Conformal Symplectic and Relativistic Optimization

Conformal Symplectic and Relativistic Optimization

11 March 2019

Daniel P. Robinson

Papers citing "Conformal Symplectic and Relativistic Optimization"

18 / 18 papers shown

Title
Accelerated First-Order Optimization under Nonlinear Constraints Michael Muehlebach Michael I. Jordan 78 3 0 01 Feb 2023
Generalisation under gradient descent via deterministic PAC-Bayes Eugenio Clerico Tyler Farghly George Deligiannidis Benjamin Guedj Arnaud Doucet 54 4 0 06 Sep 2022
On dissipative symplectic integration with applications to gradient-based optimization G. Francca Michael I. Jordan René Vidal 98 47 0 15 Apr 2020
Optimization with Momentum: Dynamical, Control-Theoretic, and Symplectic Perspectives Michael Muehlebach Michael I. Jordan 30 64 0 28 Feb 2020
Understanding the Acceleration Phenomenon via High-Resolution Differential Equations Bin Shi S. Du Michael I. Jordan Weijie J. Su 42 256 0 21 Oct 2018
Direct Runge-Kutta Discretization Achieves Acceleration J.N. Zhang Aryan Mokhtari S. Sra Ali Jadbabaie 93 108 0 01 May 2018
On Symplectic Optimization M. Betancourt Michael I. Jordan Ashia Wilson 111 94 0 10 Feb 2018
Kinetic energy choice in Hamiltonian/hybrid Monte Carlo Samuel Livingstone Michael F Faulkner Gareth O. Roberts 43 45 0 08 Jun 2017
The Marginal Value of Adaptive Gradient Methods in Machine Learning Ashia Wilson Rebecca Roelofs Mitchell Stern Nathan Srebro Benjamin Recht ODL 50 1,023 0 23 May 2017
The Physical Systems Behind Optimization Algorithms Lin F. Yang R. Arora Vladimir Braverman T. Zhao AI4CE 30 19 0 08 Dec 2016
Relativistic Monte Carlo Xiaoyu Lu Valerio Perrone Leonard Hasenclever Yee Whye Teh Sebastian J. Vollmer BDL 34 39 0 14 Sep 2016
A Variational Perspective on Accelerated Methods in Optimization Andre Wibisono Ashia Wilson Michael I. Jordan 80 572 0 14 Mar 2016
Deep Residual Learning for Image Recognition Kaiming He Xinming Zhang Shaoqing Ren Jian Sun MedIm 1.4K 192,638 0 10 Dec 2015
A Differential Equation for Modeling Nesterov's Accelerated Gradient Method: Theory and Insights Weijie Su Stephen P. Boyd Emmanuel J. Candes 149 1,161 0 04 Mar 2015
Adam: A Method for Stochastic Optimization Diederik P. Kingma Jimmy Ba ODL 844 149,474 0 22 Dec 2014
Very Deep Convolutional Networks for Large-Scale Image Recognition Karen Simonyan Andrew Zisserman FAtt MDE 943 99,991 0 04 Sep 2014
A Literature Survey of Benchmark Functions For Global Optimization Problems Momin Jamil Xin-She Yang 74 1,275 0 19 Aug 2013
Matrix Completion from a Few Entries Raghunandan H. Keshavan Andrea Montanari Sewoong Oh 265 1,242 0 20 Jan 2009