DUCKQUANT / THE QUANT & AI DIRECTORY
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Explore this resource at its source website for documentation and current information.
Explore this resource at its source website for documentation and current information.
Publication: High-Capacity Expert Binary Networks. Published Oct 7, 2020.
Publication: Multi-Prize Lottery Ticket Hypothesis: Finding Accurate Binary Neural Networks by Pruning A Randomly Weighted Network. Published Mar 17, 2021.
Publication: Neural gradients are near-lognormal: improved quantized and sparse training. Published Jun 15, 2020.
Publication: Sparse Quantized Spectral Clustering. Published Oct 3, 2020.
Publication: HAO: Hardware-aware neural Architecture Optimization for Efficient Inference. Published Apr 26, 2021.
Publication: Is In-Domain Data Really Needed? A Pilot Study on Cross-Domain Calibration for Network Quantization. Published May 16, 2021.
Publication: Generative Zero-shot Network Quantization. Published Jan 21, 2021.
Publication: HAWQV3: Dyadic Neural Network Quantization. Published Nov 20, 2020.
Collections of model quantization algorithms. Any issues, please contact Peng Chen (blueardour@gmail.com)
[ICML 2021] "Double-Win Quant: Aggressively Winning Robustness of Quantized DeepNeural Networks via Random Precision Training and Inference" by Yonggan Fu, Qixuan Yu, Meng Li,…
[ICCV 2021] Code release for "Sub-bit Neural Networks: Learning to Compress and Accelerate Binary Neural Networks"
Self-supervised Product Quantization for Deep Unsupervised Image Retrieval - ICCV2021
Public repository by LaVieEnRoseSMZ. Primary language: Python.
AFP is a hardware-friendly quantization framework for DNNs, which is contributed by Fangxin Liu and Wenbo Zhao.
This is the pytorch implementation for the paper: Generalizable Mixed-Precision Quantization via Attribution Rank Preservation, which is accepted to ICCV2021.
Publication: OPQ: Compressing Deep Neural Networks with One-shot Pruning-Quantization. Published May 18, 2021.
Publication: QPP: Real-Time Quantization Parameter Prediction for Deep Neural Networks. Published 2021.
Publication: Automated Log-Scale Quantization for Low-Cost Deep Neural Networks. Published 2021.
The Analytic Companion for Portfolio Performance Python · 21
Publication: FleXOR: Trainable Fractional Quantization. Published Sep 9, 2020.
Publication: Position-based Scaled Gradient for Model Quantization and Pruning. Published May 22, 2020.
Publication: Efficient Exact Verification of Binarized Neural Networks. Published May 7, 2020.
Publication: Path Sample-Analytic Gradient Estimators for Stochastic Binary Networks. Published Jun 4, 2020.
Publication: SIMBA: A Skyrmionic In-Memory Binary Neural Network Accelerator. Published Mar 11, 2020.
Publication: Hierarchical binary CNNs for landmark localization with limited resources. Published Aug 14, 2018.
Publication: Any-Precision Deep Neural Networks. Published Nov 17, 2019.
Publication: Ps and Qs: Quantization-aware pruning for efficient low latency neural network inference. Published Feb 22, 2021.
Publication: BoolNet: Minimizing The Energy Consumption of Binary Neural Networks. Published Jun 13, 2021.
Publication: Dynamic Precision Analog Computing for Neural Networks. Published Feb 12, 2021.
Publication: Confounding Tradeoffs for Neural Network Quantization. Published Feb 12, 2021.
Publication: Pruning and Quantization for Deep Neural Network Acceleration: A Survey. Published Jan 24, 2021.
Position-based Scaled Gradient for Model Quantization and Pruning Code (NeurIPS 2020)
Code to accompany the paper "Hierarchical Quantized Autoencoders"
