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02/26/2023
Independence-Encouraging Subsampling for Nonparametric Additive Models
The additive model is a popular nonparametric regression method due to i...
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10/10/2022
Approximating Partial Likelihood Estimators via Optimal Subsampling
With the growing availability of large-scale biomedical data, it is ofte...
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10/08/2022
Unweighted estimation based on optimal sample under measurement constraints
To tackle massive data, subsampling is a practical approach to select th...
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09/30/2022
A note on centering in subsample selection for linear regression
Centering is a commonly used technique in linear regression analysis. Wi...
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05/17/2022
Sampling with replacement vs Poisson sampling: a comparative study in optimal subsampling
Faced with massive data, subsampling is a commonly used technique to imp...
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10/25/2021
Nonuniform Negative Sampling and Log Odds Correction with Rare Events Data
We investigate the issue of parameter estimation with nonuniform negativ...
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10/03/2021
EAR-U-Net: EfficientNet and attention-based residual U-Net for automatic liver segmentation in CT
Purpose: This paper proposes a new network framework called EAR-U-Net, w...
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05/30/2021
Rethinking the constraints of multimodal fusion: case study in Weakly-Supervised Audio-Visual Video Parsing
For multimodal tasks, a good feature extraction network should extract i...
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05/24/2021
Taylor saves for later: disentanglement for video prediction using Taylor representation
Video prediction is a challenging task with wide application prospects i...
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03/11/2021
SAR-U-Net: squeeze-and-excitation block and atrous spatial pyramid pooling based residual U-Net for automatic liver CT segmentation
Background and objective: In this paper, a modified U-Net based framewor...
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01/20/2021
Bridge the Vision Gap from Field to Command: A Deep Learning Network Enhancing Illumination and Details
With the goal of tuning up the brightness, low-light image enhancement e...
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01/04/2021
Shed Various Lights on a Low-Light Image: Multi-Level Enhancement Guided by Arbitrary References
It is suggested that low-light image enhancement realizes one-to-many ma...
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01/03/2021
A Switched View of Retinex: Deep Self-Regularized Low-Light Image Enhancement
Self-regularized low-light image enhancement does not require any normal...
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11/11/2020
Maximum sampled conditional likelihood for informative subsampling
Subsampling is a computationally effective approach to extract informati...
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10/04/2020
Reproducible Science with LaTeX
This paper proposes a procedure to execute external source codes from a ...
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06/01/2020
Logistic Regression for Massive Data with Rare Events
This paper studies binary logistic regression for rare events data, or i...
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05/23/2020
Most Likely Optimal Subsampled Markov Chain Monte Carlo
Markov Chain Monte Carlo (MCMC) requires to evaluate the full data likel...
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05/21/2020
Optimal Distributed Subsampling for Maximum Quasi-Likelihood Estimators with Massive Data
Nonuniform subsampling methods are effective to reduce computational bur...
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04/02/2020
Sequential online subsampling for thinning experimental designs
We consider a design problem where experimental conditions (design point...
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01/28/2020
Optimal subsampling for quantile regression in big data
We investigate optimal subsampling for quantile regression. We derive th...
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05/23/2019
Divide-and-Conquer Information-Based Optimal Subdata Selection Algorithm
The information-based optimal subdata selection (IBOSS) is a computation...
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09/05/2018
An Online Updating Approach for Testing the Proportional Hazards Assumption with Streams of Big Survival Data
The Cox model, which remains as the first choice in analyzing time-to-ev...
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06/18/2018
Optimal Subsampling Algorithms for Big Data Generalized Linear Models
To fast approximate the maximum likelihood estimator with massive data, ...
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02/08/2018
More Efficient Estimation for Logistic Regression with Optimal Subsample
Facing large amounts of data, subsampling is a practical technique to ex...
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02/03/2017