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ORCIDHayden Schaeffer
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2020 – today
- 2025
[j17]Zecheng Zhang, Wing Tat Leung
, Hayden Schaeffer:
A discretization-invariant extension and analysis of some deep operator networks. J. Comput. Appl. Math. 456: 116226 (2025)- 2024
- [j16]Yuxuan Liu, Zecheng Zhang, Hayden Schaeffer:
PROSE: Predicting Multiple Operators and Symbolic Expressions using multimodal transformers. Neural Networks 180: 106707 (2024) - [i24]Jingmin Sun, Yuxuan Liu, Zecheng Zhang, Hayden Schaeffer:
Towards a Foundation Model for Partial Differential Equations: Multi-Operator Learning and Extrapolation. CoRR abs/2404.12355 (2024) - [i23]Jingmin Sun, Zecheng Zhang, Hayden Schaeffer:
LeMON: Learning to Learn Multi-Operator Networks. CoRR abs/2408.16168 (2024) - [i22]Yuxuan Liu, Jingmin Sun, Xinjie He, Griffin Pinney, Zecheng Zhang, Hayden Schaeffer:
PROSE-FD: A Multimodal PDE Foundation Model for Learning Multiple Operators for Forecasting Fluid Dynamics. CoRR abs/2409.09811 (2024) - [i21]Derek Jollie, Jingmin Sun, Zecheng Zhang, Hayden Schaeffer:
Time-Series Forecasting, Knowledge Distillation, and Refinement within a Multimodal PDE Foundation Model. CoRR abs/2409.11609 (2024) - [i20]Hao Liu, Zecheng Zhang, Wenjing Liao, Hayden Schaeffer:
Neural Scaling Laws of Deep ReLU and Deep Operator Network: A Theoretical Study. CoRR abs/2410.00357 (2024) - 2023
- [i19]Zecheng Zhang, Wing Tat Leung, Hayden Schaeffer:
A discretization-invariant extension and analysis of some deep operator networks. CoRR abs/2307.09738 (2023) - [i18]Zecheng Zhang, Christian Moya, Wing Tat Leung, Guang Lin, Hayden Schaeffer:
Bayesian deep operator learning for homogenized to fine-scale maps for multiscale PDE. CoRR abs/2308.14188 (2023) - [i17]Yuxuan Liu, Zecheng Zhang, Hayden Schaeffer:
PROSE: Predicting Operators and Symbolic Expressions using Multimodal Transformers. CoRR abs/2309.16816 (2023) - [i16]Zecheng Zhang, Christian Moya, Lu Lu, Guang Lin, Hayden Schaeffer:
D2NO: Efficient Handling of Heterogeneous Input Function Spaces with Distributed Deep Neural Operators. CoRR abs/2310.18888 (2023) - 2022
- [c6]Zhijun Chen, Hayden Schaeffer, Rachel A. Ward:
Concentration of Random Feature Matrices in High-Dimensions. MSML 2022: 287-302 - [c5]Yuege Xie, Robert Shi, Hayden Schaeffer, Rachel A. Ward:
SHRIMP: Sparser Random Feature Models via Iterative Magnitude Pruning. MSML 2022: 303-318 - [i15]Esha Saha, Hayden Schaeffer, Giang Tran:
HARFE: Hard-Ridge Random Feature Expansion. CoRR abs/2202.02877 (2022) - [i14]Nicholas Richardson, Hayden Schaeffer, Giang Tran:
SRMD: Sparse Random Mode Decomposition. CoRR abs/2204.06108 (2022) - [i13]Zhijun Chen, Hayden Schaeffer, Rachel A. Ward:
Concentration of Random Feature Matrices in High-Dimensions. CoRR abs/2204.06935 (2022) - [i12]Yuxuan Liu, Scott G. McCalla, Hayden Schaeffer:
Random Feature Models for Learning Interacting Dynamical Systems. CoRR abs/2212.05591 (2022) - [i11]Zecheng Zhang, Wing Tat Leung, Hayden Schaeffer:
BelNet: Basis enhanced learning, a mesh-free neural operator. CoRR abs/2212.07336 (2022) - 2021
- [c4]Kayla Bollinger, Hayden Schaeffer:
Reduced Order Modeling using Shallow ReLU Networks with Grassmann Layers. MSML 2021: 847-867 - [i10]Abolfazl Hashemi, Hayden Schaeffer, Robert Shi, Ufuk Topcu, Giang Tran, Rachel A. Ward:
Function Approximation via Sparse Random Features. CoRR abs/2103.03191 (2021) - [i9]Zhijun Chen, Hayden Schaeffer:
Conditioning of Random Feature Matrices: Double Descent and Generalization Error. CoRR abs/2110.11477 (2021) - [i8]Yuege Xie, Bobby Shi, Hayden Schaeffer, Rachel A. Ward:
SHRIMP: Sparser Random Feature Models via Iterative Magnitude Pruning. CoRR abs/2112.04002 (2021) - 2020
- [j15]Lam Si Tung Ho, Hayden Schaeffer, Giang Tran, Rachel A. Ward:
Recovery guarantees for polynomial coefficients from weakly dependent data with outliers. J. Approx. Theory 259: 105472 (2020) - [j14]Linan Zhang, Hayden Schaeffer:
