Explore live open-source projects and AI models.

DGMs for NLP. A roadmap.

Direct Graphical Models (DGM) C++ library, a cross-platform Conditional Random Fields library, which is optimized for parallel computing and includes modules for feature extraction, classification and visualization.

Deep Generative Models with Stick-Breaking Priors

This repository provides a survey on the applications of deep generative models for offline reinforcement learning and imitation learning. We cover multiple deep generative models, including VAEs, GANs, Normalizing Flows, Transformers, and Diffusion Models.

Code for "Deep Graph Matching and Searching for Semantic Code Retrieval"

PINN, DGM and DRM

Shuaikai Shi, Lijun Zhang, Yoann Altmann and Jie Chen, "Deep Generative Model for Spatial-spectral Unmixing with Multiple Endmember Priors," in IEEE Transactions on Geoscience and Remote Sensing, 2022, doi: 10.1109/TGRS.2022.3168712.

A Revision of the Dense Conditional Random Field, downloaded from [Stanford University](http://graphics.stanford.edu/projects/densecrf). The revision supports OpenCV and DGM libraries.

No description available from source.

Code accompanying the paper "Semi-Unsupervised Learning with Deep Generative Models: Clustering and Classifying using Ultra-Sparse Labels"