On Computability, Learnability and Extractability of Finite State Machines from Recurrent Neural Networks

10 September 2020

Papers citing "On Computability, Learnability and Extractability of Finite State Machines from Recurrent Neural Networks"

10 / 10 papers shown

Title
Distance and Equivalence between Finite State Machines and Recurrent Neural Networks: Computational results Reda Marzouk C. D. L. Higuera 34 7 0 01 Apr 2020
Learning Finite State Representations of Recurrent Policy Networks Anurag Koul S. Greydanus Alan Fern 41 88 0 29 Nov 2018
Implicit Regularization of Stochastic Gradient Descent in Natural Language Processing: Observations and Implications Deren Lei Zichen Sun Yijun Xiao William Yang Wang 119 14 0 01 Nov 2018
Stronger generalization bounds for deep nets via a compression approach Sanjeev Arora Rong Ge Behnam Neyshabur Yi Zhang MLT AI4CE 84 640 0 14 Feb 2018
Recurrent Neural Networks as Weighted Language Recognizers Yining Chen Sorcha Gilroy A. Maletti Jonathan May Kevin Knight 51 77 0 15 Nov 2017
Interpretable Convolutional Neural Networks Quanshi Zhang Ying Nian Wu Song-Chun Zhu FAtt 64 780 0 02 Oct 2017
Implicit Regularization in Deep Learning Behnam Neyshabur 50 146 0 06 Sep 2017
Linguistic Knowledge as Memory for Recurrent Neural Networks Bhuwan Dhingra Zhilin Yang William W. Cohen Ruslan Salakhutdinov RALM 76 37 0 07 Mar 2017
Visualizing and Understanding Recurrent Networks A. Karpathy Justin Johnson Li Fei-Fei HAI 111 1,101 0 05 Jun 2015
Visualizing and Understanding Convolutional Networks Matthew D. Zeiler Rob Fergus FAtt SSL 563 15,874 0 12 Nov 2013