Early Stopping in Deep Networks: Double Descent and How to Eliminate it

20 July 2020

Papers citing "Early Stopping in Deep Networks: Double Descent and How to Eliminate it"

13 / 13 papers shown

Title
A dynamic view of the double descent Vivek Shripad Borkar 58 0 0 03 May 2025
On the Relationship Between Double Descent of CNNs and Shape/Texture Bias Under Learning Process Shun Iwase Shuya Takahashi Nakamasa Inoue Rio Yokota Ryo Nakamura Hirokatsu Kataoka 74 0 0 04 Mar 2025
Gibbs-Based Information Criteria and the Over-Parameterized Regime Haobo Chen Yuheng Bu Greg Wornell 21 1 0 08 Jun 2023
Double Descent of Discrepancy: A Task-, Data-, and Model-Agnostic Phenomenon Yi-Xiao Luo Bin Dong 26 0 0 25 May 2023
Unifying Grokking and Double Descent Peter W. Battaglia David Raposo Kelsey 34 31 0 10 Mar 2023
On the Impossible Safety of Large AI Models El-Mahdi El-Mhamdi Sadegh Farhadkhani R. Guerraoui Nirupam Gupta L. Hoang Rafael Pinot Sébastien Rouault John Stephan 30 31 0 30 Sep 2022
Information FOMO: The unhealthy fear of missing out on information. A method for removing misleading data for healthier models Ethan Pickering T. Sapsis 16 6 0 27 Aug 2022
Image Augmentation for Satellite Images Oluwadara Adedeji Peter Owoade Op Ajayi Olayiwola F Arowolo 8 9 0 29 Jul 2022
Regularization-wise double descent: Why it occurs and how to eliminate it Fatih Yilmaz Reinhard Heckel 25 11 0 03 Jun 2022
Data Determines Distributional Robustness in Contrastive Language Image Pre-training (CLIP) Alex Fang Gabriel Ilharco Mitchell Wortsman Yu Wan Vaishaal Shankar Achal Dave Ludwig Schmidt VLM OOD 31 138 0 03 May 2022
Provable Continual Learning via Sketched Jacobian Approximations Reinhard Heckel CLL 18 9 0 09 Dec 2021
Multi-scale Feature Learning Dynamics: Insights for Double Descent Mohammad Pezeshki Amartya Mitra Yoshua Bengio Guillaume Lajoie 61 25 0 06 Dec 2021
Double Trouble in Double Descent : Bias and Variance(s) in the Lazy Regime Stéphane dÁscoli Maria Refinetti Giulio Biroli Florent Krzakala 93 152 0 02 Mar 2020