Yeonjong Shin

发表

Plateau Phenomenon in Gradient Descent Training of ReLU networks: Explanation, Quantification and Avoidance pdf

Mark Ainsworth, Yeonjong Shin, M. Ainsworth, 2020, SIAM J. Sci. Comput..

Error estimates of residual minimization using neural networks for linear PDEs pdf

George Em Karniadakis, Yeonjong Shin, Zhongqiang Zhang, 2020, ArXiv.

Nonadaptive Quasi-Optimal Points Selection for Least Squares Linear Regression

Dongbin Xiu, Yeonjong Shin, D. Xiu, 2016, SIAM J. Sci. Comput..

Correcting Data Corruption Errors for Multivariate Function Approximation

Dongbin Xiu, Yeonjong Shin, 2016, SIAM J. Sci. Comput..

Sequential function approximation with noisy data

Kailiang Wu, Dongbin Xiu, Yeonjong Shin, 2018, J. Comput. Phys..

On the convergence of physics informed neural networks for linear second-order elliptic and parabolic type PDEs

George Em Karniadakis, Yeonjong Shin, Jerome Darbon, 2020 .

On a near optimal sampling strategy for least squares polynomial regression

Dongbin Xiu, Yeonjong Shin, 2016, J. Comput. Phys..

A Randomized Algorithm for Multivariate Function Approximation

Dongbin Xiu, Yeonjong Shin, 2017, SIAM J. Sci. Comput..

Trainability of ReLU networks and Data-dependent Initialization

George Em Karniadakis, Yeonjong Shin, 2019 .

Sparse Approximation using ℓ1-ℓ2 Minimization and Its Application to Stochastic Collocation

Liang Yan, Dongbin Xiu, Yeonjong Shin, 2017, SIAM J. Sci. Comput..

Dying ReLU and Initialization: Theory and Numerical Examples pdf

Lu Lu, George E. Karniadakis, Yeonjong Shin, 2019, Communications in Computational Physics.

Trainability of ReLU networks and Data-dependent Initialization.

George Em Karniadakis, Yeonjong Shin, G. Karniadakis, 2019 .

A Randomized Tensor Quadrature Method for High Dimensional Polynomial Approximation

Kailiang Wu, Dongbin Xiu, Yeonjong Shin, 2017, SIAM J. Sci. Comput..

Effects of Depth, Width, and Initialization: A Convergence Analysis of Layer-wise Training for Deep Linear Neural Networks pdf

Yeonjong Shin, Yeonjong Shin, 2019, Analysis and Applications.

On the Convergence and generalization of Physics Informed Neural Networks pdf

George Em Karniadakis, Yeonjong Shin, Jerome Darbon, 2020, ArXiv.

GFINNs: GENERIC formalism informed neural networks for deterministic and stochastic dynamical systems pdf

George Em Karniadakis, Yeonjong Shin, Zhen Zhang, 2021, Philosophical Transactions of the Royal Society A.

A Caputo fractional derivative-based algorithm for optimization pdf

George Em Karniadakis, Yeonjong Shin, J'erome Darbon, 2021, ArXiv.

Convergence rate of DeepONets for learning operators arising from advection-diffusion equations pdf

George Em Karniadakis, Yeonjong Shin, Zhongqiang Zhang, 2021, ArXiv.

Deep Kronecker neural networks: A general framework for neural networks with adaptive activation functions

George Em Karniadakis, Yeonjong Shin, Kenji Kawaguchi, 2022, Neurocomputing.

Effects of Depth, Width, and Initialization: A Convergence Analysis of Layer-wise Training for Deep Linear Neural Networks pdf

Yeonjong Shin, 2021, Analysis and Applications.

TRAINABILITY OF ReLU NETWORKS AND DATA-DEPENDENT INITIALIZATION

George Em Karniadakis, Yeonjong Shin, G. Karniadakis, 2019, Journal of Machine Learning for Modeling and Computing.