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Input Switched Affine Networks: An RNN Architecture Designed for
  Interpretability
v1v2 (latest)

Input Switched Affine Networks: An RNN Architecture Designed for Interpretability

28 November 2016
Jakob N. Foerster
Justin Gilmer
J. Chorowski
Jascha Narain Sohl-Dickstein
David Sussillo
ArXiv (abs)PDFHTML

Papers citing "Input Switched Affine Networks: An RNN Architecture Designed for Interpretability"

6 / 6 papers shown
RNN Architecture Learning with Sparse Regularization
RNN Architecture Learning with Sparse RegularizationConference on Empirical Methods in Natural Language Processing (EMNLP), 2019
Jesse Dodge
Roy Schwartz
Hao Peng
Noah A. Smith
218
11
0
06 Sep 2019
Language Modeling for Code-Switching: Evaluation, Integration of
  Monolingual Data, and Discriminative Training
Language Modeling for Code-Switching: Evaluation, Integration of Monolingual Data, and Discriminative Training
Hila Gonen
Yoav Goldberg
234
34
0
28 Oct 2018
Rational Recurrences
Rational Recurrences
Hao Peng
Roy Schwartz
Sam Thomson
Noah A. Smith
AI4CE
184
39
0
28 Aug 2018
Comparison of Decoding Strategies for CTC Acoustic Models
Comparison of Decoding Strategies for CTC Acoustic ModelsInterspeech (Interspeech), 2017
Thomas Zenkel
Ramon Sanabria
Florian Metze
Jan Niehues
Matthias Sperber
Sebastian Stüker
A. Waibel
118
44
0
15 Aug 2017
Gated Orthogonal Recurrent Units: On Learning to Forget
Gated Orthogonal Recurrent Units: On Learning to ForgetNeural Computation (Neural Comput.), 2017
Li Jing
Çağlar Gülçehre
J. Peurifoy
Yichen Shen
Max Tegmark
Marin Soljacic
Yoshua Bengio
252
133
0
08 Jun 2017
Capacity and Trainability in Recurrent Neural Networks
Capacity and Trainability in Recurrent Neural Networks
Jasmine Collins
Jascha Narain Sohl-Dickstein
David Sussillo
377
213
0
29 Nov 2016
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