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.
no code implementations • 29 May 2023 • Anh T Nguyen, Lam Tran, Anh Tong, Tuan-Duy H. Nguyen, Toan Tran
In this paper, we propose a novel conditional adversarial support alignment (CASA) whose aim is to minimize the conditional symmetric support divergence between the source's and target domain's feature representation distributions, aiming at a more helpful representation for the classification task.
no code implementations • 21 Dec 2020 • Anh Tong, Toan Tran, Hung Bui, Jaesik Choi
Choosing a proper set of kernel functions is an important problem in learning Gaussian Process (GP) models since each kernel structure has different model complexity and data fitness.
no code implementations • 19 Oct 2020 • Anh Tong, Jaesik Choi
Recent advances in Deep Gaussian Processes (DGPs) show the potential to have more expressive representation than that of traditional Gaussian Processes (GPs).
no code implementations • 30 May 2019 • Jiyeon Han, Kyowoon Lee, Anh Tong, Jaesik Choi
We also provide conditions under which CBOCPD provides the lower prediction error compared to BOCPD.
no code implementations • 28 Mar 2017 • Anh Tong, Jaesik Choi
In this paper, we present a new GP model which naturally handles multiple time series by placing an Indian Buffet Process (IBP) prior on the presence of shared kernels.
no code implementations • 4 Jul 2016 • Anh Tong, Jaesik Choi
In this paper, we provide a new perspective to build expressive probabilistic program from continue time series data when the structure of model is not given.
no code implementations • 11 Mar 2016 • Kallol Roy, Anh Tong, Jaesik Choi
To compute the symmetry in a grid structure, we introduce three legal grid moves (i) Commutation (ii) Cyclic Permutation (iii) Stabilization on sets of local grid squares, grid blocks.
no code implementations • 26 Nov 2015 • Yunseong Hwang, Anh Tong, Jaesik Choi
Gaussian Processes (GPs) provide a general and analytically tractable way of modeling complex time-varying, nonparametric functions.