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neural-combinatorial-rl-pytorch

PyTorch implementation of Neural Combinatorial Optimization with Reinforcement Learning https://arxiv.org/abs/1611.09940

neural-rewriter

Learning to Perform Local Rewriting for Combinatorial Optimization

neuralcrf-models

CRF and hyphergraph based models combined with deep learning models.

nlp-datasets

Alphabetical list of free/public domain datasets with text data for use in Natural Language Processing (NLP)

nlp-progress

Repository to track the progress in Natural Language Processing (NLP), including the datasets and the current state-of-the-art for the most common NLP tasks.

nmt

TensorFlow Neural Machine Translation Tutorial

nmt_gan

generative adversarial nets for neural machine translation

nre

Neural Relation Extraction, including CNN, PCNN, CNN+ATT, PCNN+ATT

nrlpapers

Must-read papers on network representation learning (NRL) / network embedding (NE)

nsc

Neural Sentiment Classification

opennre

Neural Relation Extraction implemented in TensorFlow

option-critic-arch

Implementation of the Option-Critic Architecture

platemo

Evolutionary multi-objective optimization platform

pointer-generator

Code for the ACL 2017 paper "Get To The Point: Summarization with Pointer-Generator Networks"

pouch

PouchContainer is an open-source project created to promote the container technology movement.

pymoo

NSGA2, NSGA3, R-NSGA3, MOEAD, GA, DE,

reinforcement-learning

rl

seq2seq

Attention-based sequence to sequence learning

seqgan

Implementation of Sequence Generative Adversarial Nets with Policy Gradient

seqgan-1

A simplified PyTorch implementation of "SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient." (Yu, Lantao, et al.)

seqgan-text-tensorflow

:dango: SeqGAN implementation for generating text using an RNN.

seqgan_tensorflow

SeqGAN tensorflow implementation

stanford-tensorflow-tutorials

This repository contains code examples for the course CS 20SI: TensorFlow for Deep Learning Research.

state-of-the-art-result-for-machine-learning-problems

This repository provides state of the art (SoTA) results for all machine learning problems. We do our best to keep this repository up to date. If you do find a problem's SoTA result is out of date or missing, please raise this as an issue or submit Google form (with this information: research paper name, dataset, metric, source code and year). We will fix it immediately.

summarunner

The PyTorch Implementation of SummaRuNNer

temporaldifferencelearning

Temporal-difference learning is a method to compute the values of all states by sampling the environment. It approximates the current estimate of a state value based on previously learned estimates (bootstrapping).

tensorflow-seq2seq-tutorials

Dynamic seq2seq in TensorFlow, step by step

tensorflow_practice

tensorflow实战练习，包括强化学习、推荐系统、nlp等

tensorflow_rlre

Reinforcement Learning for Relation Classification from Noisy Data(TensorFlow)