1 code implementation • 20 Nov 2023 • Taiyu Ban, Lyuzhou Chen, Derui Lyu, Xiangyu Wang, Huanhuan Chen
Causal discovery from observational data is pivotal for deciphering complex relationships.
1 code implementation • ICCV 2023 • Xiangyu Wang, Jingsen Zhu, Qi Ye, Yuchi Huo, Yunlong Ran, Zhihua Zhong, Jiming Chen
With the popularity of implicit neural representations, or neural radiance fields (NeRF), there is a pressing need for editing methods to interact with the implicit 3D models for tasks like post-processing reconstructed scenes and 3D content creation.
no code implementations • 29 Jun 2023 • Taiyu Ban, Lyvzhou Chen, Xiangyu Wang, Huanhuan Chen
In this paper, we advance the current research of LLM-driven causal discovery by proposing a novel framework that combines knowledge-based LLM causal analysis with data-driven causal structure learning.
no code implementations • 12 Jun 2023 • Lyuzhou Chen, Taiyu Ban, Xiangyu Wang, Derui Lyu, Huanhuan Chen
LLM presents strong capability in discovering causal relationships between variables with the "text" inputs defining the investigated variables, leading to a potential new hierarchy and new ladder of causality.
no code implementations • 27 May 2023 • Xin Xiong, Furao Shen, Xiangyu Wang, Jian Zhao
Many GCL methods with automated data augmentation face the risk of insufficient information as they fail to preserve the essential information necessary for the downstream task.
no code implementations • 12 May 2023 • Yi Su, Xiangyu Wang, Elaine Ya Le, Liang Liu, Yuening Li, Haokai Lu, Benjamin Lipshitz, Sriraj Badam, Lukasz Heldt, Shuchao Bi, Ed Chi, Cristos Goodrow, Su-Lin Wu, Lexi Baugher, Minmin Chen
We conduct live experiments on one of the largest short-form video recommendation platforms that serves billions of users to validate the new experiment designs, quantify the long-term values of exploration, and to verify the effectiveness of the adopted neural linear bandit algorithm for exploration.
no code implementations • 26 Jan 2023 • Xiangyu Wang, Xueming Yan, Yaochu Jin
In this paper, we propose a graph network model for graph coloring, which is a class of representative heterophilous problems.
no code implementations • 4 Oct 2022 • Anjun Chen, Xiangyu Wang, Kun Shi, Shaohao Zhu, Bin Fang, Yingfeng Chen, Jiming Chen, Yuchi Huo, Qi Ye
However, combining RGB and mmWave signals for robust all-weather 3D human reconstruction is still an open challenge, given the sparse nature of mmWave and the vulnerability of RGB images.
no code implementations • 12 Sep 2022 • Anjun Chen, Xiangyu Wang, Shaohao Zhu, Yanxu Li, Jiming Chen, Qi Ye
The results demonstrate that 1) despite the noise and sparsity of the generated point clouds, the mmWave radar can achieve better reconstruction accuracy than the RGB camera but worse than the depth camera; 2) the reconstruction from the mmWave radar is affected by adverse weather conditions moderately while the RGB(D) camera is severely affected.
no code implementations • ACL 2021 • Xiangyu Wang, Chengqing Zong
Emotion category is usually divided into different ones by human beings, but it is indeed difficult to clearly distinguish and define the boundaries between different emotion categories.
no code implementations • 2 May 2021 • Jinjian Li, Chuandong Guo, Li Su, Xiangyu Wang, Quan Hu
The proposed eSUSAN extracts the univalue segment assimilating nucleus from the circle kernel based on the similarity across timestamps and distinguishes corner events by the number of pixels in the nucleus area.
no code implementations • 8 Sep 2020 • Caijun Ren, Xiangyu Wang, Jian Gao, Huanhuan Chen
Detecting changed regions in paired satellite images plays a key role in many remote sensing applications.
no code implementations • 21 Nov 2017 • Risheng Liu, Xin Fan, Shichao Cheng, Xiangyu Wang, Zhongxuan Luo
Deep learning models have gained great success in many real-world applications.
no code implementations • 17 Nov 2016 • Fangjian Guo, Xiangyu Wang, Kai Fan, Tamara Broderick, David B. Dunson
Variational inference (VI) provides fast approximations of a Bayesian posterior in part because it formulates posterior approximation as an optimization problem: to find the closest distribution to the exact posterior over some family of distributions.
no code implementations • 18 Mar 2016 • Xiangyu Wang, Alex Yong-Sang Chia
Here, given a query of any media type, cross-media retrieval seeks to find relevant results of different media types from heterogeneous data sources.
no code implementations • NeurIPS 2016 • Yizhe Zhang, Xiangyu Wang, Changyou Chen, Ricardo Henao, Kai Fan, Lawrence Carin
We unify slice sampling and Hamiltonian Monte Carlo (HMC) sampling, demonstrating their connection via the Hamiltonian-Jacobi equation from Hamiltonian mechanics.
no code implementations • NeurIPS 2016 • Xiangyu Wang, David Dunson, Chenlei Leng
The dataset can be partitioned either horizontally (in the sample space) or vertically (in the feature space).
no code implementations • 1 Oct 2015 • Binyan Jiang, Xiangyu Wang, Chenlei Leng
Formulated in a simple and coherent framework, DA-QDA aims to directly estimate the key quantities in the Bayes discriminant function including quadratic interactions and a linear index of the variables for classification.
2 code implementations • NeurIPS 2015 • Xiangyu Wang, Fangjian Guo, Katherine A. Heller, David B. Dunson
The new algorithm applies random partition trees to combine the subset posterior draws, which is distribution-free, easy to resample from and can adapt to multiple scales.
no code implementations • 7 Jun 2015 • Xiangyu Wang, David Dunson, Chenlei Leng
Ordinary least squares (OLS) is the default method for fitting linear models, but is not applicable for problems with dimensionality larger than the sample size.
no code implementations • 5 Jun 2015 • Xiangyu Wang, Chenlei Leng
Variable selection is a challenging issue in statistical applications when the number of predictors $p$ far exceeds the number of observations $n$.
no code implementations • CVPR 2015 • Alex Yong-Sang Chia, Udana Bandara, Xiangyu Wang, Hiromi Hirano
We model this blending of information by an additive process, and exploit this to design a visual contents distortion algorithm that supports real-time contents recovery by the human visual system.
no code implementations • NeurIPS 2015 • Xiangyu Wang, Chenlei Leng, David B. Dunson
Variable screening is a fast dimension reduction technique for assisting high dimensional feature selection.
no code implementations • NeurIPS 2014 • Xiangyu Wang, Peichao Peng, David Dunson
For massive data sets, efficient computation commonly relies on distributed algorithms that store and process subsets of the data on different machines, minimizing communication costs.
1 code implementation • 17 Dec 2013 • Xiangyu Wang, David B. Dunson
With the rapidly growing scales of statistical problems, subset based communication-free parallel MCMC methods are a promising future for large scale Bayesian analysis.