27 citations · 101 across the 25 of their papers we have counts for
25 papers
Towards Faithful Neural Network Intrinsic Interpretation with Shapley Additive Self-Attribution
Ying Sun, Hengshu Zhu, Hui Xiong
Self-interpreting neural networks have garnered significant interest in research. Existing works in this domain often (1) lack a solid theoretical foundation ensuring genuine inter…
Which Parameterization of the Matérn Covariance Function?
Kesen Wang, Sameh Abdulah, Ying Sun +1
The Matérn family of covariance functions is currently the most popularly used model in spatial statistics, geostatistics, and machine learning to specify the correlation between t…
Exploring the Efficacy of Statistical and Deep Learning Methods for Large Spatial Datasets: A Case Study
Arnab Hazra, Pratik Nag, Rishikesh Yadav +1
Increasingly large and complex spatial datasets pose massive inferential challenges due to high computational and storage costs. Our study is motivated by the KAUST Competition on…
A Study on Differentiable Logic and LLMs for EPIC-KITCHENS-100 Unsupervised Domain Adaptation Challenge for Action Recognition 2023
Yi Cheng, Ziwei Xu, Fen Fang +5
In this technical report, we present our findings from a study conducted on the EPIC-KITCHENS-100 Unsupervised Domain Adaptation task for Action Recognition. Our research focuses o…
Bivariate DeepKriging for Large-scale Spatial Interpolation of Wind Fields
Pratik Nag, Ying Sun, Brian J Reich
High spatial resolution wind data are essential for a wide range of applications in climate, oceanographic and meteorological studies. Large-scale spatial interpolation or downscal…
Spatio-temporal DeepKriging for Interpolation and Probabilistic Forecasting
Pratik Nag, Ying Sun, Brian J Reich
Gaussian processes (GP) and Kriging are widely used in traditional spatio-temporal mod-elling and prediction. These techniques typically presuppose that the data are observed from…
A semi-parametric estimation method for quantile coherence with an application to bivariate financial time series clustering
Cristian F. Jiménez-Varón, Ying Sun, Ta-Hsin Li
In multivariate time series analysis, spectral coherence measures the linear dependency between two time series at different frequencies. However, real data applications often exhi…
Forecasting high-dimensional functional time series: Application to sub-national age-specific mortality
Cristian F. Jiménez-Varón, Ying Sun, Han Lin Shang
We study the modeling and forecasting of high-dimensional functional time series (HDFTS), which can be cross-sectionally correlated and temporally dependent. We introduce a decompo…
Large-Scale Assessment of Labour Market Dynamics in China during the COVID-19 Pandemic
Ying Sun, Hengshu Zhu, Hui Xiong
The outbreak of the COVID-19 pandemic has had an unprecedented impact on China's labour market, and has largely changed the structure of labour supply and demand in different regio…
Meta Compositional Referring Expression Segmentation
Li Xu, Mark He Huang, Xindi Shang +3
Referring expression segmentation aims to segment an object described by a language expression from an image. Despite the recent progress on this task, existing models tackling thi…
Domain-knowledge Inspired Pseudo Supervision (DIPS) for Unsupervised Image-to-Image Translation Models to Support Cross-Domain Classification
Firas Al-Hindawi, Md Mahfuzur Rahman Siddiquee, Teresa Wu +2
The ability to classify images is dependent on having access to large labeled datasets and testing on data from the same domain that the model can train on. Classification becomes…
Team VI-I2R Technical Report on EPIC-KITCHENS-100 Unsupervised Domain Adaptation Challenge for Action Recognition 2022
Yi Cheng, Dongyun Lin, Fen Fang +3
In this report, we present the technical details of our submission to the EPIC-KITCHENS-100 Unsupervised Domain Adaptation (UDA) Challenge for Action Recognition 2022. This task ai…
A Framework for Generalizing Critical Heat Flux Detection Models Using Unsupervised Image-to-Image Translation
Firas Al-Hindawi, Tejaswi Soori, Han Hu +4
The detection of critical heat flux (CHF) is crucial in heat boiling applications as failure to do so can cause rapid temperature ramp leading to device failures. Many machine lear…
The Second Competition on Spatial Statistics for Large Datasets
Sameh Abdulah, Faten Alamri, Pratik Nag +4
In the last few decades, the size of spatial and spatio-temporal datasets in many research areas has rapidly increased with the development of data collection technologies. As a re…
Tackling Data Heterogeneity: A New Unified Framework for Decentralized SGD with Sample-induced Topology
Yan Huang, Ying Sun, Zehan Zhu +2
We develop a general framework unifying several gradient-based stochastic optimization methods for empirical risk minimization problems both in centralized and distributed scenario…
Cohomology algebra of orbit spaces of free involutions on the product of projective space and 4-sphere
Ying Sun, Jianbo Wang
Let be a finitistic space with the mod 2 cohomology of the product space of a projective space and a 4-sphere. Assume that admits a free involution. In this paper we study…
Bone marrow sparing for cervical cancer radiotherapy on multimodality medical images
Yuening Wang, Ying Sun, Jie Yuan +4
Cervical cancer threatens the health of women seriously. Radiotherapy is one of the main therapy methods but with high risk of acute hematologic toxicity. Delineating the bone marr…
Modeling and Predicting Spatio-temporal Dynamics of PM Concentrations Through Time-evolving Covariance Models
Ghulam A. Qadir, Ying Sun
Fine particulate matter (PM) has become a great concern worldwide due to its adverse health effects. PM concentrations typically exhibit complex spatio-temporal var…
Decentralized Sparse Linear Regression via Gradient-Tracking: Linear Convergence and Statistical Guarantees
Marie Maros, Gesualdo Scutari, Ying Sun +1
We study sparse linear regression over a network of agents, modeled as an undirected graph and no server node. The estimation of the -sparse parameter is formulated as a constra…
Estimation of Spatial Deformation for Nonstationary Processes via Variogram Alignment
Ghulam A. Qadir, Ying Sun, Sebastian Kurtek
In modeling spatial processes, a second-order stationarity assumption is often made. However, for spatial data observed on a vast domain, the covariance function often varies over…
Semiparametric Estimation of Cross-covariance Functions for Multivariate Random Fields
Ghulam A. Qadir, Ying Sun
The prevalence of spatially referenced multivariate data has impelled researchers to develop a procedure for the joint modeling of multiple spatial processes. This ordinarily invol…
Efficiency Assessment of Approximated Spatial Predictions for Large Datasets
Yiping Hong, Sameh Abdulah, Marc G. Genton +1
Due to the well-known computational showstopper of the exact Maximum Likelihood Estimation (MLE) for large geospatial observations, a variety of approximation methods have been pro…
A Unified Contraction Analysis of a Class of Distributed Algorithms for Composite Optimization
Jinming Xu, Ying Sun, Ye Tian +1
We study distributed composite optimization over networks: agents minimize the sum of a smooth (strongly) convex function, the agents' sum-utility, plus a non-smooth (extended-valu…
Accelerated Primal-Dual Algorithms for Distributed Smooth Convex Optimization over Networks
Jinming Xu, Ye Tian, Ying Sun +1
This paper proposes a novel family of primal-dual-based distributed algorithms for smooth, convex, multi-agent optimization over networks that uses only gradient information and go…