Dr. Yann LeCun
Chief AI Scientist
Verified Proof of Work Artifacts
2 items catalogedEach artifact below represents an authenticated research publication, production code repository, or technical architectural framework directly authored or co-created by Dr. Yann LeCun. Every entry undergoes editorial source verification.
A Path Towards Autonomous Machine Intelligence (JEPA Architecture Framework)
Published the comprehensive theoretical blueprint proposing Joint Embedding Predictive Architecture (JEPA) as an alternative to generative pixel-level or token-level prediction, learning representations in abstract embedding space to enable hierarchical planning.
Action-conditional latent dynamics models remain active research topics; translating abstract latent representations back into fine-grained execution paths requires auxiliary decoding heads.
V-JEPA: Self-Supervised Video Representation Learning by Joint Embedding Prediction
Co-authored the implementation and empirical validation of V-JEPA, training Vision Transformers on masked spatiotemporal video tubes using feature-space prediction without pixel reconstruction or data augmentation, achieving superior efficiency on downstream motion and action classification.
Requires extensive video pre-training compute (hundreds of GPU-days); does not directly produce human-interpretable generative video frames.