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1706.03301
Cited By
Neural networks and rational functions
International Conference on Machine Learning (ICML), 2017
11 June 2017
Matus Telgarsky
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Papers citing
"Neural networks and rational functions"
50 / 55 papers shown
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Don't Forget the Nonlinearity: Unlocking Activation Functions in Efficient Fine-Tuning
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Xingyi Yang
Xinchao Wang
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C
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\mathcal{C}^1
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Martin Holler
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Graph Neural Networks Need Cluster-Normalize-Activate Modules
Neural Information Processing Systems (NeurIPS), 2024
Arseny Skryagin
Felix Divo
Mohammad Amin Ali
Devendra Singh Dhami
Kristian Kersting
GNN
251
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05 Dec 2024
On the Geometry and Optimization of Polynomial Convolutional Networks
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Vahid Shahverdi
Giovanni Luca Marchetti
Kathlén Kohn
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01 Oct 2024
Cauchy activation function and XNet
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Xin Li
Zhihong Xia
Hongkun Zhang
436
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28 Sep 2024
Kolmogorov-Arnold Transformer
Xingyi Yang
Xinchao Wang
314
108
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16 Sep 2024
rKAN: Rational Kolmogorov-Arnold Networks
Alireza Afzal Aghaei
445
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20 Jun 2024
Learning smooth functions in high dimensions: from sparse polynomials to deep neural networks
Ben Adcock
Simone Brugiapaglia
N. Dexter
S. Moraga
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04 Apr 2024
Covering Number of Real Algebraic Varieties and Beyond: Improved Bounds and Applications
Yifan Zhang
Joe Kileel
517
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09 Nov 2023
Rational Neural Network Controllers
M. Newton
A. Papachristodoulou
OOD
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195
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12 Jul 2023
A comparison of rational and neural network based approximations
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R. D. Millán
N. Sukhorukova
J. Ugon
207
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08 Mar 2023
Learning to Optimize for Reinforcement Learning
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Rupam Mahmood
Shuicheng Yan
Zhongwen Xu
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03 Feb 2023
Convolutional Neural Operators for robust and accurate learning of PDEs
Neural Information Processing Systems (NeurIPS), 2023
Bogdan Raonić
Roberto Molinaro
Tim De Ryck
Tobias Rohner
Francesca Bartolucci
Rima Alaifari
Siddhartha Mishra
Emmanuel de Bezenac
AAML
638
192
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02 Feb 2023
SignReLU neural network and its approximation ability
Journal of Computational and Applied Mathematics (JCAM), 2022
Jianfei Li
Han Feng
Ding-Xuan Zhou
385
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19 Oct 2022
Transformers with Learnable Activation Functions
Findings (Findings), 2022
Haishuo Fang
Ji-Ung Lee
N. Moosavi
Iryna Gurevych
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30 Aug 2022
Why Robust Generalization in Deep Learning is Difficult: Perspective of Expressive Power
Neural Information Processing Systems (NeurIPS), 2022
Binghui Li
Jikai Jin
Han Zhong
John E. Hopcroft
Liwei Wang
OOD
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27 May 2022
On Regularizing Coordinate-MLPs
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L. MacDonald
Simon Lucey
358
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Activation Functions in Deep Learning: A Comprehensive Survey and Benchmark
S. Dubey
S. Singh
B. B. Chaudhuri
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29 Sep 2021
Sparse approximation of triangular transports. Part II: the infinite dimensional case
Constructive approximation (Constr. Approx.), 2020
Jakob Zech
Youssef Marzouk
287
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28 Jul 2021
Bridging the Gap between Spatial and Spectral Domains: A Unified Framework for Graph Neural Networks
ACM Computing Surveys (CSUR), 2021
Zhiqian Chen
Fanglan Chen
Lei Zhang
Taoran Ji
Kaiqun Fu
Bo Pan
Feng Chen
Lingfei Wu
Charu C. Aggarwal
Chang-Tien Lu
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21 Jul 2021
Neural tensor contractions and the expressive power of deep neural quantum states
Physical review B (PRB), 2021
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Amnon Shashua
Giuseppe Carleo
277
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18 Mar 2021
Parametric Complexity Bounds for Approximating PDEs with Neural Networks
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Tanya Marwah
Zachary Chase Lipton
Andrej Risteski
248
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03 Mar 2021
Consistent Sparse Deep Learning: Theory and Computation
