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Convolutional neural networks automate detection for tracking of submicron-scale particles in 2D and 3D
Convolutional neural networks automate detection for tracking of submicron-scale particles in 2D and 3D
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Convolutional neural networks automate detection for tracking of submicron-scale particles in 2D and 3D

Newby, Jay M. ; Schaefer, Alison M. ; Lee, Phoebe T. ; Forest, M. Gregory ; Lai, Samuel K.

Proceedings of the National Academy of Sciences - PNAS, 2018-09, Vol.115 (36), p.9026-9031 [Periódico revisado por pares]

United States: National Academy of Sciences

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Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning
Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning
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Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning

Norouzzadeh, Mohammad Sadegh ; Nguyen, Anh ; Kosmala, Margaret ; Swanson, Alexandra ; Palmer, Meredith S. ; Packer, Craig ; Clune, Jeff

Proceedings of the National Academy of Sciences - PNAS, 2018-06, Vol.115 (25), p.E5716-E5725 [Periódico revisado por pares]

United States: National Academy of Sciences

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3
Deep learning predicts path-dependent plasticity
Deep learning predicts path-dependent plasticity
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Deep learning predicts path-dependent plasticity

Mozaffar, M. ; Bostanabad, R. ; Chen, W. ; Ehmann, K. ; Cao, J. ; Bessa, M. A.

Proceedings of the National Academy of Sciences - PNAS, 2019-12, Vol.116 (52), p.26414-26420 [Periódico revisado por pares]

United States: National Academy of Sciences

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4
Exploring deep neural networks via layer-peeled model: Minority collapse in imbalanced training
Exploring deep neural networks via layer-peeled model: Minority collapse in imbalanced training
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Exploring deep neural networks via layer-peeled model: Minority collapse in imbalanced training

Fang, Cong ; He, Hangfeng ; Long, Qi ; Su, Weijie J

Proceedings of the National Academy of Sciences - PNAS, 2021-10, Vol.118 (43) [Periódico revisado por pares]

United States: National Academy of Sciences

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5
Neural networks to learn protein sequence-function relationships from deep mutational scanning data
Neural networks to learn protein sequence-function relationships from deep mutational scanning data
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Neural networks to learn protein sequence-function relationships from deep mutational scanning data

Gelman, Sam ; Fahlberg, Sarah A ; Heinzelman, Pete ; Romero, Philip A ; Gitter, Anthony

Proceedings of the National Academy of Sciences - PNAS, 2021-11, Vol.118 (48) [Periódico revisado por pares]

United States: National Academy of Sciences

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6
Surrogate gradients for analog neuromorphic computing
Surrogate gradients for analog neuromorphic computing
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Surrogate gradients for analog neuromorphic computing

Cramer, Benjamin ; Billaudelle, Sebastian ; Kanya, Simeon ; Leibfried, Aron ; Grübl, Andreas ; Karasenko, Vitali ; Pehle, Christian ; Schreiber, Korbinian ; Stradmann, Yannik ; Weis, Johannes ; Schemmel, Johannes ; Zenke, Friedemann

Proceedings of the National Academy of Sciences - PNAS, 2022-01, Vol.119 (4), p.1 [Periódico revisado por pares]

United States: National Academy of Sciences

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7
Simple framework for constructing functional spiking recurrent neural networks
Simple framework for constructing functional spiking recurrent neural networks
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Simple framework for constructing functional spiking recurrent neural networks

Kim, Robert ; Li, Yinghao ; Sejnowski, Terrence J.

Proceedings of the National Academy of Sciences - PNAS, 2019-11, Vol.116 (45), p.22811-22820 [Periódico revisado por pares]

United States: National Academy of Sciences

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8
Recurrence is required to capture the representational dynamics of the human visual system
Recurrence is required to capture the representational dynamics of the human visual system
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Recurrence is required to capture the representational dynamics of the human visual system

Kietzmann, Tim C. ; Spoerer, Courtney J. ; Sörensen, Lynn K. A. ; Cichy, Radoslaw M. ; Hauk, Olaf ; Kriegeskorte, Nikolaus

Proceedings of the National Academy of Sciences - PNAS, 2019-10, Vol.116 (43), p.21854-21863 [Periódico revisado por pares]

United States: National Academy of Sciences

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9
A mixed-scale dense convolutional neural network for image analysis
A mixed-scale dense convolutional neural network for image analysis
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A mixed-scale dense convolutional neural network for image analysis

Pelt, Daniël M. ; Sethian, James A.

Proceedings of the National Academy of Sciences - PNAS, 2018-01, Vol.115 (2), p.254-259 [Periódico revisado por pares]

United States: National Academy of Sciences

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10
Data-driven discovery of coordinates and governing equations
Data-driven discovery of coordinates and governing equations
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Data-driven discovery of coordinates and governing equations

Champion, Kathleen ; Lusch, Bethany ; Kutz, J. Nathan ; Brunton, Steven L.

Proceedings of the National Academy of Sciences - PNAS, 2019-11, Vol.116 (45), p.22445-22451 [Periódico revisado por pares]

United States: National Academy of Sciences

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