Popular GitHub Repositories for Distance Matrix

A Python scikit for building and analyzing recommender systems

Cross-platform chatbot framework made with love

A Python implementation of LightFM, a hybrid recommendation algorithm.

Deep recommender models using PyTorch.

High-efficiency floating-point neural network inference operators for mobile, server, and Web

Recommender Learning with Tensorflow2.x

[CVPR 2021] Official PyTorch implementation for Transformer Interpretability Beyond Attention Visualization, a novel method to visualize classifications by Transformer based networks.

Acceleration package for neural networks on multi-core CPUs

自然语言处理（nlp），小姜机器人（闲聊检索式chatbot），BERT句向量-相似度（Sentence Similarity），XLNET句向量-相似度（text xlnet embedding），文本分类（Text classification）， 实体提取（ner，bert+bilstm+crf），数据增强（text augment, data enhance），同义句同义词生成，句子主干提取（mainpart），中文汉语短文本相似度，文本特征工程，keras-http-service调用

A fast, ergonomic and portable tensor library in Nim with a deep learning focus for CPU, GPU and embedded devices via OpenMP, Cuda and OpenCL backends

A super fast Graph Database uses GraphBLAS under the hood for its sparse adjacency matrix graph representation. Our goal is to provide the best Knowledge Graph for LLM (GraphRAG).

High-fidelity performance metrics for generative models in PyTorch

PyTorch - FID calculation with proper image resizing and quantization steps [CVPR 2022]

PyTorch Extension Library of Optimized Autograd Sparse Matrix Operations

Programmable CUDA/C++ GPU Graph Analytics

Pure PyTorch Implementation of NVIDIA paper on Instant Training of Neural Graphics primitives: https://nvlabs.github.io/instant-ngp/

This is the repository of our article published in RecSys 2019 "Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches" and of several follow-up studies.

A Comparative Framework for Multimodal Recommender Systems

A parallel implementation of "graph2vec: Learning Distributed Representations of Graphs" (MLGWorkshop 2017).

Library for specialized dense and sparse matrix operations, and deep learning primitives.