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David Sussillo
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2020 – today
- 2024
- [c14]Dailyn Despradel Despradel
, Max Murphy
Enabling Advanced Interactions through Closed-loop Control of Motor Unit Activity After Tetraplegia. UIST (Adjunct Volume) 2024: 107:1-107:3 - 2023
- [j6]Connor Brennan
One dimensional approximations of neuronal dynamics reveal computational strategy. PLoS Comput. Biol. 19(1) (2023) - [i15]Jordan Cotler, Kai Sheng Tai, Felipe Hernández, Blake Elias, David Sussillo:
Analyzing Populations of Neural Networks via Dynamical Model Embedding. CoRR abs/2302.14078 (2023) - 2022
- [j5]Aran Nayebi, Javier Sagastuy-Breña, Daniel M. Bear, Kohitij Kar, Jonas Kubilius, Surya Ganguli, David Sussillo, James J. DiCarlo, Daniel L. K. Yamins:
Recurrent Connections in the Primate Ventral Visual Stream Mediate a Trade-Off Between Task Performance and Network Size During Core Object Recognition. Neural Comput. 34(8): 1652-1675 (2022) - [i14]Anthony Zador, Blake A. Richards, Bence Ölveczky, Sean Escola, Yoshua Bengio, Kwabena Boahen, Matthew M. Botvinick, Dmitri B. Chklovskii, Anne Churchland, Claudia Clopath
Toward Next-Generation Artificial Intelligence: Catalyzing the NeuroAI Revolution. CoRR abs/2210.08340 (2022) - 2021
- [c13]Kyle Aitken, Vinay Venkatesh Ramasesh, Ankush Garg, Yuan Cao, David Sussillo, Niru Maheswaranathan:
The geometry of integration in text classification RNNs. ICLR 2021 - [c12]Jimmy T. H. Smith, Scott W. Linderman, David Sussillo:
Reverse engineering recurrent neural networks with Jacobian switching linear dynamical systems. NeurIPS 2021: 16700-16713 - [c11]Niru Maheswaranathan, David Sussillo, Luke Metz, Ruoxi Sun, Jascha Sohl-Dickstein:
Reverse engineering learned optimizers reveals known and novel mechanisms. NeurIPS 2021: 19910-19922 - [i13]Jimmy T. H. Smith, Scott W. Linderman, David Sussillo:
Reverse engineering recurrent neural networks with Jacobian switching linear dynamical systems. CoRR abs/2111.01256 (2021) - 2020
- [c10]Niru Maheswaranathan, David Sussillo:
How recurrent networks implement contextual processing in sentiment analysis. ICML 2020: 6608-6619 - [c9]Lea Duncker, Laura Driscoll, Krishna V. Shenoy, Maneesh Sahani, David Sussillo:
Organizing recurrent network dynamics by task-computation to enable continual learning. NeurIPS 2020 - [i12]Niru Maheswaranathan, David Sussillo:
How recurrent networks implement contextual processing in sentiment analysis. CoRR abs/2004.08013 (2020) - [i11]Kyle Aitken, Vinay V. Ramasesh, Ankush Garg, Yuan Cao, David Sussillo, Niru Maheswaranathan:
The geometry of integration in text classification RNNs. CoRR abs/2010.15114 (2020) - [i10]Niru Maheswaranathan, David Sussillo, Luke Metz, Ruoxi Sun, Jascha Sohl-Dickstein:
Reverse engineering learned optimizers reveals known and novel mechanisms. CoRR abs/2011.02159 (2020)
2010 – 2019
- 2019
- [c8]Niru Maheswaranathan, Alex H. Williams, Matthew D. Golub, Surya Ganguli, David Sussillo:
Universality and individuality in neural dynamics across large populations of recurrent networks. NeurIPS 2019: 15603-15615 - [c7]Niru Maheswaranathan, Alex H. Williams, Matthew D. Golub, Surya Ganguli, David Sussillo:
Reverse engineering recurrent networks for sentiment classification reveals line attractor dynamics. NeurIPS 2019: 15670-15679 - [i9]Niru Maheswaranathan, Alex H. Williams, Matthew D. Golub, Surya Ganguli, David Sussillo:
