garfieldsun's Stars
CyC2018/CS-Notes
:books: 技术面试必备基础知识、Leetcode、计算机操作系统、计算机网络、系统设计
deezer/spleeter
Deezer source separation library including pretrained models.
thunlp/GNNPapers
Must-read papers on graph neural networks (GNN)
openai/jukebox
Code for the paper "Jukebox: A Generative Model for Music"
microsoft/NUWA
A unified 3D Transformer Pipeline for visual synthesis
MLNLP-World/Top-AI-Conferences-Paper-with-Code
MLNLP: This repository is a collection of AI top conferences papers (e.g. ACL, EMNLP, NAACL, COLING, AAAI, IJCAI, ICLR, NeurIPS, and ICML) with open resource code
datawhalechina/thorough-pytorch
PyTorch入门教程，在线阅读地址：https://datawhalechina.github.io/thorough-pytorch/
nateshmbhat/pyttsx3
Offline Text To Speech synthesis for python
FighterLYL/GraphNeuralNetwork
《深入浅出图神经网络：GNN原理解析》配套代码
datawhalechina/team-learning-data-mining
主要存储Datawhale组队学习中“数据挖掘/机器学习”方向的资料。
luwill/Machine_Learning_Code_Implementation
Mathematical derivation and pure Python code implementation of machine learning algorithms.
kmkolasinski/deep-learning-notes
Experiments with Deep Learning
bytedance/music_source_separation
datawhalechina/team-learning-nlp
主要存储Datawhale组队学习中“自然语言处理”方向的资料。
makeyourownneuralnetwork/gan
python notebooks accompanying the book Make Your Own GAN
Renovamen/Speech-and-Text
Speech to text (PocketSphinx, Iflytex API, Baidu API) and text to speech (pyttsx3) | 语音转文字（PocketSphinx、百度 API、科大讯飞 API）和文字转语音（pyttsx3）
maziarraissi/DeepHPMs
Deep Hidden Physics Models: Deep Learning of Nonlinear Partial Differential Equations
lelechen63/ATVGnet
CVPR 2019
levimcclenny/SA-PINNs
Implementation of the paper "Self-Adaptive Physics-Informed Neural Networks using a Soft Attention Mechanism" [AAAI-MLPS 2021]
PredictiveIntelligenceLab/MultiscalePINNs
wmylxmj/Pix2Pix-Keras
基于pix2pix模型的动漫图片自动上色(keras实现) 2019-2-25
JackHCC/Awesome-Uplift-Model
🛠 How to Apply Causal ML to Real Scene Modeling？How to learn Causal ML？【✔从Causal ML到实际场景的Uplift建模】
sksteinke/schroedinger-deeplearning
A simple neural network solver for the 1-D Schrödinger equation.
201518018629031/HGATRD
Xovee/ccgl
TKDE 22. CCCL: Contrastive Cascade Graph Learning.
millskyle/deep_learning_and_the_schrodinger_equation
Examples of the generation and training procedures from Deep Learning and the Schrodinger Equation
shawshany/IMDB
This tutorial focuses on Word2Vec for sentiment analysis.
wzpan/lipsync-demo
Cocos lipsync 插件的 demo 示例
httn22/Causal_Inference_NLP_Social_Sciences
Repository of course materials for the course Causal Inference in NLP for Social Sciences, taught by Dr. Huyen Nguyen at University of Hamburg in Summer semester 2022.
Jagath918/Measuring-User-s-Influence-in-Twitter
Abstract— Directed links in social media may represent something from intimate friendships to common interests, or even a passion for breaking news or celebrity gossip. Such directed links confirm the flow of data and therefore indicate a user's influence on others — an inspiration that is crucial in social science and infective agent selling. Throughout this paper, using a good deal of data collected from Twitter, we tend to gift an in-depth comparison of 3 measures of influence: indegree, retweets, and mentions. Supported these measures, we tend to investigate the dynamics of user influence across topics and time. We tend to create many fascinating observations. First, in style users World Health Organization have high indegree are not essentially prestigious in terms of spawning retweets or mentions. Second, most prestigious users will hold vital influence over a spread of topics. We tend to believe that these findings give new insights for infective agent selling and counsel that topological measures like indegree alone reveals very little or no concerning the influence of a user. These measures are terribly numerous. Some are supported easy metrics provided by the Twitter API, whereas others are supported complicated mathematical models.