Popular GitHub Repositories for Ibp Bounds

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

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

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

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

Set up a seamless process where historical sales data in SAP Integrated Business Planning for Supply Chain is used to calculate the statistical forecast via a forecasting algorithm based on Prophet implemented in SAP Data Intelligence.

A repository about Robust Deep Neural Networks with Uncertainty, Local Competition and Error-Correcting-Output-Codes in TensorFlow.

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

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.

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

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