Popular GitHub Repositories for Particle Picking

Genetic Algorithm, Particle Swarm Optimization, Simulated Annealing, Ant Colony Optimization Algorithm,Immune Algorithm, Artificial Fish Swarm Algorithm, Differential Evolution and TSP(Traveling salesman)

Computer Vision library for human-computer interaction. It implements Head Pose and Gaze Direction Estimation Using Convolutional Neural Networks, Skin Detection through Backprojection, Motion Detection and Tracking, Saliency Map.

Implementation of hyperparameter optimization/tuning methods for machine learning & deep learning models (easy&clear)

Implementations of (theoretical) generative adversarial networks and comparison without cherry-picking

EQTransformer, a python package for earthquake signal detection and phase picking using AI.

Autonomous car simulator (based on JavaScript & WebGL) implemented by fuzzy control system, genetic algorithm and particle swarm optimization.

The official code repository supporting the book, Grokking Artificial Intelligence Algorithms

MIT-Princeton Vision Toolbox for the Amazon Picking Challenge 2016 - RGB-D ConvNet-based object segmentation and 6D object pose estimation.

Pipeline for particle picking in cryo-electron microscopy images using convolutional neural networks trained from positive and unlabeled examples. Also featuring micrograph and tomogram denoising with DNNs.

HILO-MPC is a Python toolbox for easy, flexible and fast development of machine-learning-supported optimal control and estimation problems

Steven C. Y. Hung, Cheng-Hao Tu, Cheng-En Wu, Chien-Hung Chen, Yi-Ming Chan, and Chu-Song Chen, "Compacting, Picking and Growing for Unforgetting Continual Learning," Thirty-third Conference on Neural Information Processing Systems, NeurIPS 2019

Multi-agent project (commnet, bicnet, maddpg) in pytorch for Multi-Agent Particle Environment

Code for "Picking Winning Tickets Before Training by Preserving Gradient Flow" https://openreview.net/pdf?id=SkgsACVKPH

A Deep learning library for neutrino telescopes

A particle-based encoding for Neural Radiance Fields

Implementation of Particle Guidance: non-I.I.D. Diverse Sampling with Diffusion Models

JAX-SPH: A Differentiable Smoothed Particle Hydrodynamics Framework

Training, generation, and analysis code for Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics

Particle Swarm Optimizer For Neural Network Training

Multi-Agent Step Race Benchmark: Assessing LLM Collaboration and Deception Under Pressure. A multi-player “step-race” that challenges LLMs to engage in public conversation before secretly picking a move (1, 3, or 5 steps). Whenever two or more players choose the same number, all colliding players fail to advance.