eigenvalue decomposition

Learning Eigenstructures of Unstructured Data Manifolds CVPR 2026 Hallucination Detection in LLMs Using Spectral Features of Attention Maps EMNLP 2025 Temporal Difference Learning: Why It Can Be Fast and How It Will Be Faster ICLR 2025 Entropy Induced Pruning Framework for Convolutional Neural Networks AAAI 2024 INSIDE: LLMs' Internal States Retain the Power of Hallucination Detection ICLR 2024 Proper Laplacian Representation Learning ICLR 2024 An Operator Theoretic Approach for Analyzing Sequence Neural Networks AAAI 2023 Toward Large Kernel Models ICML 2023 Adversarial Eigen Attack on Black-Box Models CVPR 2022 Failure and success of the spectral bias prediction for Laplace Kernel Ridge Regression: the case of low-dimensional data ICML 2022 Faster proximal algorithms for matrix optimization using Jacobi-based eigenvalue methods NeurIPS 2021 How can classical multidimensional scaling go wrong? NeurIPS 2021 Principal Component Hierarchy for Sparse Quadratic Programs ICML 2021 A Practical Riemannian Algorithm for Computing Dominant Generalized Eigenspace UAI 2020 Eliminating the Invariance on the Loss Landscape of Linear Autoencoders ICML 2020 Image Aesthetic Assessment Based on Pairwise Comparison A Unified Approach to Score Regression, Binary Classification, and Personalization ICCV 2019 On Distributed Averaging for Stochastic k-PCA NeurIPS 2019 Scalable Interpretable Multi-Response Regression via SEED JMLR 2019 Selecting the independent coordinates of manifolds with large aspect ratios NeurIPS 2019 Diving into the shallows: a computational perspective on large-scale shallow learning NeurIPS 2017 EigenNet: Towards Fast and Structural Learning of Deep Neural Networks IJCAI 2017 Anchor-Free Correlated Topic Modeling: Identifiability and Algorithm NeurIPS 2016 A posteriori error bounds for joint matrix decomposition problems NeurIPS 2016 Consensus of Non-Rigid Reconstructions CVPR 2016 Correlated-PCA: Principal Components' Analysis when Data and Noise are Correlated NeurIPS 2016