DUCKQUANT / THE QUANT & AI DIRECTORY
14,444 resources, ranked by in-directory PageRank
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Publication: Mixed-Precision Neural Network Quantization via Learned Layer-wise Importance. Published Mar 16, 2022.
ActNN: Reducing Training Memory Footprint via 2-Bit Activation Compressed Training
How Do Adam and Training Strategies Help BNNs Optimization? In ICML 2021.
Improving Post Training Neural Quantization: Layer-wise Calibration and Integer Programming
The official PyTorch implementation of the ICLR2022 paper, QDrop: Randomly Dropping Quantization for Extremely Low-bit Post-Training Quantization
Information Bottleneck in Neural Networks
Pytorch implementation of RAPQ, IJCAI 2022
Official implementation for ECCV 2022 paper LIMPQ - "Mixed-Precision Neural Network Quantization via Learned Layer-wise Importance"
BASQ: Branch-wise Activation-clipping Search Quantization for Sub-4-bit Neural Networks, ECCV 2022
Code for the NeurIPS 2022 paper "Optimal Brain Compression: A Framework for Accurate Post-Training Quantization and Pruning".
Pytorch implementation of our paper accepted by CVPR 2022 -- IntraQ: Learning Synthetic Images with Intra-Class Heterogeneity for Zero-Shot Network Quantization
Library for 8-bit optimizers and quantization routines.
Publication: IntraQ: Learning Synthetic Images With Intra-Class Heterogeneity for Zero-Shot Network Quantization. Published 2022.
Public repository by Qualcomm-AI-research. Primary language: Python.
Publication: Stochastic Precision Ensemble: Self-Knowledge Distillation for Quantized Deep Neural Networks. Published Sep 30, 2020.
Publication: Network Quantization with Element-wise Gradient Scaling. Published Apr 2, 2021.
Publication: Zero-shot Adversarial Quantization. Published Mar 29, 2021.
Publication: Learnable Companding Quantization for Accurate Low-bit Neural Networks. Published Mar 12, 2021.
Publication: Permute, Quantize, and Fine-tune: Efficient Compression of Neural Networks. Published Oct 29, 2020.
Publication: Diversifying Sample Generation for Accurate Data-Free Quantization. Published Mar 1, 2021.
Auto-NBA: Efficient and Effective Search Over the Joint Space of Networks, Bitwidths, and AcceleratorsYonggan Fu, Yongan Zhang, Yang Zhang, Da...
Differentiable Dynamic Quantization with Mixed Precision and Adaptive ResolutionZhaoyang Zhang, Wenqi Shao, Jinwei Gu, Xiaogang Wang, Pin...
BSQ: Exploring Bit-Level Sparsity for Mixed-Precision Neural Network Quantization (ICLR 2021)
Pytorch implementation of BRECQ, ICLR 2021
Identify a binary weight or binary weight and activation subnetwork within a randomly initialized network by only pruning and binarizing the network.
Code for High-Capacity Expert Binary Networks (ICLR 2021).
An official implementation of "Network Quantization with Element-wise Gradient Scaling" (CVPR 2021) in PyTorch.
Using ideas from product quantization for state-of-the-art neural network compression.
Qimera: Data-free Quantization with Synthetic Boundary Supporting Samples [NeurIPS 2021]
[ICML 2021] "Auto-NBA: Efficient and Effective Search Over the Joint Space of Networks, Bitwidths, and Accelerators" by Yonggan Fu, Yongan Zhang, Yang Zhang, David…
Quantization library for PyTorch. Support low-precision and mixed-precision quantization, with hardware implementation through TVM.
Explore this resource at its source website for documentation and current information.
Gorgonia is a library that helps facilitate machine learning in Go. Go · 5,919
Explore this resource at its source website for documentation and current information.
