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.
2549papers
Papers - Page 72
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Learning accurate and interpretable tree-based models
Maria-Florina Balcan, Dravyansh SharmaLearning the Language of Protein Structure
Benoit Gaujac, Jérémie Donà, Liviu Copoiu, Timothy Atkinson, Thomas Pierrot, Thomas D. BarrettLearning from Linear Algebra: A Graph Neural Network Approach to Preconditioner Design for Conjugate Gradient Solvers
Vladislav Trifonov, Alexander Rudikov, Oleg Iliev, Yuri M. Laevsky, Ivan Oseledets, Ekaterina MuravlevaLearning to Discretize Denoising Diffusion ODEs
Vinh Tong, Hoang Trung-Dung, Anji Liu, Guy Van den Broeck, Mathias NiepertLearning Beyond Pattern Matching? Assaying Mathematical Understanding in LLMs
Siyuan Guo, Aniket Didolkar, Nan Rosemary Ke, Anirudh Goyal, Ferenc Huszár, Bernhard SchölkopfUnlearning during Learning: An Efficient Federated Machine Unlearning Method
Hanlin Gu, Gongxi Zhu, Jie Zhang, Xinyuan Zhao, Yuxing Han, Lixin Fan, Qiang YangLearning to optimize: A tutorial for continuous and mixed-integer optimization
Xiaohan Chen, Jialin Liu, Wotao YinLearning from True-False Labels via Multi-modal Prompt Retrieving
Zhongnian Li, Jinghao Xu, Peng Ying, Meng Wei, Xinzheng Xu
May 23, 2024
LOVA3: Learning to Visual Question Answering, Asking and Assessment
Henry Hengyuan Zhao, Pan Zhou, Difei Gao, Zechen Bai, Mike Zheng ShouMOD-UV: Learning Mobile Object Detectors from Unlabeled Videos
Yihong Sun, Bharath HariharanLearning Multi-dimensional Human Preference for Text-to-Image Generation
Sixian Zhang, Bohan Wang, Junqiang Wu, Yan Li, Tingting Gao, Di Zhang, Zhongyuan WangLearning with Fitzpatrick Losses
Seta Rakotomandimby, Jean-Philippe Chancelier, Michel de Lara, Mathieu BlondelLARS-VSA: A Vector Symbolic Architecture For Learning with Abstract Rules
Mohamed Mejri, Chandramouli Amarnath, Abhijit ChatterjeeLogarithmic Smoothing for Pessimistic Off-Policy Evaluation, Selection and Learning
Otmane Sakhi, Imad Aouali, Pierre Alquier, Nicolas Chopin