"representation learning" Papers
170 papers found • Page 3 of 4
Conference
Wasserstein-Regularized Conformal Prediction under General Distribution Shift
Rui Xu, Chao Chen, Yue Sun et al.
WildSAT: Learning Satellite Image Representations from Wildlife Observations
Rangel Daroya, Elijah Cole, Oisin Mac Aodha et al.
You Are Your Own Best Teacher: Achieving Centralized-level Performance in Federated Learning under Heterogeneous and Long-tailed Data
Shanshan Yan, Zexi Li, Chao Wu et al.
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 Information Theoretical View for Out-Of-Distribution Detection
Jinjing Hu, Wenrui Liu, Hong Chang 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.
CricaVPR: Cross-image Correlation-aware Representation Learning for Visual Place Recognition
Feng Lu, Xiangyuan Lan, Lijun Zhang 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
Efficient Vision-Language Pre-training by Cluster Masking
Zihao Wei, Zixuan Pan, Andrew Owens
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
Equivariant Spatio-Temporal Self-Supervision for LiDAR Object Detection
Deepti Hegde, Suhas Lohit, Kuan-Chuan Peng et al.
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.
Federated Generalized Category Discovery
Nan Pu, Wenjing Li, Xinyuan Ji 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.
From Fake to Real: Pretraining on Balanced Synthetic Images to Prevent Spurious Correlations in Image Recognition
Maan Qraitem, Kate Saenko, Bryan Plummer
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.