Reverse engineering recurrent networks for sentiment classification reveals line attractor dynamics. CoRR abs/1906.10720 (2019) - [i8]Niru Maheswaranathan, Alex H. Williams, Matthew D. Golub, Surya Ganguli, David Sussillo:
Universality and individuality in neural dynamics across large populations of recurrent networks. CoRR abs/1907.08549 (2019) - 2018
- [j4]Matthew D. Golub
FixedPointFinder: A Tensorflow toolbox for identifying and characterizing fixed points in recurrent neural networks. J. Open Source Softw. 3(31): 1003 (2018) - [c6]Lane McIntosh, Niru Maheswaranathan, David Sussillo, Jonathon Shlens:
Recurrent Segmentation for Variable Computational Budgets. CVPR Workshops 2018: 1648-1657 - [c5]Guangyu Robert Yang, Igor Ganichev, Xiao-Jing Wang, Jonathon Shlens, David Sussillo:
A Dataset and Architecture for Visual Reasoning with a Working Memory. ECCV (10) 2018: 729-745 - [c4]Aran Nayebi, Daniel Bear, Jonas Kubilius, Kohitij Kar, Surya Ganguli, David Sussillo, James J. DiCarlo, Daniel L. K. Yamins:
Task-Driven Convolutional Recurrent Models of the Visual System. NeurIPS 2018: 5295-5306 - [i7]Guangyu Robert Yang, Igor Ganichev, Xiao-Jing Wang, Jonathon Shlens, David Sussillo:
A dataset and architecture for visual reasoning with a working memory. CoRR abs/1803.06092 (2018) - [i6]Aran Nayebi, Daniel Bear, Jonas Kubilius, Kohitij Kar, Surya Ganguli, David Sussillo, James J. DiCarlo, Daniel L. K. Yamins:
Task-Driven Convolutional Recurrent Models of the Visual System. CoRR abs/1807.00053 (2018) - 2017
- [c3]Jasmine Collins, Jascha Sohl-Dickstein, David Sussillo:
Capacity and Trainability in Recurrent Neural Networks. ICLR (Poster) 2017 - [c2]Jakob N. Foerster, Justin Gilmer, Jascha Sohl-Dickstein, Jan Chorowski
Input Switched Affine Networks: An RNN Architecture Designed for Interpretability. ICML 2017: 1136-1145 - [i5]Lane McIntosh, David Sussillo, Niru Maheswaranathan, Jonathon Shlens:
Recurrent Segmentation for Variable Computational Budgets. CoRR abs/1711.10151 (2017) - 2016
- [c1]Navdeep Jaitly, Quoc V. Le, Oriol Vinyals, Ilya Sutskever, David Sussillo, Samy Bengio:
An Online Sequence-to-Sequence Model Using Partial Conditioning. NIPS 2016: 5067-5075 - [i4]David Sussillo, Rafal Józefowicz, L. F. Abbott, Chethan Pandarinath
LFADS - Latent Factor Analysis via Dynamical Systems. CoRR abs/1608.06315 (2016) - [i3]Jakob N. Foerster, Justin Gilmer, Jan Chorowski, Jascha Sohl-Dickstein, David Sussillo:
Intelligible Language Modeling with Input Switched Affine Networks. CoRR abs/1611.09434 (2016) - [i2]Jasmine Collins, Jascha Sohl-Dickstein, David Sussillo:
Capacity and Trainability in Recurrent Neural Networks. CoRR abs/1611.09913 (2016) - 2014
- [j3]Jonathan C. Kao, Sergey D. Stavisky
Information Systems Opportunities in Brain-Machine Interface Decoders. Proc. IEEE 102(5): 666-682 (2014) - [i1]David Sussillo:
Random Walks: Training Very Deep Nonlinear Feed-Forward Networks with Smart Initialization. CoRR abs/1412.6558 (2014) - 2013
- [j2]David Sussillo
Opening the Black Box: Low-Dimensional Dynamics in High-Dimensional Recurrent Neural Networks. Neural Comput. 25(3): 626-649 (2013)
2000 – 2009
- 2004
- [j1]David Sussillo
Spectrogram Analysis of Genomes. EURASIP J. Adv. Signal Process. 2004(1): 29-42 (2004)
Coauthor Index
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last updated on 2024-11-04 20:46 CET by the dblp team
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