Popular GitHub Repositories for Hamiltonian Path

Motion planning algorithms commonly used on autonomous vehicles. (path planning + path tracking)

Bayesian inference with probabilistic programming.

A selection of state-of-the-art research materials on trajectory prediction

MiniSora: A community aims to explore the implementation path and future development direction of Sora.

High-fidelity performance metrics for generative models in PyTorch

This guide details a learning path for high school students looking to explore the field of Machine Learning & Artificial Intelligence.

Connectionist Temporal Classification (CTC) decoding algorithms: best path, beam search, lexicon search, prefix search, and token passing. Implemented in Python.

Python sample codes and documents about Autonomous vehicle control algorithm. This project can be used as a technical guide book to study the algorithms and the software architectures for beginners.

Machine learning algorithms for many-body quantum systems

RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation

Trajectory Planner in Multi-Agent and Dynamic Environments

Code for our paper "Hamiltonian Neural Networks"

Implementing Reinforcement Learning, namely Q-learning and Sarsa algorithms, for global path planning of mobile robot in unknown environment with obstacles. Comparison analysis of Q-learning and Sarsa

Simulation of path planning for self-driving vehicles in Unity. This is also an implementation of the Hybrid A* pathfinding algorithm which is useful if you are interested in pathfinding for vehicles.

[ICCV 2023] OccFormer: Dual-path Transformer for Vision-based 3D Semantic Occupancy Prediction

Complete path for a beginner to become a Machine Learning Scientist!

Reinforcement learning with A* and a deep heuristic

An ML framework to accelerate research and its path to production.

Be Driven ?

User can set up destination for any agent to navigate on Google Map and learn the best route for the agent based on its current condition and the traffic. Our result is 10% less energy consumption than the route provided by Google map