Forward Stability of ResNet and Its Variants. J. Math. Imaging Vis. 62(3): 328-351 (2020) - [j13]Hayden Schaeffer, Giang Tran, Rachel A. Ward, Linan Zhang:
Extracting Structured Dynamical Systems Using Sparse Optimization With Very Few Samples. Multiscale Model. Simul. 18(4): 1435-1461 (2020) - [j12]Hayden Schaeffer, Scott G. McCalla:
Extending the Step-Size Restriction for Gradient Descent to Avoid Strict Saddle Points. SIAM J. Math. Data Sci. 2(4): 1181-1197 (2020) - [c3]Yifan Sun, Linan Zhang, Hayden Schaeffer:
NeuPDE: Neural Network Based Ordinary and Partial Differential Equations for Modeling Time-Dependent Data. MSML 2020: 352-372 - [i7]Kayla Bollinger, Hayden Schaeffer:
Reduced Order Modeling using Shallow ReLU Networks with Grassmann Layers. CoRR abs/2012.09940 (2020)
2010 – 2019
- 2019
- [j11]Linan Zhang, Hayden Schaeffer:
On the Convergence of the SINDy Algorithm. Multiscale Model. Simul. 17(3): 948-972 (2019) - [i6]Hayden Schaeffer, Scott G. McCalla:
Extending the step-size restriction for gradient descent to avoid strict saddle points. CoRR abs/1908.01753 (2019) - [i5]Yifan Sun, Linan Zhang, Hayden Schaeffer:
NeuPDE: Neural Network Based Ordinary and Partial Differential Equations for Modeling Time-Dependent Data. CoRR abs/1908.03190 (2019) - 2018
- [j10]Hayden Schaeffer, Giang Tran, Rachel A. Ward:
Extracting Sparse High-Dimensional Dynamics from Limited Data. SIAM J. Appl. Math. 78(6): 3279-3295 (2018) - [i4]Hayden Schaeffer, Giang Tran, Rachel A. Ward, Linan Zhang:
Extracting structured dynamical systems using sparse optimization with very few samples. CoRR abs/1805.04158 (2018) - [i3]Linan Zhang, Hayden Schaeffer:
On the Convergence of the SINDy Algorithm. CoRR abs/1805.06445 (2018) - [i2]Linan Zhang, Hayden Schaeffer:
Forward Stability of ResNet and Its Variants. CoRR abs/1811.09885 (2018) - [i1]Lam Si Tung Ho, Hayden Schaeffer, Giang Tran, Rachel A. Ward:
Recovery guarantees for polynomial approximation from dependent data with outliers. CoRR abs/1811.10115 (2018) - 2016
- [j9]Hayden Schaeffer, Thomas Y. Hou:
An Accelerated Method for Nonlinear Elliptic PDE. J. Sci. Comput. 69(2): 556-580 (2016) - 2015
- [j8]Thomas Y. Hou, Qin Li, Hayden Schaeffer:
Sparse + low-energy decomposition for viscous conservation laws. J. Comput. Phys. 288: 150-166 (2015) - [j7]Giang Tran, Hayden Schaeffer, William M. Feldman, Stanley J. Osher:
An L1 Penalty Method for General Obstacle Problems. SIAM J. Appl. Math. 75(4): 1424-1444 (2015) - [j6]Hayden Schaeffer, Yi Yang, Stanley J. Osher:
Space-Time Regularization for Video Decompression. SIAM J. Imaging Sci. 8(1): 373-402 (2015) - [j5]Hayden Schaeffer, Yi Yang, Hongkai Zhao, Stanley J. Osher:
Real-Time Adaptive Video Compression. SIAM J. Sci. Comput. 37(6) (2015) - 2014
- [j4]Hayden Schaeffer, Luminita A. Vese:
Active Contours with Free Endpoints. J. Math. Imaging Vis. 49(1): 20-36 (2014) - [j3]Hayden Schaeffer, Luminita A. Vese:
Variational Dynamics of Free Triple Junctions. J. Sci. Comput. 59(2): 386-411 (2014) - [j2]Alan Mackey, Hayden Schaeffer, Stanley J. Osher:
On the Compressive Spectral Method. Multiscale Model. Simul. 12(4): 1800-1827 (2014) - 2013
- [b1]Hayden Schaeffer:
Variational Models for Fine Structures. University of California, Los Angeles, USA, 2013 - [j1]Hayden Schaeffer, Stanley J. Osher:
A Low Patch-Rank Interpretation of Texture. SIAM J. Imaging Sci. 6(1): 226-262 (2013) - [c2]Yi Yang, Hayden Schaeffer, Wotao Yin, Stanley J. Osher:
Mixing space-time derivatives for video compressive sensing. ACSSC 2013: 158-162 - [c1]Nóirín Duggan, Hayden Schaeffer, Carole Le Guyader, Edward Jones, Martin Glavin, Luminita A. Vese:
Boundary detection in echocardiography using a Split Bregman edge detector and a topology preserving level set approach. ISBI 2013: 73-76
Coauthor Index
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last updated on 2024-11-06 21:34 CET by the dblp team
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