Popular GitHub Repositories for Coordinate Ascent

Low code tool to rapidly build and coordinate multi-agent teams

Fast and accurate face landmark detection library using PyTorch; Support 68-point semi-frontal and 39-point profile landmark detection; Support both coordinate-based and heatmap-based inference; Up to 100 FPS landmark inference speed with SOTA face detector on CPU.

Pytorch implementation of convolutional neural network adversarial attack techniques

[NeurIPS 2024] MeshXL: Neural Coordinate Field for Generative 3D Foundation Models, a 3D fundamental model for mesh generation

A sparsity aware implementation of "Deep Autoencoder-like Nonnegative Matrix Factorization for Community Detection" (CIKM 2018).

Experiments of coordinate MLPs

Use the Intel D435 real-sensing camera to realize target detection based on the Yolov3 framework under the Opencv DNN framework, and realize the 3D positioning of the Objection according to the depth information. Real-time display of the coordinates in the camera coordinate system.ADD--Using Yolov5 By TensorRT model,AGX-Xavier,RealTime Object Detection

Use the Intel D435 real-sensing camera to realize object detection based on the Yolov3-5 framework under the Opencv DNN(old version)/TersorRT(now) by ROS-melodic.Real-time display of the Pointcloud in the camera coordinate system.

Unofficial implementation (replicates paper results!) of MINER: Multiscale Implicit Neural Representations in pytorch-lightning

cardiAc ultrasound Segmentation & Color-dopplEr dealiasiNg Toolbox (ASCENT)

Official source code for "Roto-translated Local Coordinate Frames For Interacting Dynamical Systems". In NeurIPS 2021.

PyTorch implementation of joint coordinate and sparse parametric encodings for offline RGB-D surface reconstruction

Experiments for ICML paper DICOD: Distributed Convolutional Coordinate Descent for Convolutional Sparse Coding, ICML 2018, T. Moreau, L. Oudre, N. Vayatis.

Like a CLIP + VQGAN. Except without a VQGAN.

Use CLIP to create matching texts + embeddings for given images; useful for XAI, adversarial training

Universal Agent Protocol (UAP) - Making AI agents work together seamlessly. The standard way for agents to find, talk to, and coordinate with each other in multi agent systems (MAS).

A modular multi-agent system framework leveraging Streamlit and Langgraph to coordinate complex tasks through a dynamically configurable, user-friendly interface.

Coordinate Ascent Variational Inference for Dirichlet Process Mixtures of Gaussians

Generative Model with Coordinate Metric Learning

Allow MCP clients like claude-desktop to use rooms to coordinate with other agents