Journal of the American Statistical Association (JASA), 2021
Y. Sun
Qifan Song
F. Liang
BDL
310
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25 Feb 2021
Adaptive Rational Activations to Boost Deep Reinforcement Learning
International Conference on Learning Representations (ICLR), 2021
Quentin Delfosse
P. Schramowski
Martin Mundt
Alejandro Molina
Kristian Kersting
549
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18 Feb 2021
Can stable and accurate neural networks be computed? -- On the barriers of deep learning and Smale's 18th problem
Proceedings of the National Academy of Sciences of the United States of America (PNAS), 2021
Matthew J. Colbrook
Vegard Antun
A. Hansen
511
150
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20 Jan 2021
STENCIL-NET: Data-driven solution-adaptive discretization of partial differential equations
Suryanarayana Maddu
D. Sturm
B. Cheeseman
Christian L. Müller
I. Sbalzarini
326
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15 Jan 2021
On Polynomial Approximations for Privacy-Preserving and Verifiable ReLU Networks
Ramy E. Ali
Jinhyun So
A. Avestimehr
451
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11 Nov 2020
Learning Safe Neural Network Controllers with Barrier Certificates
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Hengjun Zhao
Xia Zeng
Taolue Chen
Zhiming Liu
Jim Woodcock
275
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18 Sep 2020
A deep network construction that adapts to intrinsic dimensionality beyond the domain
A. Cloninger
T. Klock
AI4CE
433
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06 Aug 2020
Expressivity of Deep Neural Networks
Ingo Gühring
Mones Raslan
Gitta Kutyniok
285
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09 Jul 2020
Rational neural networks
Neural Information Processing Systems (NeurIPS), 2020
Nicolas Boullé
Y. Nakatsukasa
Alex Townsend
229
114
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04 Apr 2020
Neural Networks and Polynomial Regression. Demystifying the Overparametrization Phenomena
Matt Emschwiller
D. Gamarnik
Eren C. Kizildag
Ilias Zadik
288
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23 Mar 2020
Padé Activation Units: End-to-end Learning of Flexible Activation Functions in Deep Networks
International Conference on Learning Representations (ICLR), 2019
Alejandro Molina
P. Schramowski
Kristian Kersting
ODL
255
114
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15 Jul 2019
The phase diagram of approximation rates for deep neural networks
Neural Information Processing Systems (NeurIPS), 2019
Dmitry Yarotsky
Anton Zhevnerchuk
353
145
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22 Jun 2019
Smooth function approximation by deep neural networks with general activation functions
Ilsang Ohn
Yongdai Kim
172
91
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17 Jun 2019
A Theoretical Analysis of Deep Neural Networks and Parametric PDEs
Gitta Kutyniok
P. Petersen
Mones Raslan
R. Schneider
334
221
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31 Mar 2019
Data Augmentation for Bayesian Deep Learning
YueXing Wang
Nicholas G. Polson
Vadim Sokolov
UQCV
BDL
396
6
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22 Mar 2019
Theoretical guarantees for sampling and inference in generative models with latent diffusions
Annual Conference Computational Learning Theory (COLT), 2019
Belinda Tzen
Maxim Raginsky
DiffM
307
123
0
05 Mar 2019
Representation Learning with Weighted Inner Product for Universal Approximation of General Similarities
International Joint Conference on Artificial Intelligence (IJCAI), 2019
Geewook Kim
Akifumi Okuno
Kazuki Fukui
Hidetoshi Shimodaira
223
9
0
27 Feb 2019
Negative results for approximation using single layer and multilayer feedforward neural networks
J. M. Almira
P. E. López-de-Teruel
D. J. Romero-Lopez
F. Voigtlaender
MLT
166
2
0
23 Oct 2018
Graph Embedding with Shifted Inner Product Similarity and Its Improved Approximation Capability
Akifumi Okuno
Geewook Kim
Hidetoshi Shimodaira
142
8
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04 Oct 2018
Rational Neural Networks for Approximating Jump Discontinuities of Graph Convolution Operator
Zhiqian Chen
F. Chen
Rongjie Lai
Xuchao Zhang
Chang-Tien Lu
GNN
197
7
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30 Aug 2018
Deep Learning for Energy Markets
Michael Polson
Vadim Sokolov
AI4TS
297
28
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16 Aug 2018
Polynomial Regression As an Alternative to Neural Nets
Xi Cheng
B. Khomtchouk
N. Matloff
Pete Mohanty
261
91
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13 Jun 2018
On representation power of neural network-based graph embedding and beyond
Akifumi Okuno
Hidetoshi Shimodaira
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31 May 2018
Representational Power of ReLU Networks and Polynomial Kernels: Beyond Worst-Case Analysis
Frederic Koehler
Andrej Risteski
130
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29 May 2018
Posterior Concentration for Sparse Deep Learning
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Veronika Rockova
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24 Mar 2018
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