Popular GitHub Repositories for Sequential Halving

Time series Timeseries Deep Learning Machine Learning Python Pytorch fastai | State-of-the-art Deep Learning library for Time Series and Sequences in Pytorch / fastai

A unified, comprehensive and efficient recommendation library

[NeurIPS 2023 Spotlight] LightZero: A Unified Benchmark for Monte Carlo Tree Search in General Sequential Decision Scenarios (awesome MCTS)

Transformers4Rec is a flexible and efficient library for sequential and session-based recommendation and works with PyTorch.

Next RecSys Library

Attention mechanism for processing sequential data that considers the context for each timestamp.

Recommender Systems Paperlist that I am interested in

Keras Layer implementation of Attention for Sequential models

HLLM: Enhancing Sequential Recommendations via Hierarchical Large Language Models for Item and User Modeling

Grid2Op a testbed platform to model sequential decision making in power systems.

Recurrent Variational Autoencoder that generates sequential data implemented with pytorch

[NeurIPS'24 Spotlight] Text2CAD: Generating Sequential CAD Designs from Beginner-to-Expert Level Text Prompts

StoryGAN: A Sequential Conditional GAN for Story Visualization

Automated Hypothesis Testing with Agentic Sequential Falsifications

A highly-modularized and recommendation-efficient recommendation library based on PyTorch.

Variational Auto-Encoders in a Sequential Setting.

SGAS: Sequential Greedy Architecture Search (CVPR'2020) https://www.deepgcns.org/auto/sgas

(ECCV 2020) Conditional Sequential Modulation for Efficient Global Image Retouching

rec_pangu is a flexible open-source project for recommendation systems. It incorporates diverse AI models like ranking algorithms, sequence recall, multi-interest models, and graph-based techniques. Designed for both beginners and advanced users, it enables rapid construction of efficient, custom recommendation engines.

Small scale distributed training of sequential deep learning models, built on Numpy and MPI.