xhchrn

Provable_Plug_and_Play

[ICML 2019] Plug-and-Play Methods Provably Converge with Properly Trained Denoisers

ALISTA

[ICLR 2019] "ALISTA: Analytic Weights Are As Good As Learned Weights in LISTA", by Jialin Liu*, Xiaohan Chen*, Zhangyang Wang and Wotao Yin.

LISTA-CPSS

[NeurIPS'18, Spotlight oral] "Theoretical Linear Convergence of Unfolded ISTA and its Practical Weights and Thresholds", by Xiaohan Chen*, Jialin Liu*, Zhangyang Wang and Wotao Yin.

Orthogonality-in-CNNs

[NeurIPS '18] "Can We Gain More from Orthogonality Regularizations in Training Deep CNNs?" Official Implementation.

ACBEGAN

deit

Official DeiT repository

gegan

lottery-ticket-reading-list

MS4L2O

xhchrn/MS4L2O

xhchrn/lottery-ticket-reading-list

xhchrn/deit

Official DeiT repository

xhchrn/AdaptiveOptim

Adaptive optimization procedure

xhchrn/ALISTA

[ICLR 2019] "ALISTA: Analytic Weights Are As Good As Learned Weights in LISTA", by Jialin Liu*, Xiaohan Chen*, Zhangyang Wang and Wotao Yin.

xhchrn/ConfigArgParse

A drop-in replacement for argparse that allows options to also be set via config files and/or environment variables.

xhchrn/D-LADMM

Differentiable Linearized ADMM (ICML 2019)

xhchrn/DPGN

This repository is the official implementation of DPGN: Distribution Propagation Graph Network for Few-shot Learning.

xhchrn/E2Train

[NeurIPS 2019] E2-Train: Training State-of-the-art CNNs with Over 80% Less Energy

xhchrn/Early-Bird-Tickets

[ICLR 2020] Drawing Early-Bird Tickets: Toward More Efficient Training of Deep Networks

xhchrn/Federated-Learning-PyTorch

Implementation of Communication-Efficient Learning of Deep Networks from Decentralized Data

xhchrn/few-shot-ctm

Few shot learning

xhchrn/hidden-networks

xhchrn/learn2branch

Exact Combinatorial Optimization with Graph Convolutional Neural Networks (NeurIPS 2019)

xhchrn/LG-FedAvg

Federated Learning with Local and Global Representations, NeurIPS 2019 FL workshop

xhchrn/lottery-ticket-hypothesis

A reimplementation of "The Lottery Ticket Hypothesis" (Frankle and Carbin) on MNIST.

xhchrn/LTH-Pytorch

This repository contains a Pytorch implementation of the paper "The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks" by Jonathan Frankle and Michael Carbin that can be easily adapted to any model/dataset.

xhchrn/mamba

xhchrn/na-alista

xhchrn/nn_modularity

xhchrn/open_lth

A repository in preparation for open-sourcing lottery ticket hypothesis code.

xhchrn/pytorch-cifar

95.16% on CIFAR10 with PyTorch

xhchrn/rfs

xhchrn/rigl

End-to-end training of sparse deep neural networks with little-to-no performance loss.

xhchrn/rigl-torch

Extremely lightweight & easy to use PyTorch implementation of the sparse model training method "RigL" by Google Research.

xhchrn/st

My customization of suckless terminal (st)

xhchrn/Synaptic-Flow

xhchrn/vimrc

The ultimate Vim configuration (vimrc)

xhchrn/volo

VOLO: Vision Outlooker for Visual Recognition

xhchrn/website