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ORCIDStephan Wojtowytsch
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2020 – today
- 2024
[j6]Stephan Wojtowytsch:
Optimal Bump Functions for Shallow ReLU networks: Weight Decay, Depth Separation, Curse of Dimensionality. J. Mach. Learn. Res. 25: 27:1-27:49 (2024)- [j5]Stephan Wojtowytsch
:
Stochastic Gradient Descent with Noise of Machine Learning Type Part II: Continuous Time Analysis. J. Nonlinear Sci. 34(1): 16 (2024) - [c5]Xuan Zhang, Jacob Helwig, Yuchao Lin, Yaochen Xie, Cong Fu, Stephan Wojtowytsch, Shuiwang Ji:
SineNet: Learning Temporal Dynamics in Time-Dependent Partial Differential Equations. ICLR 2024 - [i19]Xuan Zhang, Jacob Helwig, Yuchao Lin, Yaochen Xie, Cong Fu, Stephan Wojtowytsch, Shuiwang Ji:
SineNet: Learning Temporal Dynamics in Time-Dependent Partial Differential Equations. CoRR abs/2403.19507 (2024) - [i18]Kanan Gupta, Stephan Wojtowytsch:
Nesterov acceleration in benignly non-convex landscapes. CoRR abs/2410.08395 (2024) - 2023
- [j4]Stephan Wojtowytsch:
Stochastic Gradient Descent with Noise of Machine Learning Type Part I: Discrete Time Analysis. J. Nonlinear Sci. 33(3): 45 (2023) - [c4]Jacob Helwig, Xuan Zhang, Cong Fu, Jerry Kurtin, Stephan Wojtowytsch, Shuiwang Ji:
Group Equivariant Fourier Neural Operators for Partial Differential Equations. ICML 2023: 12907-12930 - [c3]Jiyoung Park, Ian Pelakh, Stephan Wojtowytsch:
Minimum norm interpolation by perceptra: Explicit regularization and implicit bias. NeurIPS 2023 - [i17]Kanan Gupta, Jonathan W. Siegel, Stephan Wojtowytsch:
Achieving acceleration despite very noisy gradients. CoRR abs/2302.05515 (2023) - [i16]Jacob Helwig, Xuan Zhang, Cong Fu, Jerry Kurtin, Stephan Wojtowytsch, Shuiwang Ji:
Group Equivariant Fourier Neural Operators for Partial Differential Equations. CoRR abs/2306.05697 (2023) - [i15]Jonathan W. Siegel, Stephan Wojtowytsch:
A qualitative difference between gradient flows of convex functions in finite- and infinite-dimensional Hilbert spaces. CoRR abs/2310.17610 (2023) - [i14]Jiyoung Park, Ian Pelakh, Stephan Wojtowytsch:
Minimum norm interpolation by perceptra: Explicit regularization and implicit bias. CoRR abs/2311.06138 (2023) - 2022
- [i13]Josiah Park, Stephan Wojtowytsch:
Qualitative neural network approximation over R and C: Elementary proofs for analytic and polynomial activation. CoRR abs/2203.13410 (2022) - [i12]Stephan Wojtowytsch:
Optimal bump functions for shallow ReLU networks: Weight decay, depth separation and the curse of dimensionality. CoRR abs/2209.01173 (2022) - 2021
- [j3]Irene Fonseca, Janusz Ginster, Stephan Wojtowytsch:
On the Motion of Curved Dislocations in Three Dimensions: Simplified Linearized Elasticity. SIAM J. Math. Anal. 53(2): 2373-2426 (2021) - [c2]Weinan E, Stephan Wojtowytsch:
Some observations on high-dimensional partial differential equations with Barron data. MSML 2021: 253-269 - [c1]Weinan E, Stephan Wojtowytsch:
On the emergence of simplex symmetry in the final and penultimate layers of neural network classifiers. MSML 2021: 270-290 - [i11]Stephan Wojtowytsch:
Stochastic gradient descent with noise of machine learning type. Part I: Discrete time analysis. CoRR abs/2105.01650 (2021) - [i10]Stephan Wojtowytsch:
Stochastic gradient descent with noise of machine learning type. Part II: Continuous time analysis. CoRR abs/2106.02588 (2021) - 2020
- [j2]Stephan Wojtowytsch, Weinan E:
Can Shallow Neural Networks Beat the Curse of Dimensionality? A Mean Field Training Perspective. IEEE Trans. Artif. Intell. 1(2): 121-129 (2020) - [i9]Weinan E, Stephan Wojtowytsch:
Kolmogorov Width Decay and Poor Approximators in Machine Learning: Shallow Neural Networks, Random Feature Models and Neural Tangent Kernels. CoRR abs/2005.10807 (2020) - [i8]Stephan Wojtowytsch, Weinan E:
Can Shallow Neural Networks Beat the Curse of Dimensionality? A mean field training perspective. CoRR abs/2005.10815 (2020) - [i7]Stephan Wojtowytsch:
On the Convergence of Gradient Descent Training for Two-layer ReLU-networks in the Mean Field Regime. CoRR abs/2005.13530 (2020) - [i6]Weinan E, Stephan Wojtowytsch:
Representation formulas and pointwise properties for Barron functions. CoRR abs/2006.05982 (2020) - [i5]Weinan E, Stephan Wojtowytsch:
On the Banach spaces associated with multi-layer ReLU networks: Function representation, approximation theory and gradient descent dynamics. CoRR abs/2007.15623 (2020) - [i4]Weinan E, Chao Ma, Stephan Wojtowytsch, Lei Wu:
Towards a Mathematical Understanding of Neural Network-Based Machine Learning: what we know and what we don't. CoRR abs/2009.10713 (2020) - [i3]Weinan E, Stephan Wojtowytsch:
A priori estimates for classification problems using neural networks. CoRR abs/2009.13500 (2020) - [i2]Weinan E, Stephan Wojtowytsch:
Some observations on partial differential equations in Barron and multi-layer spaces. CoRR abs/2012.01484 (2020) - [i1]Weinan E, Stephan Wojtowytsch:
On the emergence of tetrahedral symmetry in the final and penultimate layers of neural network classifiers. CoRR abs/2012.05420 (2020)
2010 – 2019
- 2019
- [j1]Patrick W. Dondl, Matteo Novaga, Benedikt Wirth, Stephan Wojtowytsch:
A Phase-field Approximation of the Perimeter under a Connectedness Constraint. SIAM J. Math. Anal. 51(5): 3902-3920 (2019)
Coauthor Index
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