Fetching the paper…
Reading the bibliography…
We consider shallow (single hidden layer) neural networks and characterize their performance when trained with stochastic gradient descent as the number of hidden units $N$ and gradient descent steps grow to infinity.
Markov Processes: Characterization and Convergence
S. Ethier and T. Kurtz · 1986
Earlier work this paper cites.
Multilayer feedforward networks are universal approximators
K. Hornik, M. Stinchcombe, and H. White · 1989
Earlier work this paper cites.
Approximation capabilities of multilayer feedforward networks
K. Hornik · 1991
Earlier work this paper cites.
Convergence of learning algorithms with constant learning rates
C. Kuan and K. Hornik · 1991
Earlier work this paper cites.
Approximation and estimation bounds for artificial neural networks
A. Barron · 1994
Earlier work this paper cites.
Nonlinearity creates linear independence
Yoshifusa Ito · 1996
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner · 1998
Earlier work this paper cites.
Large deviations and mean-field theory for asymmetric random recurrent neural networks
O. Moynot and M. Samuelides · 2002
Earlier work this paper cites.
Stochastic approximation and recurisve algorithms and applications
H.J. Kushner and G.G. Yin · 2003
Earlier work this paper cites.
Learning Multiple Layers of Features from Tiny Images,
A. Krizhevsky · 2009
Earlier work this paper cites.
Understanding the difficulty of training deep feedforward neural networks
X. Glorot and Y. Bengio · 2010
Earlier work this paper cites.
Stochastic Gradient Descent Optimizes Over-parameterized Deep ReLU Networks
D. Zou, Y. Cao, D. Zhou, and Q. Gu · 2011
Earlier work this paper cites.
Deepface: Closing the gap to human-level performance in face verification
Y. Taigman, M. Yang, M. Ranzato, L. Wolf · 2014
Earlier work this paper cites.
Predicting the sequence specificities of DNA-and RNA-binding proteins by deep learning
B. Alipanahi, A. Delong, M. Weirauch, and B. Frey · 2015
Earlier work this paper cites.
Deep Learning
Y. LeCun, Y. Bengio, and G. Hinton · 2015
Earlier work this paper cites.
End to end learning for self-driving cars,
M. Bojarski, D. Del Test, D. Dworakowski, B. Firnier, B. Flepp, P. Goyal, L. Jackel, M. Monfort, U. Muller, J. Zhang, and X. Zhang · 2016
Cited alongside, same era.
Deep Learning
I. Goodfellow, Y. Bengio, and A. Courville · 2016
Cited alongside, same era.
Reynolds averaged turbulence modelling using deep neural networks with embedded invariance
J. Ling, A. Kurzawski, and J. Templeton · 2016
Cited alongside, same era.
Machine learning strategies for systems with invariance properties
J. Ling, R. Jones, and J. Templeton · 2016
Cited alongside, same era.
Understanding deep convolutional neural networks
S. Mallat · 2016
Cited alongside, same era.
Google Duplex: An AI System for Accomplishing Real-World Tasks Over the Phone
Y. Leviathan and Y. Matias · 2018
Later among the works it cites.
A mean field view of the landscape of two-layer neural networks
S. Mei, A. Montanari, and P. Nguyen · 2018
Later among the works it cites.
A Modern Take on the Bias-Variance Tradeoff in Neural Networks
B. Neal, S. Mittal, A. Baratin, V. Tantia, M. Scicluna, S. Lacoste-Julien, and I. Mitliagkas · 2018
Later among the works it cites.
G. M. Rotskoff and E. Vanden-Eijnden · 2018
Later among the works it cites.
DGM: A deep learning algorithm for solving partial differential equations
J. Sirignano and K. Spiliopoulos · 2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
J. Sirignano, A. Sadhwani, and K. Giesecke · 2016
Cited alongside, same era.
Benefits of depth in neural networks
M. Telgarsky · 2016
Cited alongside, same era.
Deep voice: Real-time neural text-to-speech
S. Arik, M. Chrzanowski, A. Coates, G. Diamos, A. Gibiansky, Y. Kang, X. Li, J. Miller, A. Ng, J. Raiman, S. Sengputa · 2017
Cited alongside, same era.
Spectrally-normalized margin bounds for neural networks
P. Bartlett, D. Foster, and M. Telgarsky · 2017
Cited alongside, same era.
Dermatologist-level classification of skin cancer with deep neural networks
A. Esteva, B. Kuprel, R. Novoa, J. Ko, S. Swetter, H. Blau, and S. Thrun · 2017
Cited alongside, same era.
Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates
S. Gu, E. Holly, T. Lillicrap, and S. Levine · 2017
Cited alongside, same era.
Deep learning in robotics: a review of recent research
H. Pierson and M. Gashler · 2017
Cited alongside, same era.
Gradient Descent Finds Global Minima of Deep Neural Networks
S. Du, J. Lee, H. Li, L. Wang, and X. Zhai · 2019
Later among the works it cites.
Gradient Descent Provably Optimizes Over-Parameterized Neural Networks
S. Du, X. Zhai, B. Poczos, and A. Singh · 2019
Later among the works it cites.
Scaling description of generalization with number of parameters in deep learning,
M. Geiger, A. Jacot, S. Spigler, F. Gabriel, L. Sagun, S. d’Ascoli, G. Biroli, C. Hongler, and M. Wyart · 2019
Later among the works it cites.
Universal features of price formation in financial markets: perspectives from Deep Learning
J. Sirignano and R. Cont · 2019
Later among the works it cites.
Dynamics of deep neural networks and neural tangent hierarchy
J. Huang and H.T. Yau · 2020
Closest in time.
Mean Field Analysis of Neural Networks: a law of large numbers
J. Sirignano and K. Spiliopoulos · 2020
Closest in time.
Mean Field Analysis of Neural Networks: A Central Limit Theorem
J. Sirignano and K. Spiliopoulos · 2020
Closest in time.
Mean Field Analysis of Deep Neural Networks
J. Sirignano and K. Spiliopoulos · 2020
Closest in time.
Asymptotics of Reinforcement Learning with Neural Networks
J. Sirignano and K. Spiliopoulos · 2021
Closest in time.