neural network pruning

What Makes a Good Prune? Maximal Unstructured Pruning for Maximal Cosine Similarity ICLR 2024 Your Transformer is Secretly Linear ACL 2024 A Comparative Study on the Impact of Model Compression Techniques on Fairness in Language Models ACL 2023 A General Framework For Proving The Equivariant Strong Lottery Ticket Hypothesis ICLR 2023 A Multiple-Teacher Pruning Based Self-Distillation (MT-PSD) Approach to Model Compression for Audio-Visual Wake Word Spotting Interspeech 2023 Are Straight-Through Gradients and Soft-Thresholding All You Need for Sparse Training? WACV 2023 Balanced Training for Sparse GANs NeurIPS 2023 Bias in Pruned Vision Models: In-Depth Analysis and Countermeasures CVPR 2023 Bi-directional Masks for Efficient N:M Sparse Training ICML 2023 Breaking through Deterministic Barriers: Randomized Pruning Mask Generation and Selection EMNLP 2023 Calibrating the Rigged Lottery: Making All Tickets Reliable ICLR 2023 Can We Find Strong Lottery Tickets in Generative Models? AAAI 2023 Connectivity Matters: Neural Network Pruning Through the Lens of Effective Sparsity JMLR 2023 Differentiable Transportation Pruning ICCV 2023 Don’t just prune by magnitude! Your mask topology is a secret weapon NeurIPS 2023 Dynamic Sparsity Is Channel-Level Sparsity Learner NeurIPS 2023 Efficient Distributed Inference of Deep Neural Networks via Restructuring and Pruning AAAI 2023 Efficient Latency-Aware CNN Depth Compression via Two-Stage Dynamic Programming ICML 2023 EfficientVLM: Fast and Accurate Vision-Language Models via Knowledge Distillation and Modal-adaptive Pruning ACL 2023 Enhancing Efficient Continual Learning with Dynamic Structure Development of Spiking Neural Networks IJCAI 2023 ESL-SNNs: An Evolutionary Structure Learning Strategy for Spiking Neural Networks AAAI 2023 Fantastic Weights and How to Find Them: Where to Prune in Dynamic Sparse Training NeurIPS 2023 Fast as CHITA: Neural Network Pruning with Combinatorial Optimization ICML 2023 Fast, Differentiable and Sparse Top-k: a Convex Analysis Perspective ICML 2023 FLOAT: Fast Learnable Once-for-All Adversarial Training for Tunable Trade-Off Between Accuracy and Robustness WACV 2023