Search Results for author: Dongeun Lee

Found 9 papers, 3 papers with code

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

no code implementations20 Feb 2024 Jinsung Jeon, Hyundong Jin, Jonghyun Choi, Sanghyun Hong, Dongeun Lee, Kookjin Lee, Noseong Park

Extensively evaluating methods with seven image recognition benchmarks, we show that the proposed PAC-FNO improves the performance of existing baseline models on images with various resolutions by up to 77. 1% and various types of natural variations in the images at inference.

Operator-learning-inspired Modeling of Neural Ordinary Differential Equations

no code implementations16 Dec 2023 Woojin Cho, Seunghyeon Cho, Hyundong Jin, Jinsung Jeon, Kookjin Lee, Sanghyun Hong, Dongeun Lee, Jonghyun Choi, Noseong Park

Neural ordinary differential equations (NODEs), one of the most influential works of the differential equation-based deep learning, are to continuously generalize residual networks and opened a new field.

Image Classification Image Generation +3

SigFormer: Signature Transformers for Deep Hedging

1 code implementation20 Oct 2023 Anh Tong, Thanh Nguyen-Tang, Dongeun Lee, Toan Tran, Jaesik Choi

To mitigate such difficulties, we introduce SigFormer, a novel deep learning model that combines the power of path signatures and transformers to handle sequential data, particularly in cases with irregularities.

Time Series Forecasting with Hypernetworks Generating Parameters in Advance

no code implementations22 Nov 2022 Jaehoon Lee, Chan Kim, Gyumin Lee, Haksoo Lim, Jeongwhan Choi, Kookjin Lee, Dongeun Lee, Sanghyun Hong, Noseong Park

Forecasting future outcomes from recent time series data is not easy, especially when the future data are different from the past (i. e. time series are under temporal drifts).

Time Series Time Series Forecasting

Climate Modeling with Neural Diffusion Equations

2 code implementations11 Nov 2021 Jeehyun Hwang, Jeongwhan Choi, Hwangyong Choi, Kookjin Lee, Dongeun Lee, Noseong Park

On the other hand, neural ordinary differential equations (NODEs) are to learn a latent governing equation of ODE from data.

Weather Forecasting

Regularizing Image Classification Neural Networks with Partial Differential Equations

no code implementations29 Sep 2021 Jungeun Kim, Seunghyun Hwang, Jeehyun Hwang, Kookjin Lee, Dongeun Lee, Noseong Park

In other words, the knowledge contained by the learned governing equation can be injected into the neural network which approximates the PDE solution function.

Classification Image Classification

Neural Partial Differential Equations

no code implementations1 Jan 2021 Jungeun Kim, Seunghyun Hwang, Jihyun Hwang, Kookjin Lee, Dongeun Lee, Noseong Park

Neural ordinary differential equations (neural ODEs) introduced an approach to approximate a neural network as a system of ODEs after considering its layer as a continuous variable and discretizing its hidden dimension.

DPM: A Novel Training Method for Physics-Informed Neural Networks in Extrapolation

1 code implementation4 Dec 2020 Jungeun Kim, Kookjin Lee, Dongeun Lee, Sheo Yon Jin, Noseong Park

We present a method for learning dynamics of complex physical processes described by time-dependent nonlinear partial differential equations (PDEs).

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