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-0.0.25.tar.gz (12.4 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-0.0.25-py3-none-any.whl (11.9 kB view details)

Uploaded Python 3

File details

Details for the file x_jepa-0.0.25.tar.gz.

File metadata

  • Download URL: x_jepa-0.0.25.tar.gz
  • Upload date:
  • Size: 12.4 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: uv/0.8.17

File hashes

Hashes for x_jepa-0.0.25.tar.gz
Algorithm Hash digest
SHA256 db6c8270fd2429bd86fabaa0a31691ef09a071b64e8e687ca7e009dd7027b6cf
MD5 4628fe99fa15dec101f6733c45b9785c
BLAKE2b-256 5d9c7392e59fa8eba3a4f1d9f9a2ea894abb1e132228d4ca2347265150b8fcff

See more details on using hashes here.

File details

Details for the file x_jepa-0.0.25-py3-none-any.whl.

File metadata

  • Download URL: x_jepa-0.0.25-py3-none-any.whl
  • Upload date:
  • Size: 11.9 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: uv/0.8.17

File hashes

Hashes for x_jepa-0.0.25-py3-none-any.whl
Algorithm Hash digest
SHA256 3785bee3671ec4eef9227ad095aedf167a7d0f03d67a248bbf4386195dcb3235
MD5 47aba4223a9d1594f40ba36ba7496ea4
BLAKE2b-256 01f5616faa2809a1512b0c194ad00d93c1d14c2b699c4b8520e73b34ad6744dd

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