Popular GitHub Repositories for Crown Bounds

auto_LiRPA: An Automatic Linear Relaxation based Perturbation Analysis Library for Neural Networks and General Computational Graphs

alpha-beta-CROWN: An Efficient, Scalable and GPU Accelerated Neural Network Verifier (winner of VNN-COMP 2021, 2022, 2023, and 2024)

Certified defense to adversarial examples using CROWN and IBP. Also includes GPU implementation of CROWN verification algorithm (in PyTorch).

Efficient, lightweight variational inference and approximation bounds

implement basic and contextual MAB algorithms for recommendation system

Pytorch implementation of our paper accepted by ECCV2022 -- Dynamic Dual Trainable Bounds for Ultra-low Precision Super-Resolution Networks

β-CROWN: Efficient Bound Propagation with Per-neuron Split Constraints for Neural Network Verification

Implementation of the paper "Improving Optimization Bounds using Machine Learning: Decision Diagrams meet Deep Reinforcement Learning".

Fastened CROWN: Tightened Neural Network Robustness Certificates

AI for the game "Connect Four". Available on PyPI.

Detect crown-of-thorns starfish (COTS) in underwater image data based on YOLOv5 and Weighted Box Fusion (WBF). Kaggle: TensorFlow - Help Protect the Great Barrier Reef

Welcome to the ongoing adventures of Stan-The Growing Stump in his never-ending quest for understanding and connection. Stan's journey explores the bounds of artificial intelligence within the framework of a Discord bot, navigating the complexities of memory, interaction, and existential musing.

A Comparative Evaluation of Active Learning Methods in Deep Recommendation

Code for the paper "Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs", AAAI Conference on Artificial Intelligence 2024

This repo analyzes capacity bounds of communication systems with quantization and spectral constraints. It includes theoretical derivations and numerical evaluations of mutual information under coarse quantization and bandwidth limitations, with applications in modern wireless systems.

Research project investigating the addition of confidence bounds to the drift diffusion model (DDM) in order to explain confidence ratings and their associated reaction times.

Quadratic Upper Bound Loss for Robust Adversarial Training

Welcome to the repository for my Cancer Detection Model, projects, and work from the Harvard AI Summer Bootcamp 2025, a five-day program exploring the fundamentals and frontiers of artificial intelligence.