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grocery

models for grocery shopping behavior (Wan et al, CIKM'18, Wan et al, WWW'17)

groundhog

Library for implementing RNNs with Theano

group-method-of-data-handling

My realization of Group method of data handling (GMDH)

group-sparsity-sbp

Structured Bayesian Pruning, NIPS 2017

group_31_irdm-2017

This is the repository for Group 31's IRDM assignment submission for UCL 2017.

growing-instability

growing_instability

grp-assign-2

Twitter sentiments analysis assignment

gru4rec

GRU4Rec is the cleaned & simplified implementation of the algorithm of the "Session-based Recommendations with Recurrent Neural Networks" paper, published at ICLR 2016. The code is stripped of features that we had found to be unhelpful in increasing accuracy.

gs-kernel

The generic string kernel for amino acid sequence comparison

gsn

Efficient implementation of Generative Stochastic Networks

gsp-implementation-python

Sequence pattern discovery using Generalized Sequential Pattern Mining Algorithm

gsp_implementation

Generalised Sequential Pattern Mining is implemented on Online Retail Data as a part of a course project

gsp_implementation_in_python

Implement of generalized sequential pattern mining algorithm in Python from scratch

gspan

Python implementation of frequent subgraph mining algorithm gSpan. Directed graphs are supported.

gsqr

This package implements a blockwise-majorization-decent algorithm for group quantile regression (GSQR). It computes efficiently the solution paths of the group-Lasso penalized quantile regression, the group-MCP penalized quantile regression, the group-SCAD penalized quantile regression and theirs approximations.

gstar

Generalized Space Time Auto Regressive

gtc2015

gtg

Source code of "Grid-to-Graph: Flexible Spatial Relational Inductive Biases for Reinforcement Learning" (AAMAS 2021).

guanrank_all

Application examples of GuanRank in deep learning (images), lightGBM and recurrent neural networks (time-series)

gui4svm

GUI program for SVM/One-Class SVM

guided-gan

A Guided Learning Approach for Generative Adversarial Networks

guided-learning

We present the the concept of Guided Learning, which out-lines a framework in which a reinforcement learning agent can effectively’ask for help’ as it encounters stagnation. Either a human or expert agentsupervisor can then effectively ’guide’ the agent as to how to progressbeyond the point of stagnation. This guidance is then encoded in a novelway using a separately trained neural network referred to as a ’TaughtResponse Memory’ that can be recalled when another ’similar’ situa-tion arises in the future. This paper applies Guided Learning on topof an evolutionary algorithm but also shows how Guided Learning isalgorithm independent and can be applied in any reinforcement learn-ing context. The results show that our initial implementation of GuidedLearning provided in this paper gives superior performance and yields,on average, an increase of 136% in the rate of progression of the mostfit genome with best and worst case results yielding 137% and 110%respectively and an average increase of 112% in rate of progression forthe average genome with best and worst case results of 558% and 47%respectively. All results were achieved with minimal guidance. Such re-sults occur because the agent can exploit more information and thus,the need for exploration of the solution space is reduced. The results ob-tained show good promise for Guided Learnings potential as such resultswere obtained with only a partial implementation and much future workstill remains.

guineapig

Pure python PIG-like language

gurobimh

Drop-in replacement for Gurobi's gurobipy python interface that supports Python3.4 and is much more efficient

gwaskb

Machine-curated database of genetic disease and genome-wide association studies

gym

A toolkit for developing and comparing reinforcement learning algorithms.

h2o-matrix

Demonstration of Mahout compatible matrix and vector types based on h2o

h2o-usage-examples

Examples and test with h2o

h2o-world-2014-training

training material