neural network pruning

ChipNet: Budget-Aware Pruning with Heaviside Continuous Approximations ICLR 2021 Convolutional Neural Network Pruning With Structural Redundancy Reduction CVPR 2021 Dense for the Price of Sparse: Improved Performance of Sparsely Initialized Networks via a Subspace Offset ICML 2021 Do We Actually Need Dense Over-Parameterization? In-Time Over-Parameterization in Sparse Training ICML 2021 Effective Sparsification of Neural Networks With Global Sparsity Constraint CVPR 2021 Efficient Lottery Ticket Finding: Less Data is More ICML 2021 Exploring Sparsity in Image Super-Resolution for Efficient Inference CVPR 2021 Growing Efficient Deep Networks by Structured Continuous Sparsification ICLR 2021 Holistic Filter Pruning for Efficient Deep Neural Networks WACV 2021 Is Each Layer Non-trivial in CNN? (Student Abstract) AAAI 2021 Iterative Deep Model Compression and Acceleration in the Frequency Domain ACML 2021 Joint-DetNAS: Upgrade Your Detector With NAS, Pruning and Dynamic Distillation CVPR 2021 Layer-adaptive Sparsity for the Magnitude-based Pruning ICLR 2021 Layer-wise Model Pruning based on Mutual Information EMNLP 2021 Lottery Ticket Preserves Weight Correlation: Is It Desirable or Not? ICML 2021 MixMix: All You Need for Data-Free Compression Are Feature and Data Mixing ICCV 2021 Network Pruning via Performance Maximization CVPR 2021 Neural Pruning via Growing Regularization ICLR 2021 Neural Response Interpretation Through the Lens of Critical Pathways CVPR 2021 Only Train Once: A One-Shot Neural Network Training And Pruning Framework NeurIPS 2021 OPQ: Compressing Deep Neural Networks with One-shot Pruning-Quantization AAAI 2021 Parameter-Efficient Transfer Learning with Diff Pruning ACL 2021 Powerpropagation: A sparsity inducing weight reparameterisation NeurIPS 2021 Pruning Neural Machine Translation for Speed Using Group Lasso EMNLP 2021 Pruning Neural Networks at Initialization: Why Are We Missing the Mark? ICLR 2021