Reliable, minimal and scalable library for pretraining foundation and world models

Official code for the paper Graph-level Representation Learning with Joint-Embedding Predictive Architectures published in Transactions on Machine Learning Research (TMLR)

Official codebase for TI-JEPA, the Text-Image Joint-Embedding Predictive Architecture. First outlined in our Capstone Project Defense, got 9.9/10

Curated papers, code, datasets, and benchmarks for medical world models in imaging, EHR trajectories, treatment planning, surgical AI, robotics, and virtual-cell simulation.

A practical explainer of JEPA, Meta AI’s Joint Embedding Predictive Architecture, with diagrams and insights comparing JEPA and Transformers.