Kookjin Lee

According to our database1, Kookjin Lee authored at least 40 papers between 2012 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Links

On csauthors.net:

Bibliography

2024
PAC-FNO: Parallel-Structured All-Component Fourier Neural Operators for Recognizing Low-Quality Images.
CoRR, 2024

Operator-Learning-Inspired Modeling of Neural Ordinary Differential Equations.
Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023
Climate modeling with neural advection-diffusion equation.
Knowl. Inf. Syst., June, 2023

Learning Flexible Body Collision Dynamics with Hierarchical Contact Mesh Transformer.
CoRR, 2023

Graph Convolutions Enrich the Self-Attention in Transformers!
CoRR, 2023

Reduced-order modeling for parameterized PDEs via implicit neural representations.
CoRR, 2023

Reversible and irreversible bracket-based dynamics for deep graph neural networks.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Hypernetwork-based Meta-Learning for Low-Rank Physics-Informed Neural Networks.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022
Stochastic Galerkin Methods for Linear Stability Analysis of Systems with Parametric Uncertainty.
SIAM/ASA J. Uncertain. Quantification, March, 2022

Time Series Forecasting with Hypernetworks Generating Parameters in Advance.
CoRR, 2022

Sinophobia, misogyny, facism, and many more: A multi-ethnic approach to identifying anti-Asian racism in social media.
CoRR, 2022

Mining Causality from Continuous-time Dynamics Models: An Application to Tsunami Forecasting.
CoRR, 2022

Parameter-varying neural ordinary differential equations with partition-of-unity networks.
CoRR, 2022

AdamNODEs: When Neural ODE Meets Adaptive Moment Estimation.
CoRR, 2022

Unsupervised physics-informed disentanglement of multimodal data for high-throughput scientific discovery.
CoRR, 2022

Structure-preserving Sparse Identification of Nonlinear Dynamics for Data-driven Modeling.
Proceedings of the Mathematical and Scientific Machine Learning, 2022

Deep Sequence Models for Packet Stream Analysis and Early Decisions.
Proceedings of the 47th IEEE Conference on Local Computer Networks, 2022

Significant Enhancement of HCD and TDDB in CMOS FETs by Mechanical Stress.
Proceedings of the IEEE International Reliability Physics Symposium, 2022

2021
Probabilistic partition of unity networks: clustering based deep approximation.
CoRR, 2021

On Surrogate Learning for Linear Stability Assessment of Navier-Stokes Equations with Stochastic Viscosity.
CoRR, 2021

Large-Scale Flight Frequency Optimization with Global Convergence in the US Domestic Air Passenger Markets.
Proceedings of the 2021 SIAM International Conference on Data Mining, 2021

Machine learning structure preserving brackets for forecasting irreversible processes.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

A Novel Method to Solve Neural Knapsack Problems.
Proceedings of the 38th International Conference on Machine Learning, 2021

Climate Modeling with Neural Diffusion Equations.
Proceedings of the IEEE International Conference on Data Mining, 2021

Partition of Unity Networks: Deep HP-Approximation.
Proceedings of the AAAI 2021 Spring Symposium on Combining Artificial Intelligence and Machine Learning with Physical Sciences, Stanford, CA, USA, March 22nd - to, 2021

Deep Conservation: A Latent-Dynamics Model for Exact Satisfaction of Physical Conservation Laws.
Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

DPM: A Novel Training Method for Physics-Informed Neural Networks in Extrapolation.
Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020
Model reduction of dynamical systems on nonlinear manifolds using deep convolutional autoencoders.
J. Comput. Phys., 2020

Parameterized Neural Ordinary Differential Equations: Applications to Computational Physics Problems.
CoRR, 2020

Alternating Energy Minimization Methods for Multi-term Matrix Equations.
CoRR, 2020

2019
A Low-Rank Solver for the Navier-Stokes Equations with Uncertain Viscosity.
SIAM/ASA J. Uncertain. Quantification, 2019

Solving Large-Scale 0-1 Knapsack Problems and its Application to Point Cloud Resampling.
CoRR, 2019

Two Problems in Knowledge Graph Embedding: Non-Exclusive Relation Categories and Zero Gradients.
Proceedings of the 2019 IEEE International Conference on Big Data (IEEE BigData), 2019

2018
Inexact Methods for Symmetric Stochastic Eigenvalue Problems.
SIAM/ASA J. Uncertain. Quantification, 2018

Stochastic Least-Squares Petrov-Galerkin Method for Parameterized Linear Systems.
SIAM/ASA J. Uncertain. Quantification, 2018

On Integrating Knowledge Graph Embedding into SPARQL Query Processing.
Proceedings of the 2018 IEEE International Conference on Web Services, 2018

MMGAN: Manifold-Matching Generative Adversarial Networks.
Proceedings of the 24th International Conference on Pattern Recognition, 2018

2017
A Preconditioned Low-Rank Projection Method with a Rank-Reduction Scheme for Stochastic Partial Differential Equations.
SIAM J. Sci. Comput., 2017

MMGAN: Manifold Matching Generative Adversarial Network for Generating Images.
CoRR, 2017

2012
A group-based communication scheme based on the location information of MTC devices in cellular networks.
Proceedings of IEEE International Conference on Communications, 2012


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