Xu, Masayoshi Tomizuka
LeArning Abstract New Framework Simulation Study Social Navigation Dynamic Scenario Cooperative Game Agent Model Potential Game
SEGMN: A Structure-Enhanced Graph Matching Network for Graph Similarity Learning
Wenjun Wang, Jiacheng Lu, Kejia Chen, Zheng Liu, Shilong Sang
LeArning Abstract Graph Matching Graph Similarity Multi Graph Matching Graph Similarity Computation

LeArning Abstract

Papers

A Variance Minimization Approach to Temporal-Difference Learning

Layer-Wise Feature Metric of Semantic-Pixel Matching for Few-Shot Learning

Activation Map Compression through Tensor Decomposition for Deep Learning

SEEKR: Selective Attention-Guided Knowledge Retention for Continual Learning of Large Language Models

CGLearn: Consistent Gradient-Based Learning for Out-of-Distribution Generalization

Predicting Stroke through Retinal Graphs and Multimodal Self-supervised Learning

Towards Active Flow Control Strategies Through Deep Reinforcement Learning

Bridging the Gap between Learning and Inference for Diffusion-Based Molecule Generation

Learning in Budgeted Auctions with Spacing Objectives

Learning from Demonstration with Hierarchical Policy Abstractions Toward High-Performance and Courteous Autonomous Racing

Boosting the Efficiency of Metaheuristics Through Opposition-Based Learning in Optimum Locating of Control Systems in Tall Buildings

Constrained Latent Action Policies for Model-Based Offline Reinforcement Learning

Hypercube Policy Regularization Framework for Offline Reinforcement Learning

FedDP: Privacy-preserving method based on federated learning for histopathology image segmentation

Quantum Diffusion Models for Few-Shot Learning

Calibrating for the Future:Enhancing Calorimeter Longevity with Deep Learning

Overcoming label shift in targeted federated learning

UnityGraph: Unified Learning of Spatio-temporal features for Multi-person Motion Prediction

Imagined Potential Games: A Framework for Simulating, Learning and Evaluating Interactive Behaviors

SEGMN: A Structure-Enhanced Graph Matching Network for Graph Similarity Learning