Continuous vs. Discrete Optimization of Deep Neural Networks

14 July 2021

Papers citing "Continuous vs. Discrete Optimization of Deep Neural Networks"

8 / 8 papers shown

Title
The Expected Loss of Preconditioned Langevin Dynamics Reveals the Hessian Rank Amitay Bar Rotem Mulayoff T. Michaeli Ronen Talmon 38 0 0 21 Feb 2024
Gradient Descent Monotonically Decreases the Sharpness of Gradient Flow Solutions in Scalar Networks and Beyond Itai Kreisler Mor Shpigel Nacson Daniel Soudry Y. Carmon 21 13 0 22 May 2023
On a continuous time model of gradient descent dynamics and instability in deep learning Mihaela Rosca Yan Wu Chongli Qin Benoit Dherin 13 6 0 03 Feb 2023
Symmetries, flat minima, and the conserved quantities of gradient flow Bo-Lu Zhao I. Ganev Robin G. Walters Rose Yu Nima Dehmamy 34 16 0 31 Oct 2022
On the Effective Number of Linear Regions in Shallow Univariate ReLU Networks: Convergence Guarantees and Implicit Bias Itay Safran Gal Vardi Jason D. Lee MLT 37 23 0 18 May 2022
Neural Mechanics: Symmetry and Broken Conservation Laws in Deep Learning Dynamics D. Kunin Javier Sagastuy-Breña Surya Ganguli Daniel L. K. Yamins Hidenori Tanaka 97 77 0 08 Dec 2020
The large learning rate phase of deep learning: the catapult mechanism Aitor Lewkowycz Yasaman Bahri Ethan Dyer Jascha Narain Sohl-Dickstein Guy Gur-Ari ODL 150 198 0 04 Mar 2020
A Differential Equation for Modeling Nesterov's Accelerated Gradient Method: Theory and Insights Weijie Su Stephen P. Boyd Emmanuel J. Candes 97 1,150 0 04 Mar 2015