Popular GitHub Repositories for Upper Bounds

We present MocapNET, a real-time method that estimates the 3D human pose directly in the popular Bio Vision Hierarchy (BVH) format, given estimations of the 2D body joints originating from monocular color images. Our contributions include: (a) A novel and compact 2D pose NSRM representation. (b) A human body orientation classifier and an ensemble of orientation-tuned neural networks that regress the 3D human pose by also allowing for the decomposition of the body to an upper and lower kinematic hierarchy. This permits the recovery of the human pose even in the case of significant occlusions. (c) An efficient Inverse Kinematics solver that refines the neural-network-based solution providing 3D human pose estimations that are consistent with the limb sizes of a target person (if known). All the above yield a 33% accuracy improvement on the

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).

[Materials & Design 2024] A Bayesian global optimization package for material design ｜ Adaptive Learning | Active Learning

Prediction of respiratory diseases such as COPD(Chronic obstructive pulmonary disease), URTI(upper respiratory tract infection), Bronchiectasis, Pneumonia, Bronchiolitis with the help of deep neural networks or deep learning. We have constructed a deep neural network model that takes in respiratory sound as input and classifies the condition of its respiratory system. It not only classifies among the above-mentioned disease but also classifies if a person’s respiratory system is healthy or not with higher accuracy and precision.

Efficient, lightweight variational inference and approximation bounds

implement basic and contextual MAB algorithms for recommendation system

In This repository I made some simple to complex methods in machine learning. Here I try to build template style code.

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".

Official implementation for LoLDU: Low-Rank Adaptation via Lower-Diag-Upper Decomposition for Parameter-Efficient Fine-Tuning

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

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.

An experimental study on upper limb position invariant EMG signal classification using various machine learning models such as Linear regression, logistic regression, SVM, SGD etc.This paper is focused on the man-machine interfaces for proper controlling of prosthtic devices with multiple degrees of freedom.This is a research project I have done under Prof. Suman Samui.

Final Project For IAT481, Android Application for Body Part Detection in the Upper Extremity and Fracture Detection using deep learning (Resnet50)

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