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Is Model Collapse Inevitable? Breaking the Curse of Recursion by Accumulating Real and Synthetic Data.

M. Gerstgrasser, R. Schaeffer, A. Dey, R. Rafailov, H. Sleight, J. Hughes, T. Korbak, R. Agrawal, D. Pai, A. Gromov, D. Roberts, D. Yang, D. Donoho, and S. Koyejo. CoRR, (2024)

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Uli Schaeffer

Annegrete Schaeffer

Harry Schaeffer

Helmut Schaeffer

Michael Schaeffer

Other publications of authors with the same name

Why Has Predicting Downstream Capabilities of Frontier AI Models with Scale Remained Elusive?R. Schaeffer, H. Schoelkopf, B. Miranda, G. Mukobi, V. Madan, A. Ibrahim, H. Bradley, S. Biderman, and S. Koyejo. CoRR, (2024)Towards an Improved Understanding and Utilization of Maximum Manifold Capacity Representations.R. Schaeffer, V. Lecomte, D. Pai, A. Carranza, B. Isik, A. Unell, M. Khona, T. Yerxa, Y. LeCun, S. Chung and 3 other author(s). CoRR, (2024)Streaming Inference for Infinite Non-Stationary Clustering.R. Schaeffer, G. Liu, Y. Du, S. Linderman, and I. Fiete. CoLLAs, volume 199 of Proceedings of Machine Learning Research, page 310-326. PMLR, (2022)Reverse-engineering recurrent neural network solutions to a hierarchical inference task for mice.R. Schaeffer, M. Khona, L. Meshulam, I. Laboratory, and I. Fiete. NeurIPS, (2020)Quantifying Variance in Evaluation Benchmarks.L. Madaan, A. Singh, R. Schaeffer, A. Poulton, S. Koyejo, P. Stenetorp, S. Narang, and D. Hupkes. CoRR, (2024)Efficient online inference for nonparametric mixture models.R. Schaeffer, B. Bordelon, M. Khona, W. Pan, and I. Fiete. UAI, volume 161 of Proceedings of Machine Learning Research, page 2072-2081. AUAI Press, (2021)No Free Lunch from Deep Learning in Neuroscience: A Case Study through Models of the Entorhinal-Hippocampal Circuit.R. Schaeffer, M. Khona, and I. Fiete. NeurIPS, (2022)Streaming Inference for Infinite Feature Models.R. Schaeffer, Y. Du, G. Liu, and I. Fiete. ICML, volume 162 of Proceedings of Machine Learning Research, page 19366-19387. PMLR, (2022)Is Model Collapse Inevitable? Breaking the Curse of Recursion by Accumulating Real and Synthetic Data.M. Gerstgrasser, R. Schaeffer, A. Dey, R. Rafailov, H. Sleight, J. Hughes, T. Korbak, R. Agrawal, D. Pai, A. Gromov and 4 other author(s). CoRR, (2024)In-Context Learning of Energy Functions.R. Schaeffer, M. Khona, and S. Koyejo. CoRR, (2024)

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Disambiguation of "Schaeffer, Rylan"

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Is Model Collapse Inevitable? Breaking the Curse of Recursion by Accumulating Real and Synthetic Data.

Please choose a person to relate this publication to

Uli Schaeffer

Annegrete Schaeffer

Harry Schaeffer

Helmut Schaeffer

Michael Schaeffer

Other publications of authors with the same name

Disambiguation

BibSonomy

Disambiguation of "Schaeffer, Rylan"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Is Model Collapse Inevitable? Breaking the Curse of Recursion by Accumulating Real and Synthetic Data.

Please choose a person to relate this publication to

Uli Schaeffer

Annegrete Schaeffer

Harry Schaeffer

Helmut Schaeffer

Michael Schaeffer

Other publications of authors with the same name

Disambiguation

copy delete add this publication to your clipboard
community post
history of this post
URL
DOI
BibTeX
EndNote
APA
Chicago
DIN 1505
Harvard
MSOffice XML

Is Model Collapse Inevitable? Breaking the Curse of Recursion by Accumulating Real and Synthetic Data.