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Many biological learning systems such as the mushroom body, hippocampus, and cerebellum are built from sparsely connected networks of neurons.
Randomness in neural networks: An overview
Simone Scardapane and Dianhui Wang · 1942
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Randomness in neural networks: An overview
Simone Scardapane and Dianhui Wang · 1942
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The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain
F. Rosenblatt · 1958
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The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain
F. Rosenblatt · 1958
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Additive Regression and Other Nonparametric Models
Charles J. Stone · 1985
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Additive Regression and Other Nonparametric Models
Charles J. Stone · 1985
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The Dimensionality Reduction Principle for Generalized Additive Models
Charles J. Stone · 1986
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The Dimensionality Reduction Principle for Generalized Additive Models
Charles J. Stone · 1986
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Radial Basis Functions, Multi-Variable Functional Interpolation and Adaptive Networks
D. S. Broomhead and David Lowe · 1988
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Radial Basis Functions, Multi-Variable Functional Interpolation and Adaptive Networks
D. S. Broomhead and David Lowe · 1988
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Spline Models for Observational Data
Grace Wahba · 1990
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Spline Models for Observational Data
Grace Wahba · 1990
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Stochastic choice of basis functions in adaptive function approximation and the functional-link net
B. Igelnik and Yoh-Han Pao · 1995
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Stochastic choice of basis functions in adaptive function approximation and the functional-link net
B. Igelnik and Yoh-Han Pao · 1995
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Priors for Infinite Networks
Radford M. Neal · 1996
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Priors for Infinite Networks
Radford M. Neal · 1996
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Computing with Infinite Networks
Christopher K. I. Williams · 1997
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Computing with Infinite Networks
Christopher K. I. Williams · 1997
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Regularization with Dot-Product Kernels
Alex J. Smola, Zoltán L. Óvári, and Robert C Williamson · 2001
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Regularization with Dot-Product Kernels
Alex J. Smola, Zoltán L. Óvári, and Robert C Williamson · 2001
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Learning Bounds for Kernel Regression Using Effective Data Dimensionality
Tong Zhang · 2005
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Learning Bounds for Kernel Regression Using Effective Data Dimensionality
Tong Zhang · 2005
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Comments on "The Extreme Learning Machine"
L. P. Wang and C. R. Wan · 2008
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Uniform approximation of functions with random bases
A. Rahimi and B. Recht · 2008
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Comments on "The Extreme Learning Machine"
L. P. Wang and C. R. Wan · 2008
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Uniform approximation of functions with random bases
A. Rahimi and B. Recht · 2008
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Exploring Large Feature Spaces with Hierarchical Multiple Kernel Learning
Francis R. Bach · 2009
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The Elements of Statistical Learning: Data Mining, Inference, and Prediction
Trevor Hastie, Robert Tibshirani, and Jerome Friedman · 2009
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Fast and Robust Learning by Reinforcement Signals: Explorations in the Insect Brain
Ramón Huerta and Thomas Nowotny · 2009
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Kernel Methods for Deep Learning
Youngmin Cho and Lawrence K. Saul · 2009
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Exploring Large Feature Spaces with Hierarchical Multiple Kernel Learning
Francis R. Bach · 2009
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The Elements of Statistical Learning: Data Mining, Inference, and Prediction
Trevor Hastie, Robert Tibshirani, and Jerome Friedman · 2009
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Fast and Robust Learning by Reinforcement Signals: Explorations in the Insect Brain
Ramón Huerta and Thomas Nowotny · 2009
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Kernel Methods for Deep Learning
Youngmin Cho and Lawrence K. Saul · 2009
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Additive Gaussian Processes
David K Duvenaud, Hannes Nickisch, and Carl E. Rasmussen · 2011
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Analysis and Extension of Arc-Cosine Kernels for Large Margin Classification
Youngmin Cho and Lawrence K. Saul · 2011
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Additive Gaussian Processes
David K Duvenaud, Hannes Nickisch, and Carl E. Rasmussen · 2011
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Analysis and Extension of Arc-Cosine Kernels for Large Margin Classification
Youngmin Cho and Lawrence K. Saul · 2011
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Compressed Sensing, Sparsity, and Dimensionality in Neuronal Information Processing and Data Analysis
Surya Ganguli and Haim Sompolinsky · 2012
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Improving neural networks by preventing co-adaptation of feature detectors
Geoffrey E. Hinton, Nitish Srivastava, Alex Krizhevsky, Ilya Sutskever, and Ruslan R. Salakhutdinov · 2012
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Compressed Sensing, Sparsity, and Dimensionality in Neuronal Information Processing and Data Analysis
Surya Ganguli and Haim Sompolinsky · 2012
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Different roles for inhibition in the rhythm-generating respiratory network
Kameron Decker Harris, Tatiana Dashevskiy, Joshua Mendoza, Alfredo J. Garcia, Jan-Marino Ramirez, and Eric Shea-Brown · 2017
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Optimal Degrees of Synaptic Connectivity
Ashok Litwin-Kumar, Kameron Decker Harris, Richard Axel, Haim Sompolinsky, and L. F. Abbott · 2017
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A theory of multineuronal dimensionality, dynamics and measurement
Peiran Gao, Eric Trautmann, Byron M. Yu, Gopal Santhanam, Stephen Ryu, Krishna Shenoy, and Surya Ganguli · 2017
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Generalization Properties of Learning with Random Features
Alessandro Rudi and Lorenzo Rosasco · 2017
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Neural Tangent Kernel: Convergence and Generalization in Neural Networks
Arthur Jacot, Franck Gabriel, and Clément Hongler · 2018
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Improving neural networks by preventing co-adaptation of feature detectors
Geoffrey E. Hinton, Nitish Srivastava, Alex Krizhevsky, Ilya Sutskever, and Ruslan R. Salakhutdinov · 2012
Cited alongside, same era.
