Representation Learning
Representation learning aims to create meaningful and efficient data representations that capture underlying structure and facilitate downstream tasks like classification, prediction, and control. Current research focuses on developing robust and generalizable representations, often employing techniques like contrastive learning, transformers, and mixture-of-experts models, addressing challenges such as disentanglement, handling noisy or sparse data, and improving efficiency in multi-task and continual learning scenarios. These advancements have significant implications for various fields, improving the performance and interpretability of machine learning models across diverse applications, from recommendation systems to medical image analysis and causal inference.
Papers - Page 3
Modeling Fine-Grained Hand-Object Dynamics for Egocentric Video Representation Learning
Baoqi Pei, Yifei Huang, Jilan Xu, Guo Chen, Yuping He, Lijin Yang, Yali Wang, Weidi Xie, Yu Qiao, Fei Wu, Limin WangZhejiang University●Shanghai Artificial Intelligence Laboratory●The University of Tokyo●Fudan University●Nanjing University●SIAT●Shanghai...+1Improve Representation for Imbalanced Regression through Geometric Constraints
Zijian Dong, Yilei Wu, Chongyao Chen, Yingtian Zou, Yichi Zhang, Juan Helen ZhouNational University of Singapore●Duke University
Synergy Between Sufficient Changes and Sparse Mixing Procedure for Disentangled Representation Learning
Zijian Li, Shunxing Fan, Yujia Zheng, Ignavier Ng, Shaoan Xie, Guangyi Chen, Xinshuai Dong, Ruichu Cai, Kun ZhangCarnegie Mellon University●Mohamed bin Zayed University of Artificial Intelligence●Guangdong University of TechnologyLanguage Model Mapping in Multimodal Music Learning: A Grand Challenge Proposal
Daniel Chin, Gus XiaNYU Shanghai●MBZUAI
Few-Shot, No Problem: Descriptive Continual Relation Extraction
Nguyen Xuan Thanh, Anh Duc Le, Quyen Tran, Thanh-Thien Le, Linh Ngo Van, Thien Huu NguyenOraichain Labs●Hanoi University of Science and Technology●VinAI Research●University of OregonSuPreME: A Supervised Pre-training Framework for Multimodal ECG Representation Learning
Mingsheng Cai, Jiuming Jiang, Wenhao Huang, Che Liu, Rossella ArcucciThe University of Edinburgh●Ltd●Imperial College LondoncMIM: A Contrastive Mutual Information Framework for Unified Generative and Discriminative Representation Learning
Micha LivneNVIDIA
Improving Representation Learning of Complex Critical Care Data with ICU-BERT
Ricardo Santos, André V. Carreiro, Xi Peng, Hugo Gamboa, Holger FröhlichFraunhofer AICOS●NOVA School of Science and Technology●University of Delaware●Fraunhofer SCAI●University of BonnMixtraining: A Better Trade-Off Between Compute and Performance
Zexin Li, Jiancheng Zhang, Yinglun Zhu, Cong LiuRiversideInvariance Pair-Guided Learning: Enhancing Robustness in Neural Networks
Martin Surner, Abdelmajid Khelil, Ludwig BothmannLandshut University of Applied Sciences●LMU Munich●Munich Center for Machine Learning (MCML)
Discovery and Deployment of Emergent Robot Swarm Behaviors via Representation Learning and Real2Sim2Real Transfer
Connor Mattson, Varun Raveendra, Ricardo Vega, Cameron Nowzari, Daniel S. Drew, Daniel S. BrownGeneralization Guarantees for Representation Learning via Data-Dependent Gaussian Mixture Priors
Milad Sefidgaran, Abdellatif Zaidi, Piotr KrasnowskiHuawei Technologies France●Université Gustave Eiffel
Learning Transformation-Isomorphic Latent Space for Accurate Hand Pose Estimation
Kaiwen Ren, Lei Hu, Zhiheng Zhang, Yongjing Ye, Shihong XiaUniversity of Chinese Academic of ScienceMyna: Masking-Based Contrastive Learning of Musical Representations
Ori Yonay, Tracy Hammond, Tianbao YangTexas A&M University