Pinned Repositories
ALBFL
awesome-model-quantization
A list of papers, docs, codes about model quantization. This repo is aimed to provide the info for model quantization research, we are continuously improving the project. Welcome to PR the works (papers, repositories) that are missed by the repo.
code2vec
A PyTorch implementation of `code2vec: Learning Distributed Representations of Code` (Alon et al., 2018)
code2vec-1
TensorFlow code for the neural network presented in the paper: "code2vec: Learning Distributed Representations of Code"
CodeBERT
CodeBERT
Cream
This is a collection of our NAS and Vision Transformer work.
DAQ
An official PyTorch implementation of the paper "Distance-aware Quantization", ICCV 2021.
Deep-Learning-Paper-Review-and-Practice
꼼꼼한 딥러닝 논문 리뷰와 코드 실습
deeprl4fl-2021-icse
DSQ
pytorch implementation of "Differentiable Soft Quantization: Bridging Full-Precision and Low-Bit Neural Networks"
DOHA-HWANG's Repositories
DOHA-HWANG/awesome-model-quantization
A list of papers, docs, codes about model quantization. This repo is aimed to provide the info for model quantization research, we are continuously improving the project. Welcome to PR the works (papers, repositories) that are missed by the repo.
DOHA-HWANG/code2vec-1
TensorFlow code for the neural network presented in the paper: "code2vec: Learning Distributed Representations of Code"
DOHA-HWANG/CodeBERT
CodeBERT
DOHA-HWANG/Cream
This is a collection of our NAS and Vision Transformer work.
DOHA-HWANG/DAQ
An official PyTorch implementation of the paper "Distance-aware Quantization", ICCV 2021.
DOHA-HWANG/Deep-Learning-Paper-Review-and-Practice
꼼꼼한 딥러닝 논문 리뷰와 코드 실습
DOHA-HWANG/deeprl4fl-2021-icse
DOHA-HWANG/EWGS
An official implementation of "Network Quantization with Element-wise Gradient Scaling" (CVPR 2021) in PyTorch.
DOHA-HWANG/FQ-ViT
The official implementation for "FQ-ViT: Fully Quantized Vision Transformer without Retraining".
DOHA-HWANG/ghn3
Code for "Can We Scale Transformers to Predict Parameters of Diverse ImageNet Models?"
DOHA-HWANG/HAWQ
Quantization library for PyTorch. Support low-precision and mixed-precision quantization, with hardware implementation through TVM.
DOHA-HWANG/I-BERT
[ICML'21 Oral] I-BERT: Integer-only BERT Quantization
DOHA-HWANG/Intriguing-Properties-of-Vision-Transformers
Official repository for "Intriguing Properties of Vision Transformers" (NeurIPS 2021--Spotlight)
DOHA-HWANG/lightweightViT
VIT is a lightweight, fast, curses-based front end to Taskwarrior
DOHA-HWANG/mini-imagenet-tools
Tools for generating mini-ImageNet dataset and processing batches
DOHA-HWANG/ppuda
Code for Parameter Prediction for Unseen Deep Architectures (NeurIPS 2021)
DOHA-HWANG/pt.darts
PyTorch Implementation of DARTS: Differentiable Architecture Search
DOHA-HWANG/PyChatGPT
⚡️ Python client for the unofficial ChatGPT API with auto token regeneration, conversation tracking, proxy support and more.
DOHA-HWANG/PyHessian
PyHessian is a Pytorch library for second-order based analysis and training of Neural Networks
DOHA-HWANG/pytorch-cifar
95.47% on CIFAR10 with PyTorch
DOHA-HWANG/pytorch-cifar100
Practice on cifar100(ResNet, DenseNet, VGG, GoogleNet, InceptionV3, InceptionV4, Inception-ResNetv2, Xception, Resnet In Resnet, ResNext,ShuffleNet, ShuffleNetv2, MobileNet, MobileNetv2, SqueezeNet, NasNet, Residual Attention Network, SENet, WideResNet)
DOHA-HWANG/pytorch-image-models
PyTorch image models, scripts, pretrained weights -- ResNet, ResNeXT, EfficientNet, EfficientNetV2, NFNet, Vision Transformer, MixNet, MobileNet-V3/V2, RegNet, DPN, CSPNet, and more
DOHA-HWANG/QBERT
DOHA-HWANG/SOFT
[NeurIPS 2021 Spotlight] SOFT: Softmax-free Transformer with Linear Complexity
DOHA-HWANG/subpruning
DOHA-HWANG/TRANSFER
Fault localization and automated program repair techniques
DOHA-HWANG/transformer-quantization
DOHA-HWANG/vision-transformers-cifar10
Let's train vision transformers (ViT) for cifar 10!
DOHA-HWANG/ViT
Implementing Vi(sion)T(transformer)
DOHA-HWANG/ViT-CIFAR
PyTorch implementation for Vision Transformer[Dosovitskiy, A.(ICLR'21)] modified to obtain over 90% accuracy FROM SCRATCH on CIFAR-10 with small number of parameters (= 6.3M, originally ViT-B has 86M).