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
Pattern based learning and optimisation through pricing for bin packing problem
Huayan Zhang, Ruibin Bai, Tie-Yan Liu, Jiawei Li, Bingchen Lin, Jianfeng Ren
Learning from Complementary Features
Kosuke Sugiyama, Masato Uchida
Learning Differentially Private Diffusion Models via Stochastic Adversarial Distillation
Bochao Liu, Pengju Wang, Shiming Ge
Rethinking Knowledge Transfer in Learning Using Privileged Information
Danil Provodin, Bram van den Akker, Christina Katsimerou, Maurits Kaptein, Mykola Pechenizkiy
May the Forgetting Be with You: Alternate Replay for Learning with Noisy Labels
Monica Millunzi, Lorenzo Bonicelli, Angelo Porrello, Jacopo Credi, Petter N. Kolm, Simone Calderara
Theoretical Proportion Label Perturbation for Learning from Label Proportions in Large Bags
Shunsuke Kubo, Shinnosuke Matsuo, Daiki Suehiro, Kazuhiro Terada, Hiroaki Ito, Akihiko Yoshizawa, Ryoma Bise
Evaluating Alternative Training Interventions Using Personalized Computational Models of Learning
Christopher James MacLellan, Kimberly Stowers, Lisa Brady
Learning from the few: Fine-grained approach to pediatric wrist pathology recognition on a limited dataset
Ammar Ahmed, Ali Shariq Imran, Zenun Kastrati, Sher Muhammad Daudpota, Mohib Ullah, Waheed Noord
Scaling Cross-Embodied Learning: One Policy for Manipulation, Navigation, Locomotion and Aviation
Ria Doshi, Homer Walke, Oier Mees, Sudeep Dasari, Sergey Levine
SelfDRSC++: Self-Supervised Learning for Dual Reversed Rolling Shutter Correction
Wei Shang, Dongwei Ren, Wanying Zhang, Qilong Wang, Pengfei Zhu, Wangmeng Zuo