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
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
OmniPrism: Learning Disentangled Visual Concept for Image Generation
Yangyang Li, Daqing Liu, Wu Liu, Allen He, Xinchen Liu, Yongdong Zhang, Guoqing Jin
No More Tuning: Prioritized Multi-Task Learning with Lagrangian Differential Multiplier Methods
Zhengxing Cheng, Yuheng Huang, Zhixuan Zhang, Dan Ou, Qingwen Liu
Learning to Navigate in Mazes with Novel Layouts using Abstract Top-down Maps
Linfeng Zhao, Lawson L.S. Wong
Industrial-scale Prediction of Cement Clinker Phases using Machine Learning
Sheikh Junaid Fayaz, Nestor Montiel-Bohorquez, Shashank Bishnoi, Matteo Romano, Manuele Gatti, N. M. Anoop Krishnan
A Digital twin for Diesel Engines: Operator-infused PINNs with Transfer Learning for Engine Health Monitoring
Kamaljyoti Nath, Varun Kumar, Daniel J. Smith, George Em Karniadakis
Learning Human-Aware Robot Policies for Adaptive Assistance
Jason Qin, Shikun Ban, Wentao Zhu, Yizhou Wang, Dimitris Samaras
SPGL: Enhancing Session-based Recommendation with Single Positive Graph Learning
Tiantian Liang, Zhe Yang