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research-ms-loss

MS-Loss: Multi-Similarity Loss for Deep Metric Learning

residual_attention_network

Keras implementation of Residual Attention Network

residualattentionnetwork-pytorch

a pytorch code about Residual Attention Network. This code is based on two projects from

resnest

ResNeSt: Split-Attention Network

resnetgpt

用Resnet101+GPT搭建一个玩王者荣耀的AI

retagnn

retinaface-pytorch

这是一个retinaface-pytorch的源码，可以用于训练自己的模型。

revisiting-self-supervised

rezero

Official PyTorch Repo for "ReZero is All You Need: Fast Convergence at Large Depth"

ripplenet

A tensorflow implementation of RippleNet

ripplenet-pytorch

A PyTorch implementation of RippleNet

rl4rs-papers

A collection of research and survey papers of reforcement learning (RL) based recommender system techniques.

rlans

Learning Adversarial Networks for Semi-Supervised Text Classification via Policy Gradient

rmdl

RMDL: Random Multimodel Deep Learning for Classification

robert_recurrence_over_bert

pyTorch implementation of Recurrence over BERT (RoBERT) based on this paper https://arxiv.org/abs/1910.10781

roberta_zh

RoBERTa for Chinese

robertaabsa

Implementation of paper Does syntax matter? A strong baseline for Aspect-based Sentiment Analysis with RoBERTa.

robust04-analysis

Meta-Analysis of Robust04 Papers (Yang et al., SIGIR 2019)

rocket-launching

A universal and efficient framework for training well-performing light net

role2vec

A scalable Gensim implementation of "Learning Role-based Graph Embeddings" (IJCAI 2018).

rs_paper_study

推荐系统学习笔记

rsalgorithms

Some algorithms about traditional and social recommendation.

rsna2018

rspaper-read

Share Some Recommender System Paper I read.

rspapers

Must-read papers on Recommender System.

ruijin_round2

瑞金医院MMC人工智能辅助构建知识图谱大赛复赛

samequestion

基于Adversarial Attack的问题等价性判别比赛

samn

This is our implementation of SAMN: Social Attentional Memory Network

samples-for-ai

Samples for getting started with deep learning across TensorFlow, CNTK, Theano and more.

sasrec

SASRec: Self-Attentive Sequential Recommendation