AI 开源项目榜。

Lattice methods in TensorFlow

tracking papers, datasets, and models of "large language model (LLM) for time series"

StarGANv2-VC: A Diverse, Unsupervised, Non-parallel Framework for Natural-Sounding Voice Conversion

An experiment to give an autonomous GPT agent access to a browser and have it accomplish tasks

Semantic Segmentation in Pytorch. Network include: FCN、FCN_ResNet、SegNet、UNet、BiSeNet、BiSeNetV2、PSPNet、DeepLabv3_plus、 HRNet、DDRNet

Provides end-to-end model development pipelines for LLMs and Multimodal models that can be launched on-prem or cloud-native.

Deep Reinforcement Learning Hands-On, 3E_Published by Packt

Dockerfiles for the various software layers defined in the ROCm software platform

♊ 50+ selected Gemini prompts with images, multilingual support, and instant gallery preview. Open-source prompt engineering library

My Keras implementation of the Deep Semantic Similarity Model (DSSM)/Convolutional Latent Semantic Model (CLSM) described here: http://research.microsoft.com/pubs/226585/cikm2014_cdssm_final.pdf.

MCP Monitoring with eBPF

Current state of supervised and unsupervised depth completion methods

Xcode 27 system prompts and internal documentation

Model Activity Visualiser

NeurIPS'18: Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels

This repo contains a PyTorch implementation of the paper: "Evidential Deep Learning to Quantify Classification Uncertainty"

Code for reproducing results of NIPS 2014 paper "Semi-Supervised Learning with Deep Generative Models"

LLM powered retrieval engine designed to process a ton of sources to collect a comprehensive list of entities.

This repository contains the NarrativeQA dataset. It includes the list of documents with Wikipedia summaries, links to full stories, and questions and answers.

小红书MCP服务 x-s x-t js逆向

Sample code and documentation for using the Microsoft HoloLens 2 for Computer Vision research.

Code for "Text Generation from Knowledge Graphs with Graph Transformers"

SSL_SLAM: Lightweight 3-D Localization and Mapping for Solid-State LiDAR IEEE RA-L 2021

A framework for high-performance medical image processing, neural network inference and visualization

Showcasing Google Cloud's generative AI for marketing scenarios via application frontend, backend, and detailed, step-by-step guidance for setting up and utilizing generative AI tools, including examples of their use in crafting marketing materials like blog posts and social media content, nl2sql analysis, and campaign personalization.

"CatchMe: Make Your AI Agents Truly Personal"