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StellarGraph - Machine Learning on Graphs
《深入浅出图神经网络：GNN原理解析》配套代码
Representation learning on large graphs using stochastic graph convolutions.
PyTorch Implementation and Explanation of Graph Representation Learning papers: DeepWalk, GCN, GraphSAGE, ChebNet & GAT.
Mining effective negative training samples for keyword spotting (PyTorch)
Stochastic training of graph convolutional networks
Pytorch Geometric Tutorials
PyTorch implementation of "Simple and Deep Graph Convolutional Networks"
Code for EMNLP18 paper "Spherical Latent Spaces for Stable Variational Autoencoders"
Library for Textless Spoken Language Processing
刷算法全靠套路，认准 labuladong 就够了！English version supported! Crack LeetCode, not only how, but also why.
Transducers for C++ — Clojure style higher order push/pull sequence transformations
A curated list of speech and natural language processing resources
PyTorch Implementation of NeurIPS 2020 paper "Learning Sparse Prototypes for Text Generation"
A Collection of Variational Autoencoders (VAE) in PyTorch.
SailAlign is an open-source software toolkit for robust long speech-text alignment implementing an adaptive, iterative speech recognition and text alignment scheme that allows for the processing of…
[NeurIPS 2021]: Improve the GNN expressivity and scalability by decoupling the depth and receptive field of state-of-the-art GNN architectures
[ICLR 2020; IPDPS 2019] Fast and accurate minibatch training for deep GNNs and large graphs (GraphSAINT: Graph Sampling Based Inductive Learning Method).
pytorch implementation of structure2vec (https://arxiv.org/abs/1603.05629)
Course notes for CS228: Probabilistic Graphical Models.
Implementation of Graph Auto-Encoders in TensorFlow