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Web labeling tool for bitmap images and point clouds
A C++ & Python viewer for 3D data like meshes and point clouds
Pytorch framework for doing deep learning on point clouds.
Pointcept: a codebase for point cloud perception research. Latest works: PTv3 (CVPR'24 Oral), PPT (CVPR'24), OA-CNNs (CVPR'24), MSC (CVPR'23)
[ICCV'23 Workshop] SAM3D: Segment Anything in 3D Scenes
An extension of Open3D to address 3D Machine Learning tasks
PyTorch3D is FAIR's library of reusable components for deep learning with 3D data
This repository contains the code for the paper“iLLM-TSC: Integration reinforcement learning and large language model for traffic signal control policy improvement”
A sample app for the Retrieval-Augmented Generation pattern running in Azure, using Azure Cognitive Search for retrieval and Azure OpenAI large language models to power ChatGPT-style and Q&A experi…
Streamlit — A faster way to build and share data apps.
User-friendly AI Interface (Supports Ollama, OpenAI API, ...)
A quantum reinforcement learning framework based on PyTorch and PennyLane.
Code for Q-learning with parametrized quantum circuits in OpenAI Gym environments.
Code for "Optimizing Quantum Variational Circuits with Deep Reinforcement Learning"
A collection of optimization algorithms for maximizing unconstrained submodular set functions.
A curated list of papers, blogs, and research publications in the field of Quantum Machine Learning
Simple implementation for Constrained Policy Optimization in Pytorch
The repository is for safe reinforcement learning baselines.
A sample power system was modeled using MATLAB Simulink and all six types of faults were introduced into the transmission line of the power system. An ML classifier using 8 types model was implemen…
Classical-Quantum hybrid model for credit card fraud detection
Python version of unit commitment solver using gurobi-python api.
Cirq/PyTorch implementation of Quantum Architecture Search via Deep Reinforcement Learning by (Kuo et al., 2021)
Reinforcement learning for unit commitment
Framework for solving discrete optimization problems using a combination of Mixed-Integer Linear Programming (MIP) and Machine Learning (ML)