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
Intra-task Mutual Attention based Vision Transformer for Few-Shot Learning
Weihao Jiang, Chang Liu, Kun He
Learning from Students: Applying t-Distributions to Explore Accurate and Efficient Formats for LLMs
Jordan Dotzel, Yuzong Chen, Bahaa Kotb, Sushma Prasad, Gang Wu, Sheng Li, Mohamed S. Abdelfattah, Zhiru Zhang
A Survey of Few-Shot Learning for Biomedical Time Series
Chenqi Li, Timothy Denison, Tingting Zhu
Learning label-label correlations in Extreme Multi-label Classification via Label Features
Siddhant Kharbanda, Devaansh Gupta, Erik Schultheis, Atmadeep Banerjee, Cho-Jui Hsieh, Rohit Babbar
Learning from Evolution: Improving Collective Decision-Making Mechanisms using Insights from Evolutionary Robotics
Tanja Katharina Kaiser
Learning under Imitative Strategic Behavior with Unforeseeable Outcomes
Tian Xie, Zhiqun Zuo, Mohammad Mahdi Khalili, Xueru Zhang
Learning to Boost the Performance of Stable Nonlinear Systems
Luca Furieri, Clara Lucía Galimberti, Giancarlo Ferrari-Trecate
Quantum-Classical Separations in Shallow-Circuit-Based Learning with and without Noises
Zhihan Zhang, Weiyuan Gong, Weikang Li, Dong-Ling Deng
Learning to Compose: Improving Object Centric Learning by Injecting Compositionality
Whie Jung, Jaehoon Yoo, Sungjin Ahn, Seunghoon Hong
ULLER: A Unified Language for Learning and Reasoning
Emile van Krieken, Samy Badreddine, Robin Manhaeve, Eleonora Giunchiglia
Detection of ransomware attacks using federated learning based on the CNN model
Hong-Nhung Nguyen, Ha-Thanh Nguyen, Damien Lescos
A Unified Theory of Exact Inference and Learning in Exponential Family Latent Variable Models
Sacha Sokoloski
PEFSL: A deployment Pipeline for Embedded Few-Shot Learning on a FPGA SoC
Lucas Grativol Ribeiro, Lubin Gauthier, Mathieu Leonardon, Jérémy Morlier, Antoine Lavrard-Meyer, Guillaume Muller, Virginie Fresse, Matthieu Arzel
Learning to Communicate Functional States with Nonverbal Expressions for Improved Human-Robot Collaboration
Liam Roy, Dana Kulic, Elizabeth Croft
Learning general Gaussian mixtures with efficient score matching
Sitan Chen, Vasilis Kontonis, Kulin Shah
Learning with Norm Constrained, Over-parameterized, Two-layer Neural Networks
Fanghui Liu, Leello Dadi, Volkan Cevher
A Framework for Learning and Reusing Robotic Skills
Brendan Hertel, Nhu Tran, Meriem Elkoudi, Reza Azadeh