Model Collapse Demystified: The Case of Regression

12 February 2024

Papers citing "Model Collapse Demystified: The Case of Regression"

7 / 7 papers shown

Title
Spend Wisely: Maximizing Post-Training Gains in Iterative Synthetic Data Boostrapping Pu Yang Yunzhen Feng Ziyuan Chen Yuhang Wu Zhuoyuan Li DiffM 94 0 0 31 Jan 2025
Collapse or Thrive? Perils and Promises of Synthetic Data in a Self-Generating World Joshua Kazdan Rylan Schaeffer Apratim Dey Matthias Gerstgrasser Rafael Rafailov D. Donoho Sanmi Koyejo 45 11 0 22 Oct 2024
How Bad is Training on Synthetic Data? A Statistical Analysis of Language Model Collapse M. Seddik Suei-Wen Chen Soufiane Hayou Pierre Youssef Merouane Debbah 26 29 0 07 Apr 2024
Data Feedback Loops: Model-driven Amplification of Dataset Biases Rohan Taori Tatsunori B. Hashimoto 64 42 0 08 Sep 2022
Zero-Shot Text-to-Image Generation Aditya A. Ramesh Mikhail Pavlov Gabriel Goh Scott Gray Chelsea Voss Alec Radford Mark Chen Ilya Sutskever VLM 253 4,735 0 24 Feb 2021
Spectrum Dependent Learning Curves in Kernel Regression and Wide Neural Networks Blake Bordelon Abdulkadir Canatar C. Pehlevan 131 199 0 07 Feb 2020
Scaling Laws for Neural Language Models Jared Kaplan Sam McCandlish T. Henighan Tom B. Brown B. Chess R. Child Scott Gray Alec Radford Jeff Wu Dario Amodei 220 4,424 0 23 Jan 2020