Skip to main content

Explorations into JEPA

Project description

x-jepa

Explorations into some of the approaches advocated by Yann LeCun, and just a more wholistic architecture (JEPA) in general

Citations

@inproceedings{LeCun2022APT,
    title   = {A Path Towards Autonomous Machine Intelligence},
    author  = {Yann LeCun and Courant},
    year    = {2022},
    url     = {https://api.semanticscholar.org/CorpusID:251881108}
}
@misc{maes2026leworldmodelstableendtoendjointembedding,
    title   = {LeWorldModel: Stable End-to-End Joint-Embedding Predictive Architecture from Pixels},
    author  = {Lucas Maes and Quentin Le Lidec and Damien Scieur and Yann LeCun and Randall Balestriero},
    year    = {2026},
    eprint  = {2603.19312},
    archivePrefix = {arXiv},
    primaryClass = {cs.LG},
    url     = {https://arxiv.org/abs/2603.19312},
}
@misc{teoh2026nextlatentpredictiontransformerslearn,
    title   = {Next-Latent Prediction Transformers Learn Compact World Models},
    author  = {Jayden Teoh and Manan Tomar and Kwangjun Ahn and Edward S. Hu and Tim Pearce and Pratyusha Sharma and Akshay Krishnamurthy and Riashat Islam and Alex Lamb and John Langford},
    year    = {2026},
    eprint  = {2511.05963},
    archivePrefix = {arXiv},
    primaryClass = {cs.LG},
    url     = {https://arxiv.org/abs/2511.05963},
}
@inproceedings{saravanos2026learningtooptimize,
    title   = {Learning-to-Optimize via Deep Unfolded Flows},
    author  = {Augustinos D Saravanos and Oswin So and H M Sabbir Ahmad and Chuchu Fan},
    booktitle = {Forty-third International Conference on Machine Learning},
    year    = {2026},
    url     = {https://openreview.net/forum?id=ZOtOq7hxJP}
}
@misc{farebrother2026compositionalplanningjumpyworld,
    title   = {Compositional Planning with Jumpy World Models},
    author  = {Jesse Farebrother and Matteo Pirotta and Andrea Tirinzoni and Marc G. Bellemare and Alessandro Lazaric and Ahmed Touati},
    year    = {2026},
    eprint  = {2602.19634},
    archivePrefix = {arXiv},
    primaryClass = {cs.LG},
    url     = {https://arxiv.org/abs/2602.19634},
}
@misc{balestriero2025lejepaprovablescalableselfsupervised,
    title   = {LeJEPA: Provable and Scalable Self-Supervised Learning Without the Heuristics},
    author  = {Randall Balestriero and Yann LeCun},
    year    = {2025},
    eprint  = {2511.08544},
    archivePrefix = {arXiv},
    primaryClass = {cs.LG},
    url     = {https://arxiv.org/abs/2511.08544},
}

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

x_jepa-0.0.19-py3-none-any.whl (3.7 kB view details)

Uploaded Python 3

File details

Details for the file x_jepa-0.0.19-py3-none-any.whl.

File metadata

  • Download URL: x_jepa-0.0.19-py3-none-any.whl
  • Upload date:
  • Size: 3.7 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: uv/0.8.17

File hashes

Hashes for x_jepa-0.0.19-py3-none-any.whl
Algorithm Hash digest
SHA256 65bde2d336bd704a82e12c3ec793c0b168a38104e58fd1432243dc239e7c91a2
MD5 402f6626645ba6627e85031c9b7a56ca
BLAKE2b-256 cae27ed1bdd4e0ada97d81dc9737ee01c4ba8a29b2c8d9943c0afba80f670fb2

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page