principal component

Differentiable Optimization of Similarity Scores Between Models and Brains ICLR 2025 Interpret and Improve In-Context Learning via the Lens of Input-Label Mappings ACL 2025 Representations Shape Weak-to-Strong Generalization: Theoretical Insights and Empirical Predictions ICML 2025 Sampling Innovation-Based Adaptive Compressive Sensing CVPR 2025 A New Computationally Efficient Algorithm to solve Feature Selection for Functional Data Classification in High-dimensional Spaces ICML 2024 DiffRed: Dimensionality reduction guided by stable rank AISTATS 2024 Reliable Measures of Spread in High Dimensional Latent Spaces ICML 2023 EigenGame Unloaded: When playing games is better than optimizing ICLR 2022 OPEN: Orthogonal Propagation with Ego-Network Modeling NeurIPS 2022 Recycling Model Updates in Federated Learning: Are Gradient Subspaces Low-Rank? ICLR 2022 The dynamics of representation learning in shallow, non-linear autoencoders ICML 2022 Do Wide and Deep Networks Learn the Same Things? Uncovering How Neural Network Representations Vary with Width and Depth ICLR 2021 Regularized linear autoencoders recover the principal components, eventually NeurIPS 2020 Sparse PCA through Low-rank Approximations ICML 2013 The Fast Convergence of Incremental PCA NeurIPS 2013