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
Cosmology with Persistent Homology: Parameter Inference via Machine Learning
Juan Calles, Jacky H. T. Yip, Gabriella Contardo, Jorge Noreña, Adam Rouhiainen, Gary Shiu
Learning Disentangled Equivariant Representation for Explicitly Controllable 3D Molecule Generation
Haoran Liu, Youzhi Luo, Tianxiao Li, James Caverlee, Martin Renqiang Min
Hierarchical Subspaces of Policies for Continual Offline Reinforcement Learning
Anthony Kobanda, Rémy Portelas, Odalric-Ambrym Maillard, Ludovic Denoyer
Continuous latent representations for modeling precipitation with deep learning
Gokul Radhakrishnan, Rahul Sundar, Nishant Parashar, Antoine Blanchard, Daiwei Wang, Boyko Dodov
GBRIP: Granular Ball Representation for Imbalanced Partial Label Learning
Jintao Huang, Yiu-ming Cheung, Chi-man Vong, Wenbin Qian
FedPIA -- Permuting and Integrating Adapters leveraging Wasserstein Barycenters for Finetuning Foundation Models in Multi-Modal Federated Learning
Pramit Saha, Divyanshu Mishra, Felix Wagner, Konstantinos Kamnitsas, J. Alison Noble
Covariances for Free: Exploiting Mean Distributions for Federated Learning with Pre-Trained Models
Dipam Goswami, Simone Magistri, Kai Wang, Bartłomiej Twardowski, Andrew D. Bagdanov, Joost van de Weijer
Learning from Massive Human Videos for Universal Humanoid Pose Control
Jiageng Mao, Siheng Zhao, Siqi Song, Tianheng Shi, Junjie Ye, Mingtong Zhang, Haoran Geng, Jitendra Malik, Vitor Guizilini, Yue Wang
Scaling of Search and Learning: A Roadmap to Reproduce o1 from Reinforcement Learning Perspective
Zhiyuan Zeng, Qinyuan Cheng, Zhangyue Yin, Bo Wang, Shimin Li, Yunhua Zhou, Qipeng Guo, Xuanjing Huang, Xipeng Qiu
When Should We Prefer State-to-Visual DAgger Over Visual Reinforcement Learning?
Tongzhou Mu, Zhaoyang Li, Stanisław Wiktor Strzelecki, Xiu Yuan, Yunchao Yao, Litian Liang, Hao Su
Exploring Transformer-Augmented LSTM for Temporal and Spatial Feature Learning in Trajectory Prediction
Chandra Raskoti, Weizi Li
Tilted Quantile Gradient Updates for Quantile-Constrained Reinforcement Learning
Chenglin Li, Guangchun Ruan, Hua Geng
Learning of Patch-Based Smooth-Plus-Sparse Models for Image Reconstruction
Stanislas Ducotterd, Sebastian Neumayer, Michael Unser
SMOSE: Sparse Mixture of Shallow Experts for Interpretable Reinforcement Learning in Continuous Control Tasks
Mátyás Vincze, Laura Ferrarotti, Leonardo Lucio Custode, Bruno Lepri, Giovanni Iacca
Noise-based Local Learning using Stochastic Magnetic Tunnel Junctions
Kees Koenders, Leo Schnitzpan, Fabian Kammerbauer, Sinan Shu, Gerhard Jakob, Mathis Kläui, Johan Mentink, Nasir Ahmad, Marcel van Gerven
CALA: A Class-Aware Logit Adapter for Few-Shot Class-Incremental Learning
Chengyan Liu, Linglan Zhao, Fan Lyu, Kaile Du, Fuyuan Hu, Tao Zhou
PBVS 2024 Solution: Self-Supervised Learning and Sampling Strategies for SAR Classification in Extreme Long-Tail Distribution
Yuhyun Kim, Minwoo Kim, Hyobin Park, Jinwook Jung, Dong-Geol Choi
Aspect-Based Few-Shot Learning
Tim van Engeland, Lu Yin, Vlado Menkovski