Pinned Repositories
AAAI18-code
The code of AAAI18 paper "Learning Structured Representation for Text Classification via Reinforcement Learning".
AAAI2020-RFMetaFAS
Pytorch codes for Regularized Fine-grained Meta Face Anti-spoofing in AAAI 2020
academicpages.github.io
Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes
anomaly-detection-resources
Anomaly detection related books, papers, videos, and toolboxes
better-exceptions
Pretty and useful exceptions in Python, automatically.
conference_call_for_paper
2019-2020 International Conferences in Artificial Intelligence, Machine Learning, Computer Vision, Data Mining, Natural Language Processing and Robotics
CRSLab
CRSLab is an open-source toolkit for building Conversational Recommender System (CRS).
daisyRec
A developing recommender system in pytorch. Algorithm: KNN, LFM, SLIM, NeuMF, FM, DeepFM, VAE and so on, which aims to fair comparison for recommender system benchmarks
DeepFuzz
Graph-WaveNet
graph wavenet
GrameRules's Repositories
GrameRules/Graph-WaveNet
graph wavenet
GrameRules/academicpages.github.io
Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes
GrameRules/anomaly-detection-resources
Anomaly detection related books, papers, videos, and toolboxes
GrameRules/better-exceptions
Pretty and useful exceptions in Python, automatically.
GrameRules/conference_call_for_paper
2019-2020 International Conferences in Artificial Intelligence, Machine Learning, Computer Vision, Data Mining, Natural Language Processing and Robotics
GrameRules/CRSLab
CRSLab is an open-source toolkit for building Conversational Recommender System (CRS).
GrameRules/daisyRec
A developing recommender system in pytorch. Algorithm: KNN, LFM, SLIM, NeuMF, FM, DeepFM, VAE and so on, which aims to fair comparison for recommender system benchmarks
GrameRules/DeepFuzz
GrameRules/detectron2
Detectron2 is FAIR's next-generation platform for object detection and segmentation.
GrameRules/distribution-is-all-you-need
The basic distribution probability Tutorial for Deep Learning Researchers
GrameRules/ESAM
Code for the paper "ESAM: Discriminative Domain Adaptation with Non-Displayed Items to Improve Long-Tail Performance" (SIGIR2020)
GrameRules/introRL
Intro to Reinforcement Learning (强化学习纲要）
GrameRules/Learn-Statistical-Learning-Method
Implementation of Statistical Learning Method, Second Edition.《统计学习方法》第二版，算法实现。
GrameRules/Lihang
Statistical learning methods, 统计学习方法(第2版)[李航] [笔记, 代码, notebook, 参考文献, Errata, lihang]
GrameRules/lihang-code
《统计学习方法》的代码实现
GrameRules/microservices-demo
Deployment scripts & config for Sock Shop
GrameRules/mmdetection
OpenMMLab Detection Toolbox and Benchmark
GrameRules/modelszoo
GrameRules/NLP-with-Python
Scikit-Learn, NLTK, Spacy, Gensim, Textblob and more
GrameRules/pgmpy
Python Library for learning (Structure and Parameter) and inference (Statistical and Causal) in Bayesian Networks.
GrameRules/pretrained-models.pytorch
Pretrained ConvNets for pytorch: NASNet, ResNeXt, ResNet, InceptionV4, InceptionResnetV2, Xception, DPN, etc.
GrameRules/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers
aka "Bayesian Methods for Hackers": An introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view. All in pure Python ;)
GrameRules/pymc3
Probabilistic Programming in Python: Bayesian Modeling and Probabilistic Machine Learning with Theano
GrameRules/pytorch-tutorial
PyTorch Tutorial for Deep Learning Researchers
GrameRules/PyTorchDocs
PyTorch 官方中文教程包含 60 分钟快速入门教程，强化教程，计算机视觉，自然语言处理，生成对抗网络，强化学习。欢迎 Star，Fork！
GrameRules/RecBole
A unified, comprehensive and efficient recommendation library
GrameRules/reinforcement-learning-an-introduction
Python Implementation of Reinforcement Learning: An Introduction
GrameRules/Surprise
A Python scikit for building and analyzing recommender systems
GrameRules/sympy
A computer algebra system written in pure Python
GrameRules/TextBox
TextBox is an open-source library for building text generation system.