Aran Nayebi

发表

Deep Learning Models of the Retinal Response to Natural Scenes

Surya Ganguli, Aran Nayebi, Niru Maheswaranathan, 2017, NIPS.

CARMA : A Deep Reinforcement Learning Approach to Autonomous Driving

Aran Nayebi, Matt Vitelli, 2016 .

Robust and scalable credit assignment without weight symmetry

Surya Ganguli, Daniel Kunin, Javier Sagastuy-Brena, 2020, International Conference on Machine Learning.

ON THE RIEMANN HYPOTHESIS AND HILBERT’S TENTH PROBLEM

Aran Nayebi, 2018 .

Unsupervised neural network models of the ventral visual stream

Michael C. Frank, Martin Schrimpf, Chengxu Zhuang, 2020, Proceedings of the National Academy of Sciences.

Upper bounds on the solutions to $n = p+m^2$

Aran Nayebi, 2010, 1004.0536.

Exponential prefixed polynomial equations

Aran Nayebi, 2013, 1303.5777.

Mouse visual cortex as a limited resource system that self-learns an ecologically-general representation

Daniel L. K. Yamins, A. Norcia, J. Gardner, 2022, bioRxiv.

Plausible hypercomputability pdf

Aran Nayebi, Aran Nayebi, 2012, ArXiv.

On the distribution of Carmichael numbers

Aran Nayebi, 2009, 0906.3533.

Biologically inspired protection of deep networks from adversarial attacks pdf

Surya Ganguli, Aran Nayebi, S. Ganguli, 2017, ArXiv.

Shallow Unsupervised Models Best Predict Neural Responses in Mouse Visual Cortex

Daniel L. K. Yamins, A. Norcia, J. Gardner, 2021 .

Recurrent Connections in the Primate Ventral Visual Stream Mediate a Tradeoff Between Task Performance and Network Size During Core Object Recognition

Daniel L. K. Yamins, J. DiCarlo, David Sussillo, 2022, bioRxiv.

CORnet: Modeling the Neural Mechanisms of Core Object Recognition

Aran Nayebi, Jonas Kubilius, Martin Schrimpf, 2018, bioRxiv.

Goal-Driven Recurrent Neural Network Models of the Ventral Visual Stream

Daniel L. K. Yamins, J. DiCarlo, David Sussillo, 2021 .

When and why grid cells appear or not in trained path integrators

Daniel L. K. Yamins, Gabriel C. Mel, S. Ganguli, 2022, bioRxiv.

Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNs

Elias B. Issa, Daniel L. K. Yamins, J. DiCarlo, 2019, NeurIPS.

Recurrent Connections in the Primate Ventral Visual Stream Mediate a Tradeoff Between Task Performance and Network Size During Core Object Recognition

Daniel L. K. Yamins, J. DiCarlo, David Sussillo, 2021, bioRxiv.

Explaining heterogeneity in medial entorhinal cortex with task-driven neural networks

Alex H. Williams, Isabel I. C. Low, Gabriel C. Mel, 2021, bioRxiv.

Mouse visual cortex as a limited resource system that self-learns an ecologically-general representation

Daniel L. K. Yamins, A. Norcia, J. Gardner, 2023, bioRxiv.

Task-Driven Convolutional Recurrent Models of the Visual System

Surya Ganguli, Aran Nayebi, Jonas Kubilius, 2018, NeurIPS.

Practical Intractability: A Critique of the Hypercomputation Movement

Aran Nayebi, Aran Nayebi, 2013, Minds and Machines.

From deep learning to mechanistic understanding in neuroscience: the structure of retinal prediction

Surya Ganguli, Aran Nayebi, Hidenori Tanaka, 2019, NeurIPS.

Distinct in vivo dynamics of excitatory synapses onto cortical pyramidal neurons and parvalbumin-positive interneurons

Tianyi Mao, S. Ganguli, Bart C. Jongbloets, 2021, Cell reports.

Distinct in vivo dynamics of excitatory synapses onto cortical pyramidal neurons and inhibitory interneurons

Tianyi Mao, S. Ganguli, Bart C. Jongbloets, 2021, bioRxiv.

Deep learning models reveal internal structure and diverse computations in the retina under natural scenes

Surya Ganguli, Aran Nayebi, Niru Maheswaranathan, 2018, bioRxiv.

The dynamic neural code of the retina for natural scenes

Surya Ganguli, Aran Nayebi, Hidenori Tanaka, 2019 .

Two Routes to Scalable Credit Assignment without Weight Symmetry

Daniel L. K. Yamins, S. Ganguli, Aran Nayebi, 2020, ICML.

Neural Foundations of Mental Simulation: Future Prediction of Latent Representations on Dynamic Scenes

M. Jazayeri, G. R. Yang, Aran Nayebi, 2023, ArXiv.

Quantum lower bound for inverting a permutation with advice pdf

Aran Nayebi, Luca Trevisan, Scott Aaronson, 2014, Quantum Inf. Comput..

Unsupervised Models of Mouse Visual Cortex

Justin L. Gardner, A. Norcia, Daniel Yamins, 2021 .

Self-supervised Neural Network Models of Higher Visual Cortex Development

Aran Nayebi, Chengxu Zhuang, Siming Yan, 2019, 2019 Conference on Cognitive Computational Neuroscience.

Learning Physical Graph Representations from Visual Scenes pdf

Li Fei-Fei, Jiajun Wu, Joshua B. Tenenbaum, 2020, NeurIPS.

GRUV: Algorithmic Music Generation using Recurrent Neural Networks

Aran Nayebi, Matt Vitelli, 2015 .

On the Distributions of Pseudoprimes, Carmichael Numbers, and Strong Pseudoprimes

Aran Nayebi, 2009 .

CORnet: Modeling the Neural Mechanisms of Core Object Recognition

J. DiCarlo, Daniel Yamins, Aran Nayebi, 2018, bioRxiv.

From deep learning to mechanistic understanding in neuroscience: the structure of retinal prediction

Lane T. McIntosh, S. Ganguli, S. Baccus, 2019, NeurIPS.

Identifying Learning Rules From Neural Network Observables

Daniel L. K. Yamins, S. Ganguli, Aran Nayebi, 2020, NeurIPS.

Mouse visual cortex as a limited resource system that self-learns an ecologically-general representation

Justin L. Gardner, Daniel L. K. Yamins, A. Norcia, 2021, bioRxiv.

Interpreting the retinal neural code for natural scenes: From computations to neurons

Lane T. McIntosh, S. Ganguli, S. Baccus, 2023, Neuron.

The dynamic neural code of the retina for natural scenes

Lane T. McIntosh, S. Ganguli, S. Baccus, 2019 .

The dynamic neural code of the retina for natural scenes

Lane T. McIntosh, S. Ganguli, S. Baccus, 2019 .

The dynamic neural code of the retina for natural scenes

Lane T. McIntosh, S. Ganguli, S. Baccus, 2019 .

The dynamic neural code of the retina for natural scenes

Lane T. McIntosh, S. Ganguli, S. Baccus, 2019 .

Revealing computational mechanisms of retinal prediction via model reduction

Surya Ganguli, Aran Nayebi, Stephen A. Baccus, 2019 .