[CVPR 2023] Towards Any Structural Pruning; LLMs / SAM / Diffusion / Transformers / YOLOv8 / CNNs
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Updated
Jul 8, 2024 - Python
[CVPR 2023] Towards Any Structural Pruning; LLMs / SAM / Diffusion / Transformers / YOLOv8 / CNNs
Code and resources on scalable and efficient Graph Neural Networks
[NeurIPS2022] Official implementation of the paper 'Green Hierarchical Vision Transformer for Masked Image Modeling'.
[CVPR 2024] Dynamic Adapter Meets Prompt Tuning: Parameter-Efficient Transfer Learning for Point Cloud Analysis
[NeurIPS 2023] Structural Pruning for Diffusion Models
NeurIPS 2021, Official codes for "Efficient Training of Visual Transformers with Small Datasets".
[ICML 2023] UPop: Unified and Progressive Pruning for Compressing Vision-Language Transformers.
Official PyTorch implementation of "Rapid Neural Architecture Search by Learning to Generate Graphs from Datasets" (ICLR 2021)
Official PyTorch Implementation of HELP: Hardware-adaptive Efficient Latency Prediction for NAS via Meta-Learning (NeurIPS 2021 Spotlight)
Frame Flexible Network (CVPR2023)
Denoising Diffusion Step-aware Models (ICLR2024)
Efficient Deep Learning for Stereo Matching Tensorflow 2.x
A generic code base for neural network pruning, especially for pruning at initialization.
[ICLR'24] "DeepZero: Scaling up Zeroth-Order Optimization for Deep Model Training" by Aochuan Chen*, Yimeng Zhang*, Jinghan Jia, James Diffenderfer, Jiancheng Liu, Konstantinos Parasyris, Yihua Zhang, Zheng Zhang, Bhavya Kailkhura, Sijia Liu
[ICLR'23] Trainability Preserving Neural Pruning (PyTorch)
This repository provides implementation of a baseline method and our proposed methods for efficient Skeleton-based Human Action Recognition.
[ICML 2023] Hierarchical Grammar-Induced Geometry for Data-Efficient Molecular Property Prediction
Official PyTorch implementation of "Meta-prediction Model for Distillation-Aware NAS on Unseen Datasets" (ICLR 2023 notable top 25%)
Pytorch implementation for stereo matching described in the paper: Efficient Deep learning for stereo matching
[ICML'24 Oral] APT: Adaptive Pruning and Tuning Pretrained Language Models for Efficient Training and Inference
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