huawei-noah-admin

huawei-noah-admin's Stars

• open-mmlab/mmcv

  OpenMMLab Computer Vision Foundation

  Language:Python6k851.2k1.7k

• huawei-noah/Efficient-AI-Backbones

  Efficient AI Backbones including GhostNet, TNT and MLP, developed by Huawei Noah's Ark Lab.

  Language:Python4.1k54273711

• huawei-noah/Pretrained-Language-Model

  Pretrained language model and its related optimization techniques developed by Huawei Noah's Ark Lab.

  Language:Python3k57201628

• huawei-noah/trustworthyAI

  Trustworthy AI related projects

  Language:Python98922108219

• huawei-noah/SMARTS

  Scalable Multi-Agent RL Training School for Autonomous Driving

  Language:Python977141k192

• huawei-noah/AdderNet

  Code for paper " AdderNet: Do We Really Need Multiplications in Deep Learning?"

  Language:Python9552573186

• huawei-noah/bolt

  Bolt is a deep learning library with high performance and heterogeneous flexibility.

  Language:C++9355074160

• huawei-noah/noah-research

  Noah Research

  Language:Python88030126162

• huawei-noah/vega

  AutoML tools chain

  Language:Python84828172177

• huawei-noah/streamDM

  Stream Data Mining Library for Spark Streaming

  Language:Scala4916756141

• huawei-noah/xingtian

  xingtian is a componentized library for the development and verification of reinforcement learning algorithms

  Language:Python309141289

• huawei-noah/benchmark

  Language:HTML2767569

• yehuitang/Pruning

  Code for "Co-Evolutionary Compression for Unpaired Image Translation" (ICCV 2019), "SCOP: Scientific Control for Reliable Neural Network Pruning" (NeurIPS 2020) and “Manifold Regularized Dynamic Network Pruning” (CVPR 2021).

  Language:Python241102447

• huawei-noah/Disout

  Code for AAAI 2020 paper, Beyond Dropout: Feature Map Distortion to Regularize Deep Neural Networks (Disout).

  Language:Python218141439

• huawei-noah/BGCN

  A Tensorflow implementation of "Bayesian Graph Convolutional Neural Networks" (AAAI 2019).

  Language:Python1588245

• zhaohui-yang/LegoNet

  A Pytorch implementation of "LegoNet: Efficient Convolutional Neural Networks with Lego Filters" (ICML 2019).

  Language:Python1407215

• noah-test/CARS

  Pytorch code for paper: CARS: Continuous Evolution for Efficient Neural Architecture Search

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• noah-test/test

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