magnitude pruning

In value-based deep reinforcement learning, a pruned network is a good network ICML 2024 What Makes a Good Prune? Maximal Unstructured Pruning for Maximal Cosine Similarity ICLR 2024 Parameter-Efficient Fine-Tuning without Introducing New Latency ACL 2023 Sparsity May Cry: Let Us Fail (Current) Sparse Neural Networks Together! ICLR 2023 The Emergence of Essential Sparsity in Large Pre-trained Models: The Weights that Matter NeurIPS 2023 An Operator Theoretic View On Pruning Deep Neural Networks ICLR 2022 Combining Compressions for Multiplicative Size Scaling on Natural Language Tasks COLING 2022 Gradient and Mangitude Based Pruning for Sparse Deep Neural Networks AAAI 2022 Sparse Winning Tickets are Data-Efficient Image Recognizers NeurIPS 2022 Disentangling Representations of Text by Masking Transformers EMNLP 2021 Less Is More: Domain Adaptation with Lottery Ticket for Reading Comprehension EMNLP 2021 On the Predictability of Pruning Across Scales ICML 2021 Pruning Neural Networks at Initialization: Why Are We Missing the Mark? ICLR 2021 Lookahead: A Far-sighted Alternative of Magnitude-based Pruning ICLR 2020 Successfully Applying the Stabilized Lottery Ticket Hypothesis to the Transformer Architecture ACL 2020