MingSun-Tse / Why-the-State-of-Pruning-so-ConfusingLinks
[Preprint] Why is the State of Neural Network Pruning so Confusing? On the Fairness, Comparison Setup, and Trainability in Network Pruning
☆41Updated 3 months ago
Alternatives and similar repositories for Why-the-State-of-Pruning-so-Confusing
Users that are interested in Why-the-State-of-Pruning-so-Confusing are comparing it to the libraries listed below
Sorting:
- [ICLR'23] Trainability Preserving Neural Pruning (PyTorch)☆34Updated 2 years ago
- [NeurIPS 2024] Search for Efficient LLMs☆15Updated 11 months ago
- A generic code base for neural network pruning, especially for pruning at initialization.☆31Updated 3 years ago
- BESA is a differentiable weight pruning technique for large language models.☆17Updated last year
- [ICLR 2022] "Unified Vision Transformer Compression" by Shixing Yu*, Tianlong Chen*, Jiayi Shen, Huan Yuan, Jianchao Tan, Sen Yang, Ji Li…☆55Updated 2 years ago
- [TMLR] Official PyTorch implementation of paper "Efficient Quantization-aware Training with Adaptive Coreset Selection"☆36Updated last year
- [NeurIPS'22] What Makes a "Good" Data Augmentation in Knowledge Distillation -- A Statistical Perspective☆37Updated 3 years ago
- Implementation of PGONAS for CVPR22W and RD-NAS for ICASSP23☆23Updated 2 years ago
- [NeurIPS 2022 Spotlight] This is the official PyTorch implementation of "EcoFormer: Energy-Saving Attention with Linear Complexity"☆74Updated 3 years ago
- (CVPR 2022) Automated Progressive Learning for Efficient Training of Vision Transformers☆25Updated 10 months ago
- [NeurIPS'21] "Chasing Sparsity in Vision Transformers: An End-to-End Exploration" by Tianlong Chen, Yu Cheng, Zhe Gan, Lu Yuan, Lei Zhang…☆89Updated 2 years ago
- Official PyTorch implementation of "Meta-prediction Model for Distillation-Aware NAS on Unseen Datasets" (ICLR 2023 notable top 25%)☆26Updated last year
- ☆17Updated 3 years ago
- [ICCV-2023] EMQ: Evolving Training-free Proxies for Automated Mixed Precision Quantization☆28Updated 2 years ago
- ☆23Updated 3 years ago
- [Neurips 2022] “ Back Razor: Memory-Efficient Transfer Learning by Self-Sparsified Backpropogation”, Ziyu Jiang*, Xuxi Chen*, Xueqin Huan…☆20Updated 2 years ago
- [ICLR 2021] CompOFA: Compound Once-For-All Networks For Faster Multi-Platform Deployment☆25Updated 2 years ago
- ☆48Updated 2 years ago
- [NeurIPS 2023] ShiftAddViT: Mixture of Multiplication Primitives Towards Efficient Vision Transformer☆30Updated 2 years ago
- Pytorch implementation of our paper accepted by TPAMI 2023 — Lottery Jackpots Exist in Pre-trained Models☆34Updated 2 years ago
- [ICLR 2022] "Learning Pruning-Friendly Networks via Frank-Wolfe: One-Shot, Any-Sparsity, and No Retraining" by Lu Miao*, Xiaolong Luo*, T…☆33Updated 3 years ago
- Code for ViTAS_Vision Transformer Architecture Search☆51Updated 4 years ago
- [ICLR 2024] This is the official PyTorch implementation of "QLLM: Accurate and Efficient Low-Bitwidth Quantization for Large Language Mod…☆30Updated last year
- code for NASViT☆67Updated 3 years ago
- ☆13Updated last year
- ☆26Updated 4 years ago
- DropIT: Dropping Intermediate Tensors for Memory-Efficient DNN Training (ICLR 2023)☆32Updated 2 years ago
- [ICLR 2024] This is the official PyTorch implementation of "QLLM: Accurate and Efficient Low-Bitwidth Quantization for Large Language Mod…☆39Updated last year
- ☆28Updated 3 years ago
- Pytorch implementation of our paper accepted by ECCV 2022-- Fine-grained Data Distribution Alignment for Post-Training Quantization☆15Updated 3 years ago