Pinned Repositories
asch
Asch is an efficient, flexible, safe and decentralized application platform, which was initially designed to lower the barrier to entry for developers.The services provided by the Asch platform include a public chain and a set of application SDKs.
ConvDicLearnTensorFactor
Tensor methods have emerged as a powerful paradigm for consistent learning of many latent variable models such as topic models, independent component analysis and dictionary learning. Model parameters are estimated via CP decomposition of the observed higher order input moments. However, in many domains, additional invariances such as shift invariances exist, enforced via models such as convolutional dictionary learning. In this paper, we develop novel tensor decomposition algorithms for parameter estimation of convolutional models. Our algorithm is based on the popular alternating least squares method, but with efficient projections onto the space of stacked circulant matrices. Our method is embarrassingly parallel and consists of simple operations such as fast Fourier transforms and matrix multiplications. Our algorithm converges to the dictionary much faster and more accurately compared to the alternating minimization over filters and activation maps.
D-AMP_Toolbox
This package contains the code to run Learned D-AMP, D-AMP, D-VAMP, D-prGAMP, and DnCNN algorithms. It also includes code to train Learned D-AMP, DnCNN, and Deep Image Prior U-net using the SURE loss.
deep-EE-opt
Source code for "A Globally Optimal Energy-Efficient Power Control Framework and its Efficient Implementation in Wireless Interference Networks" by Bho Matthiesen, Alessio Zappone, Karl-L. Besser, Eduard A. Jorswieck, and Merouane Debbah, accepted for publication in IEEE Transactions on Signal Processing.
Deep-Reinforcement-Learning-Hands-On-Second-Edition
Deep-Reinforcement-Learning-Hands-On-Second-Edition, published by Packt
deeplearning.ai
deeplearning.ai , By Andrew Ng, All slide and notebook + code and some material.
dlcv_for_beginners
《深度学习与计算机视觉》配套代码
download
🔴蓝灯最新版本下载 https://github.com/getlantern/download 🔴 Lantern Latest Download https://github.com/getlantern/lantern/releases/tag/latest 🔴
dqn
Lua/Torch implementation of DQN (Nature, 2015)
LDAMP_based-Channel-estimation
This code is for the following paper: H. He, C. Wen, S. Jin, and G. Y. Li, “Deep learning-based channel estimation for beamspace mmwave massive MIMO systems,” IEEE Wireless Commun. Lett., vol. 7, no. 5, pp. 852–855, Oct. 2018.
jianghuchuanshuo's Repositories
jianghuchuanshuo/deeplearning.ai
deeplearning.ai , By Andrew Ng, All slide and notebook + code and some material.
jianghuchuanshuo/dlcv_for_beginners
《深度学习与计算机视觉》配套代码
jianghuchuanshuo/download
🔴蓝灯最新版本下载 https://github.com/getlantern/download 🔴 Lantern Latest Download https://github.com/getlantern/lantern/releases/tag/latest 🔴
jianghuchuanshuo/LDAMP_based-Channel-estimation
This code is for the following paper: H. He, C. Wen, S. Jin, and G. Y. Li, “Deep learning-based channel estimation for beamspace mmwave massive MIMO systems,” IEEE Wireless Commun. Lett., vol. 7, no. 5, pp. 852–855, Oct. 2018.
jianghuchuanshuo/asch
Asch is an efficient, flexible, safe and decentralized application platform, which was initially designed to lower the barrier to entry for developers.The services provided by the Asch platform include a public chain and a set of application SDKs.
jianghuchuanshuo/D-AMP_Toolbox
This package contains the code to run Learned D-AMP, D-AMP, D-VAMP, D-prGAMP, and DnCNN algorithms. It also includes code to train Learned D-AMP, DnCNN, and Deep Image Prior U-net using the SURE loss.
jianghuchuanshuo/deep-EE-opt
Source code for "A Globally Optimal Energy-Efficient Power Control Framework and its Efficient Implementation in Wireless Interference Networks" by Bho Matthiesen, Alessio Zappone, Karl-L. Besser, Eduard A. Jorswieck, and Merouane Debbah, accepted for publication in IEEE Transactions on Signal Processing.
jianghuchuanshuo/Deep-Reinforcement-Learning-Hands-On-Second-Edition
Deep-Reinforcement-Learning-Hands-On-Second-Edition, published by Packt
jianghuchuanshuo/dqn
Lua/Torch implementation of DQN (Nature, 2015)
jianghuchuanshuo/DQN-tensorflow
Tensorflow implementation of Human-Level Control through Deep Reinforcement Learning
jianghuchuanshuo/DQN_pytorch
Vanilla DQN, Double DQN, and Dueling DQN implemented in PyTorch
jianghuchuanshuo/dqn_zoo
DQN Zoo is a collection of reference implementations of reinforcement learning agents developed at DeepMind based on the Deep Q-Network (DQN) agent.
jianghuchuanshuo/FLAML
A fast library for AutoML and tuning.
jianghuchuanshuo/ggnn
Gated Graph Sequence Neural Networks
jianghuchuanshuo/ggnn.pytorch
A PyTorch Implementation of Gated Graph Sequence Neural Networks (GGNN) and Residual Gated Graph ConvNets (RGGC) for FYP
jianghuchuanshuo/GitHub-Chinese-Top-Charts
:cn: GitHub中文排行榜，各语言分设「软件 | 资料」榜单，精准定位中文好项目。各取所需，高效学习。
jianghuchuanshuo/GNN-Communication-Networks
This is the repository for the collection of Graph-based Deep Learning for Communication Networks.
jianghuchuanshuo/GNN-Resource-Management
jianghuchuanshuo/GNN4Com
jianghuchuanshuo/IDE2-Net
jianghuchuanshuo/jsonPrase
jianghuchuanshuo/LightGBM
A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.
jianghuchuanshuo/matlab
D2D
jianghuchuanshuo/memcached
memcached development tree
jianghuchuanshuo/Nash-Equillibrium-Tool
Lemke Howson simplex implementation to evaluate Nash Equillibrium
jianghuchuanshuo/Numerical-Computation
This repository contains programs for several approximation algorithms. 1- fixed point iteration. 2- Bisection method. 3- False position method. 4- Newton raphson method. 5- The secant method. Interpolation algorithms like 1- Langrange Interpolation. 2- Newton Dividend Difference. 3- Newton Forward Difference. 4- Newton Backward Difference 5- Spline Interpolation. are also available
jianghuchuanshuo/OAMP-Net
jianghuchuanshuo/radioconda
Software radio distribution and installer for conda
jianghuchuanshuo/Unrolled-WMMSE
Tensorflow implementation of Unfolding WMMSE using Graph Neural Networks for Efficient Power Allocation
jianghuchuanshuo/usca_power_control
Deep unfolded SCA for power allocation in wireless system