Fetching the paper…
Reading the bibliography…
This paper introduces a meta-learning approach for parameterized pseudo-differential operators with deep neural networks.
The equations of radiation hydrodynamics
G. C. Pomraning · 1973
Earlier work this paper cites.
Evolutionary Principles in Self-Referential Learning
J. Schmidhuber · 1987
Earlier work this paper cites.
Orthonormal bases of compactly supported wavelets
I. Daubechies · 1988
Earlier work this paper cites.
Learning a synaptic learning rule
Y. Bengio, S. Bengio, and J. Cloutier · 1990
Earlier work this paper cites.
Fast wavelet transforms and numerical algorithms I
G. Beylkin, R. Coifman, and V. Rokhlin · 1991
Earlier work this paper cites.
Artificial neural networks for solving ordinary and partial differential equations
I. E. Lagaris, A. Likas, and D. I. Fotiadis · 1998
Earlier work this paper cites.
A sparse matrix arithmetic based on ℋ \mathcal{H} -matrices. part I: Introduction to ℋ \mathcal{H} -matrices
W. Hackbusch · 1999
Earlier work this paper cites.
Mathematical Analysis and Numerical Methods for Science and Technology: Volume 6 Evolution Problems II
R. Dautray and J.-L. Lions · 2000
Earlier work this paper cites.
On ℋ 2 \mathcal{H}^{2} -matrices
W. Hackbusch, B. N. Khoromskij, and S. Sauter · 2000
Earlier work this paper cites.
An introduction to hierarchical matrices
W. Hackbusch, L. Grasedyck, and S. Börm · 2002
Earlier work this paper cites.
Optical tomography using the time-independent equation of radiative transfer–part 1: forward model
A. D. Klose, U. Netz, J. Beuthan, and A. H. Hielscher · 2002
Earlier work this paper cites.
Evaluation of quadrature schemes for the discrete ordinates method
R. Koch and R. Becker · 2004
Earlier work this paper cites.
3D radiative transfer in cloudy atmospheres
A. Marshak and A. Davis · 2005
Earlier work this paper cites.
A wavelet tour of signal processing: the sparse way
S. Mallat · 2008
Earlier work this paper cites.
Discrete symbol calculus
L. Demanet and L. Ying · 2011
Earlier work this paper cites.
Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups
G. Hinton, L. Deng, D. Yu, G. E. Dahl, A. r. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, T. N. Sainath, and B. Kingsbury · 2012
Earlier work this paper cites.
ImageNet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
Earlier work this paper cites.
Deep learning for NLP (without magic)
R. Socher, Y. Bengio, and C. D. Manning · 2012
Earlier work this paper cites.
On the computational efficiency of training neural networks
R. Livni, S. Shalev-Shwartz, and O. Shamir · 2014
Earlier work this paper cites.
Application of deep belief networks for natural language understanding
R. Sarikaya, G. E. Hinton, and A. Deoras · 2014
Earlier work this paper cites.
An introduction to pseudo-differential operators
M. W. Wong · 2014
Cited alongside, same era.
Visualizing and understanding convolutional networks
M. D. Zeiler and R. Fergus · 2014
Cited alongside, same era.
Deep learning
Y. LeCun, Y. Bengio, and G. Hinton · 2015
Cited alongside, same era.
Deep neural nets as a method for quantitative structure–activity relationships
J. Ma, R. P. Sheridan, A. Liaw, G. E. Dahl, and V. Svetnik · 2015
Cited alongside, same era.
Deep learning in neural networks: An overview
J. Schmidhuber · 2015
Cited alongside, same era.
Tensorflow: A system for large-scale machine learning
M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, et al · 2016
Cited alongside, same era.
Starcraft ii: A new challenge for reinforcement learning
O. Vinyals, T. Ewalds, S. Bartunov, P. Georgiev, A. S. Vezhnevets, M. Yeo, A. Makhzani, H. Küttler, J. Agapiou, J. Schrittwieser, et al · 2017
Later among the works it cites.
Deep-learning tomography
M. Araya-Polo, J. Jennings, A. Adler, and T. Dahlke · 2018
Later among the works it cites.
A unified deep artificial neural network approach to partial differential equations in complex geometries
J. Berg and K. Nyström · 2018
Later among the works it cites.
Bert: Pre-training of deep bidirectional transformers for language understanding
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova · 2018
Later among the works it cites.
De novo structure prediction with deeplearning based scoring
R. Evans, J. Jumper, J. Kirkpatrick, L. Sifre, T. Green, C. Qin, A. Zidek, A. Nelson, A. Bridgland, H. Penedones, et al · 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…
Incorporating Nesterov momentum into Adam
T. Dozat · 2016
Cited alongside, same era.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Cited alongside, same era.
Mastering the game of go with deep neural networks and tree search
D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. van den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, S. Dieleman, D. Grewe, J. Nham, N. Kalchbrenner, I. Sutskever, T. Lillicrap, M. Leach, K. Kavukcuoglu, T. Graepel, and D. Hassabis · 2016
Cited alongside, same era.
Learning to reinforcement learn
J. X. Wang, Z. Kurth-Nelson, D. Tirumala, H. Soyer, J. Z. Leibo, R. Munos, C. Blundell, D. Kumaran, and M. Botvinick · 2016
Cited alongside, same era.
Solving the quantum many-body problem with artificial neural networks
G. Carleo and M. Troyer · 2017
Cited alongside, same era.
Deep learning-based numerical methods for high-dimensional parabolic partial differential equations and backward stochastic differential equations
W. E, J. Han, and A. Jentzen · 2017
Cited alongside, same era.
Solving high-dimensional partial differential equations using deep learning
J. Han, A. Jentzen, and W. E · 2018
Later among the works it cites.
Neural tangent kernel: Convergence and generalization in neural networks
A. Jacot, F. Gabriel, and C. Hongler · 2018
Later among the works it cites.
Why deep neural networks for function approximation?
S. Liang and R. Srikant · 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.
Fast algorithms for integral formulations of steady-state radiative transfer equation
Y. Fan, J. An, and L. Ying · 2019
Closest in time.
A multiscale neural network based on hierarchical nested bases
Y. Fan, J. Feliu-Fabà, L. Lin, L. Ying, and L. Zepeda-Núñez · 2019
Closest in time.
A multiscale neural network based on hierarchical matrices
Y. Fan, L. Lin, L. Ying, and L. Zepeda-Núñez · 2019
Closest in time.
BCR-Net: A neural network based on the nonstandard wavelet form
Y. Fan, C. Orozco-Bohorquez, and L. Ying · 2019
Closest in time.
Solving for high-dimensional committor functions using artificial neural networks
Y. Khoo, J. Lu, and L. Ying · 2019
Closest in time.
SwitchNet: a neural network model for forward and inverse scattering problems
Y. Khoo and L. Ying · 2019
Closest in time.
Variational training of neural network approximations of solution maps for physical models
Y. Li, J. Lu, and A. Mao · 2019
Closest in time.
Neural network as a function approximator and its application in solving differential equations
Z. Liu, Y. Yang, and Q. Cai · 2019
Closest in time.
Smooth function approximation by deep neural networks with general activation functions
I. Ohn and Y. Kim · 2019
Closest in time.
A fast algorithm for radiative transport in isotropic media
K. Ren, R. Zhang, and Y. Zhong · 2019
Closest in time.
Solving electrical impedance tomography with deep learning
Y. Fan and L. Ying · 2020
Closest in time.