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
Point2SSM++: Self-Supervised Learning of Anatomical Shape Models from Point Clouds
Jadie Adams, Shireen Elhabian
Learning Coarse-Grained Dynamics on Graph
Yin Yu, John Harlim, Daning Huang, Yan Li
Does Machine Bring in Extra Bias in Learning? Approximating Fairness in Models Promptly
Yijun Bian, Yujie Luo
Learning from Partial Label Proportions for Whole Slide Image Segmentation
Shinnosuke Matsuo, Daiki Suehiro, Seiichi Uchida, Hiroaki Ito, Kazuhiro Terada, Akihiko Yoshizawa, Ryoma Bise
ICAL: Implicit Character-Aided Learning for Enhanced Handwritten Mathematical Expression Recognition
Jianhua Zhu, Liangcai Gao, Wenqi Zhao
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