Popular GitHub Repositories for Adaptive Filtering

Weaviate is an open-source vector database that stores both objects and vectors, allowing for the combination of vector search with structured filtering with the fault tolerance and scalability of a cloud-native database?.

Gorse open source recommender system engine

A unified, comprehensive and efficient recommendation library

Noise supression using deep filtering

AGiXT is a dynamic AI Agent Automation Platform that seamlessly orchestrates instruction management and complex task execution across diverse AI providers. Combining adaptive memory, smart features, and a versatile plugin system, AGiXT delivers efficient and comprehensive AI solutions.

This repository contains Deep Learning based articles , paper and repositories for Recommender Systems

Metric depth estimation from a single image

A modern, web-based photo management server. Run it on your home server and it will let you find the right photo from your collection on any device. Smart filtering is made possible by object recognition, face recognition, location awareness, color analysis and other ML algorithms.

Neural Collaborative Filtering

Deep learning for recommender systems

Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization

My personal prompt library for various LLMs + scripts & tools. Suitable for models from Deepseek, OpenAI, Claude, Meta, Mistral, Google, Grok, and others.

Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering

:chart_with_upwards_trend: Adaptive: parallel active learning of mathematical functions

SincNet is a neural architecture for efficiently processing raw audio samples.

Unofficial pytorch implementation of 'Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization' [Huang+, ICCV2017]

Factorization Machine models in PyTorch

Next RecSys Library

an implementation of Video Frame Interpolation via Adaptive Separable Convolution using PyTorch

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.