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Create AUTHORS.md
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AUTHORS.md

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<img src="https://github.com/user-attachments/assets/1f327e57-8ee1-4a2f-afd3-2bbce885c2f8" width="200"/>
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CEBRA was initially developed by **Mackenzie Mathis** and **Steffen Schneider** (2021+), who are co-inventors on the patent application [WO2023143843](https://infoscience.epfl.ch/entities/patent/0d9debed-4d22-47b7-bad1-f211e7010323).
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**Jin Hwa Lee** contributed significantly to our first paper:
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> **Schneider, S., Lee, J.H., & Mathis, M.W.**
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> [*Learnable latent embeddings for joint behavioural and neural analysis.*](https://doi.org/10.1038/s41586-023-06031-6)
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> Nature 617, 360–368 (2023)
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CEBRA is actively developed by [**Mackenzie Mathis**](https://www.mackenziemathislab.org/) and [**Steffen Schneider**](https://dynamical-inference.ai/) and their labs.
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It is a publicly available tool that has benefited from contributions and suggestions from many individuals: [CEBRA/graphs/contributors](https://github.com/AdaptiveMotorControlLab/CEBRA/graphs/contributors).
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## CEBRA Extensions
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### 2023
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- **Steffen Schneider, Rodrigo González Laiz, Markus Frey, Mackenzie W. Mathis**
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[*Identifiable attribution maps using regularized contrastive learning.*](https://sslneurips23.github.io/paper_pdfs/paper_80.pdf)
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NeurIPS 4th Workshop on Self-Supervised Learning: Theory and Practice (2023)
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### 2025
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- **Steffen Schneider, Rodrigo González Laiz, Anastasiia Filippova, Markus Frey, Mackenzie W. Mathis**
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[*Time-series attribution maps with regularized contrastive learning.*](https://openreview.net/forum?id=aGrCXoTB4P)
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AISTATS (2025)
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