ryfan-rs Goto Github PK

thutag

A Package of Keyphrase Extraction and Social Tag Suggestion, the project has moved to

tianchi-ruijin

瑞金医院MMC人工智能辅助构建知识图谱大赛-baseline

tikz

Complete collection of my PGF/TikZ figures.

tile-lnglat-transform

提供了高德、百度、谷歌和腾讯地图的经纬度坐标与瓦片坐标的相互转换

time-nlp

中文语句中的时间语义识别。即通过分析中文语句，识别出话语中提到的时间。

time-nlpy

Time-NLP的python版本 中文时间表达识别

tools_for_corpus_of_people_daily

人民日报语料处理工具集 | Tools for Corpus of People's Daily

transferlearning

Resources and codes about transfer learning and domain adaptation--迁移学习

triple_extraction

基于依存句法与语义角色标注的三元组抽取

triplets-extraction

triplets extraction based on tagging scheme

tuiblogs

优秀的计算机编程类博客和文章 share excellent blogs and sites

tutorials

机器学习相关教程

twitter-sentiment-analysis

Sentiment analysis on tweets using Naive Bayes, SVM, CNN, LSTM, etc.

unit

universalstyletransfer

The source code of NIPS17 'Universal Style Transfer via Feature Transforms'.

unsupervised-learning-intrinsic-images

Implementation of the intrinsic image decomposition algorithm described in "Learning Intrinsic Image Decomposition from Watching the World, Z. Li and N. Snavely, CVPR 2018"

unsupervised-vocabulary-search

完整的新词发现&词库构建例子

unsupervisedscalablerepresentationlearningtimeseries

Unsupervised Scalable Representation Learning for Multivariate Time Series: Experiments

video-prediction

spatial-temporal prediction for video

videocapsulenet

Code for VideoCapsuleNet: A Simplified Network for Action Detection

viewpointextraction

中文观点句抽取系统

virusbroadcast

A java virus broadcast simulation

vision_networks

Repo about neural networks for images handling

vsepp

PyTorch Code for the paper "VSE++: Improved Visual Semantic Embeddings"

w2v_textrank

文本自动摘要算法：用Word2Vec改进的TextRank算法

wanfangspider

wide-residual-networks

3.8% and 18.3% on CIFAR-10 and CIFAR-100

wikidata-corpus

Train Wikidata with word2vec for word embedding tasks

wikiextractor

A tool for extracting plain text from Wikipedia dumps

wikipedia-summary-dataset

This dataset contains all titles and summaries (or introductions) of English Wikipedia articles, extracted in september of 2017. It could be useful if one wants to use the smaller, more concise, and more definitional summaries in their research. Or if one just wants to use a smaller but still diverse dataset for efficient training with resource constraints.