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
Learning from String Sequences
David Lindsay, Sian Lindsay
Learning A Spiking Neural Network for Efficient Image Deraining
Tianyu Song, Guiyue Jin, Pengpeng Li, Kui Jiang, Xiang Chen, Jiyu Jin
Learning to Solve Geometry Problems via Simulating Human Dual-Reasoning Process
Tong Xiao, Jiayu Liu, Zhenya Huang, Jinze Wu, Jing Sha, Shijin Wang, Enhong Chen
A Universal Growth Rate for Learning with Smooth Surrogate Losses
Anqi Mao, Mehryar Mohri, Yutao Zhong
Learning to Slice Wi-Fi Networks: A State-Augmented Primal-Dual Approach
Yiğit Berkay Uslu, Roya Doostnejad, Alejandro Ribeiro, Navid NaderiAlizadeh
Navigate Beyond Shortcuts: Debiased Learning through the Lens of Neural Collapse
Yining Wang, Junjie Sun, Chenyue Wang, Mi Zhang, Min Yang
Learning To See But Forgetting To Follow: Visual Instruction Tuning Makes LLMs More Prone To Jailbreak Attacks
Georgios Pantazopoulos, Amit Parekh, Malvina Nikandrou, Alessandro Suglia
A General Model for Detecting Learner Engagement: Implementation and Evaluation
Somayeh Malekshahi, Javad M. Kheyridoost, Omid Fatemi
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