Popular GitHub Repositories for Picking Locks

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

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

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.

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

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

this.env defines, locks, and hashes the environment to establish a reliable and secure operational context. By detecting and responding to changes, it ensures consistency and integrity, especially for intelligent systems and machine learning applications.

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.

S&P100 stocks analysis via Graph Neural Networks (Forecasting, Clustering, Trend classification, Stocks ranking for optimal stock picking)

CNN Event Detection & RNN Phase Picking (in Tensorflow)

Localize AI Phase Picker with Rule-based Algorithm for Generalized Earthquake Detection

Deep Reinforcement Learning for Robotic Pushing and Picking in Cluttered Environment

KLEP (Key-Lock-Executable-Process) is a groundbreaking AI framework that utilizes symbolic AI for dynamic decision-making. It integrates keys, locks, executables, and processes to foster ethical, modular, and transparent AI applications, offering a novel approach for developers and researchers in AI and cognitive science.

We train an agent to manuver in a 3-D environment avoiding blue bananas and picking yellow ones as fast as possible.

Simulation of autonomous trash-picking behavior with human interaction and ethical decision-making logic, featuring object classification, permission requests, and battery monitoring.

This research employs an iterative active learning approach for cryo-EM particle picking that selects informative samples based on uncertainty metrics, progressively enhancing model performance while reducing labeling requirements.