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},
}
@misc{wu2026visreg,
    title   = {VISReg: Variance-Invariance-Sketching Regularization for JEPA training},
    author  = {Haiyu Wu and Randall Balestriero and Morgan Levine},
    year    = {2026},
    eprint  = {2606.02572},
    archivePrefix = {arXiv},
    primaryClass = {cs.LG},
    url     = {https://arxiv.org/abs/2606.02572},
}
@misc{kimiteam2026attentionresiduals,
    title   = {Attention Residuals},
    author  = {Kimi Team and Guangyu Chen and Yu Zhang and Jianlin Su and Weixin Xu and Siyuan Pan and Yaoyu Wang and Yucheng Wang and Guanduo Chen and Bohong Yin and Yutian Chen and Junjie Yan and Ming Wei and Y. Zhang and Fanqing Meng and Chao Hong and Xiaotong Xie and Shaowei Liu and Enzhe Lu and Yunpeng Tai and Yanru Chen and Xin Men and Haiqing Guo and Y. Charles and Haoyu Lu and Lin Sui and Jinguo Zhu and Zaida Zhou and Weiran He and Weixiao Huang and Xinran Xu and Yuzhi Wang and Guokun Lai and Yulun Du and Yuxin Wu and Zhilin Yang and Xinyu Zhou},
    year    = {2026},
    eprint  = {2603.15031},
    archivePrefix = {arXiv},
    primaryClass = {cs.CL},
    url     = {https://arxiv.org/abs/2603.15031},
}

Project details


Download files

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

Source Distribution

x_jepa-0.0.29.tar.gz (12.8 kB view details)

Uploaded Source

Built Distribution

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

x_jepa-0.0.29-py3-none-any.whl (12.2 kB view details)

Uploaded Python 3

File details

Details for the file x_jepa-0.0.29.tar.gz.

File metadata

  • Download URL: x_jepa-0.0.29.tar.gz
  • Upload date:
  • Size: 12.8 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: uv/0.8.17

File hashes

Hashes for x_jepa-0.0.29.tar.gz
Algorithm Hash digest
SHA256 a076e4f34bcae905ddc2010fd3948d2a259b7591d2b47c8b6bf3201b714bb2aa
MD5 6cc0e9d40530d8d5915943bb6025aabe
BLAKE2b-256 7d1e16c6231098c9d147d96f2e72f3a672de70144c98385802901388da476bc2

See more details on using hashes here.

File details

Details for the file x_jepa-0.0.29-py3-none-any.whl.

File metadata

  • Download URL: x_jepa-0.0.29-py3-none-any.whl
  • Upload date:
  • Size: 12.2 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: uv/0.8.17

File hashes

Hashes for x_jepa-0.0.29-py3-none-any.whl
Algorithm Hash digest
SHA256 1f1c9e6c32471e11f1fd715b0b1dbc24aeb8f749f4200754d832da1e70da262f
MD5 9d778483ea0f04de30b71e7d5f0f3d86
BLAKE2b-256 b28fd8b8dddc7886a6c686c22e04ab83f9c0df65c1df5cfe397d38be5f7f9775

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