no code implementations • 28 May 2024 • Xiangyu Sun, Raquel Aoki, Kevin H. Wilson
However, most existing CFE methods require access to the model's training dataset, few methods can handle multivariate time-series, and none can handle multivariate time-series without training datasets.
no code implementations • 27 May 2024 • Xiangyu Sun, Joo Chan Lee, Daniel Rho, Jong Hwan Ko, Usman Ali, Eunbyung Park
To mitigate the storage overhead, we propose Factorized 3D Gaussian Splatting (F-3DGS), a novel approach that drastically reduces storage requirements while preserving image quality.
no code implementations • 1 Jan 2024 • Byeonghyeon Lee, Howoong Lee, Xiangyu Sun, Usman Ali, Eunbyung Park
However, it suffers from severe degradation in the rendering quality if the training images are blurry.
1 code implementation • 22 Nov 2023 • Joo Chan Lee, Daniel Rho, Xiangyu Sun, Jong Hwan Ko, Eunbyung Park
On the other hand, 3D Gaussian splatting (3DGS) has recently emerged as an alternative representation that leverages a 3D Gaussisan-based representation and adopts the rasterization pipeline to render the images rather than volumetric rendering, achieving very fast rendering speed and promising image quality.
no code implementations • 7 Apr 2023 • Sibei Chen, Hanbing Liu, Weiting Jin, Xiangyu Sun, Xiaoyao Feng, Ju Fan, Xiaoyong Du, Nan Tang
Orchestrating a high-quality data preparation program is essential for successful machine learning (ML), but it is known to be time and effort consuming.
no code implementations • NeurIPS 2021 • Guiliang Liu, Xiangyu Sun, Oliver Schulte, Pascal Poupart
We propose a Represent And Mimic (RAMi) framework for training 1) an identifiable latent representation to capture the independent factors of variation for the objects and 2) a mimic tree that extracts the causal impact of the latent features on DRL action values.
1 code implementation • 9 Sep 2021 • Xiangyu Sun, Oliver Schulte, Guiliang Liu, Pascal Poupart
We describe NTS-NOTEARS, a score-based structure learning method for time-series data to learn dynamic Bayesian networks (DBNs) that captures nonlinear, lagged (inter-slice) and instantaneous (intra-slice) relations among variables.
1 code implementation • 4 Jun 2020 • Xiangyu Sun, Jack Davis, Oliver Schulte, Guiliang Liu
This paper addresses the trade-off between Accuracy and Transparency for deep learning applied to sports analytics.