YoZo-X

YoZo-X's Stars

• CCIIPLab/GCE-GNN

  The source code for "Global Context Enhanced Graph Neural Network for Session-based Recommendation".

  Language:Python12630

• psf/black

  The uncompromising Python code formatter

  Language:Python39.4k2.5k

• xiangwang1223/neural_graph_collaborative_filtering

  Neural Graph Collaborative Filtering, SIGIR2019

  Language:Python812268

• ikostrikov/pytorch-a2c-ppo-acktr-gail

  PyTorch implementation of Advantage Actor Critic (A2C), Proximal Policy Optimization (PPO), Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation (ACKTR) and Generative Adversarial Imitation Learning (GAIL).

  Language:Python3.7k830

• ikostrikov/pytorch-a3c

  PyTorch implementation of Asynchronous Advantage Actor Critic (A3C) from "Asynchronous Methods for Deep Reinforcement Learning".

  Language:Python1.2k279

• hwwang55/KGCN

  A tensorflow implementation of Knowledge Graph Convolutional Networks

  Language:Python496151

• PeiJieSun/diffnet

  Graph Neural Network based Social Recommendation Model. SIGIR2019.

  Language:Python17745

• BUPT-GAMMA/Multi-Component-Graph-Convolutional-Collaborative-Filtering

  Source code for AAAI 2020 paper "Multi-Component Graph Convolutional Collaborative Filtering"

  Language:Python6018

• DataCanvasIO/DeepTables

  DeepTables: Deep-learning Toolkit for Tabular data

  Language:Python676118

• thunlp/GNNPapers

  Must-read papers on graph neural networks (GNN)

  16.2k3k

• LYuhang/GNN_Review

  GNN综述阅读报告

  Language:Jupyter Notebook889214

• zaytoun/scihub.py

  Python API and command-line tool for Sci-Hub

  Language:Python910226

• akaxlh/MB-GMN

  MB-GMN, SIGIR 2021

  Language:Python4214

• cheungdaven/DeepRec

  An Open-source Toolkit for Deep Learning based Recommendation with Tensorflow.

  Language:Python1.1k293

• google-deepmind/reverb

  Reverb is an efficient and easy-to-use data storage and transport system designed for machine learning research

  Language:C++71397

• google-research/seed_rl

  SEED RL: Scalable and Efficient Deep-RL with Accelerated Central Inference. Implements IMPALA and R2D2 algorithms in TF2 with SEED's architecture.

  Language:Python799146

• autoliuweijie/DeepLearning

  Some practices about deep learning

  Language:Jupyter Notebook226222

• chengao982/GP4

  Language:Python1

• CyC2018/Job-Recommend

  🔎 互联网内推信息（社招、校招、实习）

  Language:Shell3k328

• toshikwa/rljax

  A collection of RL algorithms written in JAX.

  Language:Python9519

• BY571/FQF-and-Extensions

  PyTorch implementation of the state-of-the-art distributional reinforcement learning algorithm Fully Parameterized Quantile Function (FQF) and Extensions: N-step Bootstrapping, PER, Noisy Layer, Dueling Networks, and parallelization.

  Language:Jupyter Notebook2910

• rst-tu-dortmund/teb_local_planner

  An optimal trajectory planner considering distinctive topologies for mobile robots based on Timed-Elastic-Bands (ROS Package)

  Language:C++1.1k551

• rst-tu-dortmund/mpc_local_planner

  The mpc_local_planner package implements a plugin to the base_local_planner of the 2D navigation stack. It provides a generic and versatile model predictive control implementation with minimum-time and quadratic-form receding-horizon configurations.

  Language:C++573144

• changgyhub/leetcode_101

  LeetCode 101：力扣刷题指南

  8.8k1.2k

• CyC2018/CS-Notes

  :books: 技术面试必备基础知识、Leetcode、计算机操作系统、计算机网络、系统设计

  178k51.2k

• Show-Me-the-Code/python

  Show Me the Code Python version.

  Language:HTML4k2.7k

• toshikwa/fqf-iqn-qrdqn.pytorch

  PyTorch implementation of FQF, IQN and QR-DQN.

  Language:Python16425

• microsoft/FQF

  FQF(Fully parameterized Quantile Function for distributional reinforcement learning) is a general reinforcement learning framework for Atari games, which can learn to play Atari games automatically by predicting return distribution in the form of a fully parameterized quantile function.

  Language:Jupyter Notebook4110

• palash1992/GEM

  Language:Python1.3k362

• shenweichen/GraphEmbedding

  Implementation and experiments of graph embedding algorithms.

  Language:Python3.8k1k