Strong Generalization
Strong generalization, the ability of machine learning models to perform well on unseen data, is a central objective in current research. Active areas of investigation include improving the robustness of self-supervised learning, understanding the optimization dynamics of transformers and other architectures (including CNNs and RNNs), and developing methods to enhance generalization through data augmentation, regularization techniques (e.g., logical regularization, consistency regularization), and improved training strategies (e.g., few-shot learning, meta-learning). These advancements are crucial for building reliable and adaptable AI systems across diverse applications, from image classification and natural language processing to healthcare and robotics.
Papers
mlan: language-based instruction tuning improves zero-shot generalization of multimodal large language models
Jianhong Tu, Zhuohao Ni, Nicholas Crispino, Zihao Yu, Michael Bendersky, Beliz Gunel, Ruoxi Jia, Xin Liu, Lingjuan Lyu, Dawn Song, Chenguang Wang
Towards Sample-Efficiency and Generalization of Transfer and Inverse Reinforcement Learning: A Comprehensive Literature Review
Hossein Hassani, Roozbeh Razavi-Far, Mehrdad Saif, Liang Lin
Federated Domain Generalization via Prompt Learning and Aggregation
Shuai Gong, Chaoran Cui, Chunyun Zhang, Wenna Wang, Xiushan Nie, Lei Zhu
One-Shot Manipulation Strategy Learning by Making Contact Analogies
Yuyao Liu, Jiayuan Mao, Joshua Tenenbaum, Tomás Lozano-Pérez, Leslie Pack Kaelbling
Are nuclear masks all you need for improved out-of-domain generalisation? A closer look at cancer classification in histopathology
Dhananjay Tomar, Alexander Binder, Andreas Kleppe
Stability and Generalization for Distributed SGDA
Miaoxi Zhu, Yan Sun, Li Shen, Bo Du, Dacheng Tao
Enhancing generalization in high energy physics using white-box adversarial attacks
Franck Rothen, Samuel Klein, Matthew Leigh, Tobias Golling
Exact, Tractable Gauss-Newton Optimization in Deep Reversible Architectures Reveal Poor Generalization
Davide Buffelli, Jamie McGowan, Wangkun Xu, Alexandru Cioba, Da-shan Shiu, Guillaume Hennequin, Alberto Bernacchia
Doubly Mild Generalization for Offline Reinforcement Learning
Yixiu Mao, Qi Wang, Yun Qu, Yuhang Jiang, Xiangyang Ji
What Do Learning Dynamics Reveal About Generalization in LLM Reasoning?
Katie Kang, Amrith Setlur, Dibya Ghosh, Jacob Steinhardt, Claire Tomlin, Sergey Levine, Aviral Kumar
Generalization of Brady-Yong Algorithm for Fast Hough Transform to Arbitrary Image Size
Danil Kazimirov, Dmitry Nikolaev, Ekaterina Rybakova, Arseniy Terekhin
Understanding Generalization in Quantum Machine Learning with Margins
Tak Hur, Daniel K. Park
GSL-PCD: Improving Generalist-Specialist Learning with Point Cloud Feature-based Task Partitioning
Xiu Yuan
Personalize to generalize: Towards a universal medical multi-modality generalization through personalization
Zhaorui Tan, Xi Yang, Tan Pan, Tianyi Liu, Chen Jiang, Xin Guo, Qiufeng Wang, Anh Nguyen, Yuan Qi, Kaizhu Huang, Yuan Cheng
CGLearn: Consistent Gradient-Based Learning for Out-of-Distribution Generalization
Jawad Chowdhury, Gabriel Terejanu
Generalizable Single-Source Cross-modality Medical Image Segmentation via Invariant Causal Mechanisms
Boqi Chen, Yuanzhi Zhu, Yunke Ao, Sebastiano Caprara, Reto Sutter, Gunnar Rätsch, Ender Konukoglu, Anna Susmelj
Dialectal Coverage And Generalization in Arabic Speech Recognition
Amirbek Djanibekov, Hawau Olamide Toyin, Raghad Alshalan, Abdullah Alitr, Hanan Aldarmaki