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Reverse engineering recurrent neural networks with Jacobian switching linear dynamical systems

Nov 01, 2021
Jimmy T. H. Smith, Scott W. Linderman, David Sussillo

* 23 pages, 9 figures 

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Reverse engineering learned optimizers reveals known and novel mechanisms

Nov 04, 2020
Niru Maheswaranathan, David Sussillo, Luke Metz, Ruoxi Sun, Jascha Sohl-Dickstein

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The geometry of integration in text classification RNNs

Oct 28, 2020
Kyle Aitken, Vinay V. Ramasesh, Ankush Garg, Yuan Cao, David Sussillo, Niru Maheswaranathan

* 9+19 pages, 30 figures 

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How recurrent networks implement contextual processing in sentiment analysis

Apr 17, 2020
Niru Maheswaranathan, David Sussillo

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Universality and individuality in neural dynamics across large populations of recurrent networks

Jul 19, 2019
Niru Maheswaranathan, Alex H. Williams, Matthew D. Golub, Surya Ganguli, David Sussillo

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Reverse engineering recurrent networks for sentiment classification reveals line attractor dynamics

Jun 25, 2019
Niru Maheswaranathan, Alex Williams, Matthew D. Golub, Surya Ganguli, David Sussillo

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Task-Driven Convolutional Recurrent Models of the Visual System

Oct 27, 2018
Aran Nayebi, Daniel Bear, Jonas Kubilius, Kohitij Kar, Surya Ganguli, David Sussillo, James J. DiCarlo, Daniel L. K. Yamins

* NIPS 2018 Camera Ready Version, 16 pages including supplementary information, 6 figures 

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A Dataset and Architecture for Visual Reasoning with a Working Memory

Jul 20, 2018
Guangyu Robert Yang, Igor Ganichev, Xiao-Jing Wang, Jonathon Shlens, David Sussillo

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Recurrent Segmentation for Variable Computational Budgets

Mar 15, 2018
Lane McIntosh, Niru Maheswaranathan, David Sussillo, Jonathon Shlens

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Input Switched Affine Networks: An RNN Architecture Designed for Interpretability

Jun 12, 2017
Jakob N. Foerster, Justin Gilmer, Jan Chorowski, Jascha Sohl-Dickstein, David Sussillo

* ICLR 2107 submission: 

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Capacity and Trainability in Recurrent Neural Networks

Mar 03, 2017
Jasmine Collins, Jascha Sohl-Dickstein, David Sussillo

* Published as a conference paper at ICLR 2017 

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Making brain-machine interfaces robust to future neural variability

Oct 19, 2016
David Sussillo, Sergey D. Stavisky, Jonathan C. Kao, Stephen I. Ryu, Krishna V. Shenoy

* Nature Communications. 7:13749 (2016) 
* D.S., S.D.S., and J.C.K. contributed equally to this work 

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LFADS - Latent Factor Analysis via Dynamical Systems

Aug 22, 2016
David Sussillo, Rafal Jozefowicz, L. F. Abbott, Chethan Pandarinath

* 16 pages, 11 figures 

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A Neural Transducer

Aug 04, 2016
Navdeep Jaitly, David Sussillo, Quoc V. Le, Oriol Vinyals, Ilya Sutskever, Samy Bengio

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Random Walk Initialization for Training Very Deep Feedforward Networks

Feb 27, 2015
David Sussillo, L. F. Abbott

* 10 pages, 4 figures 

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