2024 "representation learning" Papers
58 papers found • Page 1 of 2
Adaptive Discovering and Merging for Incremental Novel Class Discovery
Guangyao Chen, Peixi Peng, Yangru Huang et al.
A Global Geometric Analysis of Maximal Coding Rate Reduction
Peng Wang, Huikang Liu, Druv Pai et al.
An Unsupervised Approach for Periodic Source Detection in Time Series
Berken Utku Demirel, Christian Holz
Autoencoding Conditional Neural Processes for Representation Learning
Victor Prokhorov, Ivan Titov, Siddharth N
BaCon: Boosting Imbalanced Semi-supervised Learning via Balanced Feature-Level Contrastive Learning
Qianhan Feng, Lujing Xie, Shijie Fang et al.
BeigeMaps: Behavioral Eigenmaps for Reinforcement Learning from Images
Sandesh Adhikary, Anqi Li, Byron Boots
Beyond Prototypes: Semantic Anchor Regularization for Better Representation Learning
Yanqi Ge, Qiang Nie, Ye Huang et al.
Binning as a Pretext Task: Improving Self-Supervised Learning in Tabular Domains
Kyungeun Lee, Ye Seul Sim, Hye-Seung Cho et al.
Bridging Mini-Batch and Asymptotic Analysis in Contrastive Learning: From InfoNCE to Kernel-Based Losses
Panagiotis Koromilas, Giorgos Bouritsas, Theodoros Giannakopoulos et al.
CLAP: Isolating Content from Style through Contrastive Learning with Augmented Prompts
Yichao Cai, Yuhang Liu, Zhen Zhang et al.
CLOSER: Towards Better Representation Learning for Few-Shot Class-Incremental Learning
Junghun Oh, Sungyong Baik, Kyoung Mu Lee
Contrastive Continual Learning with Importance Sampling and Prototype-Instance Relation Distillation
Jiyong Li, Dilshod Azizov, Yang LI et al.
Contrastive Learning for Clinical Outcome Prediction with Partial Data Sources
Xia, Jonathan Wilson, Benjamin Goldstein et al.
Cross-Domain Policy Adaptation by Capturing Representation Mismatch
Jiafei Lyu, Chenjia Bai, Jing-Wen Yang et al.
Data-to-Model Distillation: Data-Efficient Learning Framework
Ahmad Sajedi, Samir Khaki, Lucy Z. Liu et al.
Deep Regression Representation Learning with Topology
Shihao Zhang, Kenji Kawaguchi, Angela Yao
Differentially Private Representation Learning via Image Captioning
Tom Sander, Yaodong Yu, Maziar Sanjabi et al.
Diffusion Language Models Are Versatile Protein Learners
Xinyou Wang, Zaixiang Zheng, Fei YE et al.
Distribution Alignment Optimization through Neural Collapse for Long-tailed Classification
Jintong Gao, He Zhao, Dandan Guo et al.
Dynamic Data Selection for Efficient SSL via Coarse-to-Fine Refinement
Aditay Tripathi, Pradeep Shenoy, Anirban Chakraborty
DySeT: a Dynamic Masked Self-distillation Approach for Robust Trajectory Prediction
MOZHGAN POURKESHAVARZ, Arielle Zhang, Amir Rasouli
Elucidating the Hierarchical Nature of Behavior with Masked Autoencoders
Lucas Stoffl, Andy Bonnetto, Stéphane D'Ascoli et al.
Enhancing Trajectory Prediction through Self-Supervised Waypoint Distortion Prediction
Pranav Singh Chib, Pravendra Singh
Exploring Diverse Representations for Open Set Recognition
Yu Wang, Junxian Mu, Pengfei Zhu et al.
Fast and Sample Efficient Multi-Task Representation Learning in Stochastic Contextual Bandits
Jiabin Lin, Shana Moothedath, Namrata Vaswani
Feasibility Consistent Representation Learning for Safe Reinforcement Learning
Zhepeng Cen, Yihang Yao, Zuxin Liu et al.
Feature Contamination: Neural Networks Learn Uncorrelated Features and Fail to Generalize
Tianren Zhang, Chujie Zhao, Guanyu Chen et al.
FedSC: Provable Federated Self-supervised Learning with Spectral Contrastive Objective over Non-i.i.d. Data
Shusen Jing, Anlan Yu, Shuai Zhang et al.
Graph2Tac: Online Representation Learning of Formal Math Concepts
Lasse Blaauwbroek, Mirek Olšák, Jason Rute et al.
How Learning by Reconstruction Produces Uninformative Features For Perception
Randall Balestriero, Yann LeCun
InterLUDE: Interactions between Labeled and Unlabeled Data to Enhance Semi-Supervised Learning
Zhe Huang, Xiaowei Yu, Dajiang Zhu et al.
Isometric Representation Learning for Disentangled Latent Space of Diffusion Models
Jaehoon Hahm, Junho Lee, Sunghyun Kim et al.
Learning Shadow Variable Representation for Treatment Effect Estimation under Collider Bias
Baohong Li, Haoxuan Li, Ruoxuan Xiong et al.
Learning the Unlearned: Mitigating Feature Suppression in Contrastive Learning
Jihai Zhang, Xiang Lan, Xiaoye Qu et al.
LEVI: Generalizable Fine-tuning via Layer-wise Ensemble of Different Views
Yuji Roh, Qingyun Liu, Huan Gui et al.
Matrix Information Theory for Self-Supervised Learning
Yifan Zhang, Zhiquan Tan, Jingqin Yang et al.
Möbius Transform for Mitigating Perspective Distortions in Representation Learning
Prakash Chandra Chhipa, Meenakshi Subhash Chippa, Kanjar De et al.
MOKD: Cross-domain Finetuning for Few-shot Classification via Maximizing Optimized Kernel Dependence
Hongduan Tian, Feng Liu, Tongliang Liu et al.
Neural Causal Abstractions
Kevin Xia, Elias Bareinboim
Neural Collapse meets Differential Privacy: Curious behaviors of NoisyGD with Near-Perfect Representation Learning
Chendi Wang, Yuqing Zhu, Weijie Su et al.
Non-parametric Representation Learning with Kernels
Hebaixu Wang, Meiqi Gong, Xiaoguang Mei et al.
Overcoming Data and Model heterogeneities in Decentralized Federated Learning via Synthetic Anchors
Chun-Yin Huang, Kartik Srinivas, Xin Zhang et al.
Provable Multi-Task Representation Learning by Two-Layer ReLU Neural Networks
Liam Collins, Hamed Hassani, Mahdi Soltanolkotabi et al.
Provable Representation with Efficient Planning for Partially Observable Reinforcement Learning
Hongming Zhang, Tongzheng Ren, Chenjun Xiao et al.
Reducing Balancing Error for Causal Inference via Optimal Transport
Yuguang Yan, Hao Zhou, Zeqin Yang et al.
Regularizing with Pseudo-Negatives for Continual Self-Supervised Learning
Sungmin Cha, Kyunghyun Cho, Taesup Moon
Revisiting Disentanglement in Downstream Tasks: A Study on Its Necessity for Abstract Visual Reasoning
Ruiqian Nai, Zixin Wen, Ji Li et al.
Rich-Observation Reinforcement Learning with Continuous Latent Dynamics
Yuda Song, Lili Wu, Dylan Foster et al.
Scaling Up Semi-supervised Learning with Unconstrained Unlabelled Data
Shuvendu Roy, Ali Etemad
SCoRe: Submodular Combinatorial Representation Learning
Anay Majee, Suraj Kothawade, Krishnateja Killamsetty et al.