Fetching the paper…
Reading the bibliography…
Recently, deep learning has achieved huge successes in many important applications.
M. L. Minsky, S. Papert,
1969
Earlier work this paper cites.
W. A. Light and E. W. Cheney, Approximation Theory in Tensor Product Spaces, Springer-Verlag, 1985
1985
Earlier work this paper cites.
C. L. Giles, T. Maxwell, ”Learning, invariance, and generalization in high-order neural networks,” Applied optics, vol. 26, pp. 4972-4978, 1987
1987
Earlier work this paper cites.
H. Jeffreys and B. S. Jeffreys, ”Weierstrass’s Theorem on Approximation by Polynomials” and ”Extension of Weierstrass’s Approximation Theory.” in
1988
Earlier work this paper cites.
K. Hornik, M. Stinchcombe, H. White, ”Multilayer feedforward networks are universal approximators,”
1989
Earlier work this paper cites.
R. Remmert, ”The fundamental theorem of algebra.” in
1991
Earlier work this paper cites.
J. Park, I. W. Sandberg, ”Universal approximation using radial-basis-function networks,” Neural computation. 1991 Jun;3(2):246-57
1991
Earlier work this paper cites.
C. Debao, ”Degree of approximation by superpositions of a sigmoidal function,” Approximation Theory and its Applications. 1993 Sep 1;9(3):17-28
1993
Earlier work this paper cites.
A. Krizhevsky, I. Sutskever, G. E. Hinton, ”Imagenet classification with deep convolutional neural networks,” In NIPS, 2012
2012
Earlier work this paper cites.
G. E. Dahl, D. Yu, L. Deng and A. Acero, ”Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition. IEEE Transactions on audio, speech, and language processing,” vol. 20, no. 1, pp. 30-42, 2012
2012
Earlier work this paper cites.
Y. Bengio, A. Courville and P. Vincent, ”Representation learning: A review and new perspectives,”
2013
Earlier work this paper cites.
2014
Earlier work this paper cites.
I. Goodfellow, et al., ”Generative adversarial nets,” In NIPS, 2014
2014
Earlier work this paper cites.
L. Szymanski and B. McCane, ”Deep networks are effective encoders of periodicity,”
2014
Earlier work this paper cites.
M. Bianchini and F. Scarselli, ”On the complexity of neural network classifiers: A comparison between shallow and deep architectures,” IEEE Transactions on Neural Networks and Learning Systems, vol. 25, pp. 1553-1565, 2014
2014
Earlier work this paper cites.
R. Livni, et al., ”On the computational efficiency of training neural networks.” In NIPS, 2014
2014
Earlier work this paper cites.
A. Andoni, et al., ”Learning polynomials with neural networks.” In ICML, 2014
2014
Cited alongside, same era.
C. Szegedy, et al., ”Going deeper with convolutions,” In CVPR, 2015
2015
Cited alongside, same era.
K. He, et al., ”Deep residual learning for image recognition,” In CVPR, 2016
2016
Cited alongside, same era.
C. Szegedy, et al., ”Rethinking the inception architecture for computer vision,” In CVPR, 2016
2016
Cited alongside, same era.
A. Kumar, et al., ”Ask me anything: Dynamic memory networks for natural language processing. In ICML, 2016
2016
Cited alongside, same era.
G. Wang, ”A Perspective on Deep Imaging,”
2016
Cited alongside, same era.
S. Liang and R. Srikant, ”Why deep neural networks for function approximation?” In ICLR, 2017
2017
Later among the works it cites.
Kurkova, V. and Sanguineti, M. (2017). Probabilistic Lower Bounds for Approximation by Shallow Perceptron Networks. Neural Networks 91, pp. 34-41, 2017
2017
Later among the works it cites.
F. Fan, W. Cong, G. Wang, ”Generalized Backpropagation Algorithm for Training Second-order Neural Networks,” International Journal for Numerical Methods in Biomedical Engineering, doi.org/10.1002/cnm.2956, 2017
2017
Later among the works it cites.
H. W. Lin, M. Tegmark, D. Rolnick, ”Why does deep and cheap learning work so well?”
2017
Later among the works it cites.
S. Liang and R. Srikant., ”Why deep neural networks for function approximation?.” In ICLR, 2017
2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
R. Eldan, O. Shamir, ”The power of depth for feedforward neural networks,” In COLT, 2016
2016
Cited alongside, same era.
N. Cohen, O. Sharir, and A. Shashua, ”On the expressive power of deep learning: A tensor analysis,” In COLT, 2016
2016
Cited alongside, same era.
M. Telgarsky, ”Benefits of depth in neural networks,” In COLT, 2016
2016
Cited alongside, same era.
H. N. Mhaskar and T. Poggio, ”Deep vs. shallow networks: An approximation theory perspective,”
2016
Cited alongside, same era.
I. Goodfellow, Y. Bengio, A. Courville,
2016
Cited alongside, same era.
S. Han, ”Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding,” In ICLR, 2016
2016
Cited alongside, same era.
Y. Zhang and H. Yu, ”Convolutional Neural Network based Metal Artifact Reduction in X-ray Computed Tomography,”
2018
Closest in time.
2018
Closest in time.
F. Fan, W. Cong, G. Wang, ”A new type of neurons for machine learning,” International Journal for Numerical Methods in Biomedical Engineering, vol. 34, no. 2, Feb. 2018
2018
Closest in time.
2018
Closest in time.
D. Krotov and J. Hopfield, ”Dense associative memory is robust to adversarial inputs,” Neural computation, 30(12), pp.3151-3167. 2018
2018
Closest in time.
2018
Closest in time.
J. C. Ye, Y. Han and E. Cha, ”Deep convolutional framelets: A general deep learning framework for inverse problems,” SIAM Journal on Imaging Sciences, 11(2), pp.991-1048. 2018
2018
Closest in time.
Y. Hong, J. Kim, G. Chen, W. Lin, P. T. Yap and D. Shen, ”Longitudinal Prediction of Infant Diffusion MRI Data via Graph Convolutional Adversarial Networks,”
2019
Closest in time.
2019
Closest in time.
Y. Ding, ”On the Universal Approximability and Complexity Bounds of Quantized ReLU Neural Networks,” In ICLR, 2019
2019
Closest in time.