Popular GitHub Repositories for Matrix Profile

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

Profile-Based Long-Term Memory for AI Applications

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).

PyTorch Extension Library of Optimized Autograd Sparse Matrix Operations

Programmable CUDA/C++ GPU Graph Analytics

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).

Fast and accurate face landmark detection library using PyTorch; Support 68-point semi-frontal and 39-point profile landmark detection; Support both coordinate-based and heatmap-based inference; Up to 100 FPS landmark inference speed with SOTA face detector on CPU.

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

The goal of this library is to generate more helpful exception messages for matrix algebra expressions for numpy, pytorch, jax, tensorflow, keras, fastai.

OCTIS: Comparing Topic Models is Simple! A python package to optimize and evaluate topic models (accepted at EACL2021 demo track)