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association-rule-mining-on-distributed-datasets

attention_is_all_you_need

Transformer of "Attention Is All You Need" (Vaswani et al. 2017) by Chainer.

attentive-group-recommendation

Attentive Group Recommendation

attrvn

Vertex nomination on text-attributed graphs.

autoencoderviz

An autoencoder built specifically to work with and understand how networks learn on natual language data

awesome-2vec

Curated list of 2vec-type embedding models

awesome-community-detection

A curated list of community detection research papers with implementations.

awesome-crowdsourcing

A curated list of awesome resources on crowdsourcing and online behavioral experiments

awesome-decision-tree-papers

A collection of research papers on decision, classification and regression trees with implementations.

awesome-deep-learning-papers

The most cited deep learning papers

awesome-gradient-boosting-papers

A curated list of gradient boosting research papers with implementations.

awesome-machine-learning

A curated list of awesome Machine Learning frameworks, libraries and software.

awesome-network-embedding

A curated list of network embedding techniques.

awesome-nonconvex-optimization

A collection of papers and readings for non-convex optimization

awesome-public-datasets

An awesome list of high-quality open datasets in public domains (on-going). By everyone, for everyone.

awesome-system-for-machine-learning

A curated list of research in machine learning system. I also summarize some papers if I think they are really interesting.

bach-uai13-code

Code for "Hinge-loss Markov Random Fields: Convex Inference for Structured Prediction." Stephen H. Bach, Bert Huang, Ben London, and Lise Getoor. Uncertainty in Artificial Intelligence (UAI) 2013

basic_mondrian

The raw mondrian is designed for numerical attributes. When comes to categorical attributes, Mondrian needs to transform categorical attributes to numerical ones. This transformations is not good for some applications. In 2006, LeFevre proposed basic Mondrian, which support both categorical and numerical attributes. This repository is an implementation for basic Mondrian.

basic_reinforcement_learning

An introduction series to Reinforcement Learning (RL) with comprehensive step-by-step tutorials.

bayes_gmm

Bayesian Gaussian mixture models in Python.

bayesgan

Tensorflow code for the Bayesian GAN (https://arxiv.org/abs/1705.09558) (NIPS 2017)

bayesian-machine-learning

Notebooks about Bayesian methods for machine learning

bayesian_hyperparameters_matching

Learning hyperparameters in Bayesian models by matching moments of prior predictive distributions.

bayesian_neural_network_papers

Papers for Bayesian-NN

bayeslogisticregression

Sentiment analysis over IMDB-movie reviews and Twitter data using Logistic regression and Naive Bayes' classifiers from Python’s machine learning package: scikit-learn.

baylorml

Machine learning library currently supporting convolutional neural networks and fast k-means clustering.

bcfwstruct

Block-Coordinate Frank-Wolfe Optimization for Structural SVMs

began-pytorch

in progress

behaviac

behaviac is a framework of the game AI development, and it also can be used as a rapid game prototype design tool. behaviac supports the behavior tree(BT), finite state machine(FSM) and hierarchical task network(HTN). Behaviors can be designed and debugged in the designer, exported and executed by the game. The designer can only run on the Windows platforms. The run time library is implemented with C++ and C#, and it supports all major platforms (Windows, Linux, Android, iOS, Unity etc.) and Unity. The C++ version is suitable for the client and server side.

behaving

Behavior-Driver-Development for multi-user web/email/sms applications