Convex Programming for Estimation in Nonlinear Recurrent Models

26 August 2019

Papers citing "Convex Programming for Estimation in Nonlinear Recurrent Models"

10 / 10 papers shown

Title
Can SGD Learn Recurrent Neural Networks with Provable Generalization? Zeyuan Allen-Zhu Yuanzhi Li MLT LRM 169 57 0 04 Feb 2019
Stochastic Gradient Descent Learns State Equations with Nonlinear Activations Samet Oymak 61 43 0 09 Sep 2018
Non-asymptotic Identification of LTI Systems from a Single Trajectory Samet Oymak N. Ozay 67 226 0 14 Jun 2018
Stable Recurrent Models John Miller Moritz Hardt 63 119 0 25 May 2018
How Many Samples are Needed to Estimate a Convolutional or Recurrent Neural Network? S. Du Yining Wang Xiyu Zhai Sivaraman Balakrishnan Ruslan Salakhutdinov Aarti Singh SSL 84 57 0 21 May 2018
Learning Without Mixing: Towards A Sharp Analysis of Linear System Identification Max Simchowitz Horia Mania Stephen Tu Michael I. Jordan Benjamin Recht 68 342 0 22 Feb 2018
Finite Time Identification in Unstable Linear Systems Mohamad Kazem Shirani Faradonbeh Ambuj Tewari George Michailidis 66 147 0 05 Oct 2017
Gradient Descent Learns Linear Dynamical Systems Moritz Hardt Tengyu Ma Benjamin Recht 117 240 0 16 Sep 2016
SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives Aaron Defazio Francis R. Bach Simon Lacoste-Julien ODL 137 1,828 0 01 Jul 2014
Minimizing Finite Sums with the Stochastic Average Gradient Mark Schmidt Nicolas Le Roux Francis R. Bach 329 1,250 0 10 Sep 2013