2 code implementations • 11 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.
1 code implementation • 20 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.
1 code implementation • 4 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).
no code implementations • 11 Jun 2019 • Duanshun Li, Jing Liu, Noseong Park, Dongeun Lee, Giridhar Ramachandran, Ali Seyedmazloom, Kookjin Lee, Chen Feng, Vadim Sokolov, Rajesh Ganesan
0-1 knapsack is of fundamental importance in computer science, business, operations research, etc.
no code implementations • 1 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.
no code implementations • 29 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.
no code implementations • 22 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).
no code implementations • 16 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.
no code implementations • 20 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.