dimensional collapse

Combatting Dimensional Collapse in LLM Pre-Training Data via Submodular File Selection ICLR 2025 HEX: Hierarchical Emergence Exploitation in Self-Supervised Algorithms WACV 2025 Tackling Dimensional Collapse toward Comprehensive Universal Domain Adaptation ICML 2025 Graph Neural Networks (with Proper Weights) Can Escape Oversmoothing ACML 2024 LDReg: Local Dimensionality Regularized Self-Supervised Learning ICLR 2024 Preventing Dimensional Collapse in Self-Supervised Learning via Orthogonality Regularization NeurIPS 2024 Probabilistic Self-supervised Representation Learning via Scoring Rules Minimization ICLR 2024 Rethinking the Uniformity Metric in Self-Supervised Learning ICLR 2024 BarlowRL: Barlow Twins for Data-Efficient Reinforcement Learning ACML 2023 ContraNorm: A Contrastive Learning Perspective on Oversmoothing and Beyond ICLR 2023 Graph Contrastive Learning with Group Whitening ACML 2023 Mitigating the Popularity Bias of Graph Collaborative Filtering: A Dimensional Collapse Perspective NeurIPS 2023 Towards Understanding and Mitigating Dimensional Collapse in Heterogeneous Federated Learning ICLR 2023 What shapes the loss landscape of self supervised learning? ICLR 2023 How Mask Matters: Towards Theoretical Understandings of Masked Autoencoders NeurIPS 2022 The Mechanism of Prediction Head in Non-contrastive Self-supervised Learning NeurIPS 2022 Understanding Dimensional Collapse in Contrastive Self-supervised Learning ICLR 2022 On Feature Decorrelation in Self-Supervised Learning ICCV 2021