12 citations · 12 across the 3 of their papers we have counts for
3 papers
cs.LG2024
Universality of the Pathway in Avoiding Model Collapse
Apratim Dey, David Donoho
Researchers in empirical machine learning recently spotlighted their fears of so-called Model Collapse. They imagined a discard workflow, where an initial generative model is train…
cs.LG2024★ 12 cited
Is Model Collapse Inevitable? Breaking the Curse of Recursion by Accumulating Real and Synthetic Data
Matthias Gerstgrasser, Rylan Schaeffer, Apratim Dey +11
The proliferation of generative models, combined with pretraining on web-scale data, raises a timely question: what happens when these models are trained on their own generated out…
astro-ph.CO2024
Inferring the redshift of more than 150 GRBs with a Machine Learning Ensemble model
Maria Giovanna Dainotti, Elias Taira, Eric Wang +8
Gamma-Ray Bursts (GRBs), due to their high luminosities are detected up to redshift 10, and thus have the potential to be vital cosmological probes of early processes in the univer…