Random convergence of olfactory inputs in the Drosophila mushroom body
Sophie J. C. Caron, Vanessa Ruta, L. F. Abbott, and Richard Axel · 2013
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Brains Don’t Play Dice—or Do They?
Sophie J. C. Caron · 2013
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Improving Neural Networks with Dropout
Nitish Srivastava · 2013
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The importance of mixed selectivity in complex cognitive tasks
Mattia Rigotti, Omri Barak, Melissa R. Warden, Xiao-Jing Wang, Nathaniel D. Daw, Earl K. Miller, and Stefano Fusi · 2013
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Random convergence of olfactory inputs in the Drosophila mushroom body
Sophie J. C. Caron, Vanessa Ruta, L. F. Abbott, and Richard Axel · 2013
Cited alongside, same era.
Brains Don’t Play Dice—or Do They?
Sophie J. C. Caron · 2013
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Lenaic Chizat and Francis Bach · 2018
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A Mean Field View of the Landscape of Two-Layers Neural Networks
Song Mei, Andrea Montanari, and Phan-Minh Nguyen · 2018
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Trainability and Accuracy of Neural Networks: An Interacting Particle System Approach
Grant M. Rotskoff and Eric Vanden-Eijnden · 2018
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Spurious Valleys in Two-layer Neural Network Optimization Landscapes
Luca Venturi, Afonso S. Bandeira, and Joan Bruna · 2018
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Certified Robustness to Adversarial Examples with Differential Privacy
Mathias Lecuyer, Vaggelis Atlidakis, Roxana Geambasu, Daniel Hsu, and Suman Jana · 2018
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Insect cyborgs: Bio-mimetic feature generators improve machine learning accuracy on limited data
Charles B. Delahunt and J. Nathan Kutz · 2018
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Linking Connectivity, Dynamics, and Computations in Low-Rank Recurrent Neural Networks
Francesca Mastrogiuseppe and Srdjan Ostojic · 2018
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But How Does It Work in Theory? Linear SVM with Random Features
Yitong Sun, Anna Gilbert, and Ambuj Tewari · 2018
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Neural Tangent Kernel: Convergence and Generalization in Neural Networks
Arthur Jacot, Franck Gabriel, and Clément Hongler · 2018
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On the Global Convergence of Gradient Descent for Over-parameterized Models using Optimal Transport
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A Mean Field View of the Landscape of Two-Layers Neural Networks
Song Mei, Andrea Montanari, and Phan-Minh Nguyen · 2018
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Trainability and Accuracy of Neural Networks: An Interacting Particle System Approach
Grant M. Rotskoff and Eric Vanden-Eijnden · 2018
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Spurious Valleys in Two-layer Neural Network Optimization Landscapes
Luca Venturi, Afonso S. Bandeira, and Joan Bruna · 2018
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Certified Robustness to Adversarial Examples with Differential Privacy
Mathias Lecuyer, Vaggelis Atlidakis, Roxana Geambasu, Daniel Hsu, and Suman Jana · 2018
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Insect cyborgs: Bio-mimetic feature generators improve machine learning accuracy on limited data
Charles B. Delahunt and J. Nathan Kutz · 2018
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Linking Connectivity, Dynamics, and Computations in Low-Rank Recurrent Neural Networks
Francesca Mastrogiuseppe and Srdjan Ostojic · 2018
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But How Does It Work in Theory? Linear SVM with Random Features
Yitong Sun, Anna Gilbert, and Ambuj Tewari · 2018
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Re-evaluating Circuit Mechanisms Underlying Pattern Separation
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