Sparse Optimization

Rethinking Asymmetric Quantization: Hidden Symmetry in Vision Model Weights CVPR 2026 S2FT: Parameter-Efficient Fine-Tuning in Sparse Spectrum Domain CVPR 2026 Sparse Structure Exploration and Re-optimization for Vision Transformer UAI 2025 Constrained Implicit Learning Framework for Neural Network Sparsification ACML 2024 Learning Neural Networks with Sparse Activations COLT 2024 CAP: Correlation-Aware Pruning for Highly-Accurate Sparse Vision Models NeurIPS 2023 Graph Structure Learning via Lottery Hypothesis at Scale ACML 2023 The Emergence of Essential Sparsity in Large Pre-trained Models: The Weights that Matter NeurIPS 2023 DictFormer: Tiny Transformer with Shared Dictionary ICLR 2022 Dual Lottery Ticket Hypothesis ICLR 2022 Dynamic Forward and Backward Sparse Training (DFBST): Accelerated Deep Learning through Completely Sparse Training Schedule ACML 2022 Learning N:M Fine-grained Structured Sparse Neural Networks From Scratch ICLR 2021 GLAD: Learning Sparse Graph Recovery ICLR 2020 An Adaptive Empirical Bayesian Method for Sparse Deep Learning NeurIPS 2019 Hyper-Laplacian Regularized Unidirectional Low-Rank Tensor Recovery for Multispectral Image Denoising CVPR 2017 Learning Structured Sparsity in Deep Neural Networks NeurIPS 2016 Maximal Sparsity with Deep Networks? NeurIPS 2016 Multispectral Images Denoising by Intrinsic Tensor Sparsity Regularization CVPR 2016 Fast and Guaranteed Tensor Decomposition via Sketching NeurIPS 2015 Robust PCA with compressed data NeurIPS 2015 Curvilinear Structure Tracking by Low Rank Tensor Approximation with Model Propagation CVPR 2014 BIG & QUIC: Sparse Inverse Covariance Estimation for a Million Variables NeurIPS 2013 On Algorithms for Sparse Multi-factor NMF NeurIPS 2013 Fast Image Deconvolution using Hyper-Laplacian Priors NeurIPS 2009 Differentiable Sparse Coding NeurIPS 2008