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Found 15 papers, 1 papers with code
Optimal Subsampling for Large Sample Logistic Regression
In this paper, we propose fast subsampling algorithms to efficiently approximate the maximum likelihood estimate in logistic regression.
Incremental Transductive Learning Approaches to Schistosomiasis Vector Classification
The key issues pertaining to collection of epidemic disease data for our analysis purposes are that it is a labour intensive, time consuming and expensive process resulting in availability of sparse sample data which we use to develop prediction models.
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-event data even for large datasets, relies on the proportional hazards assumption.
Methodology
Optimal Distributed Subsampling for Maximum Quasi-Likelihood Estimators with Massive Data
We first derive optimal Poisson subsampling probabilities in the context of quasi-likelihood estimation under the A- and L-optimality criteria.
Logistic Regression for Massive Data with Rare Events
We first derive the asymptotic distribution of the maximum likelihood estimator (MLE) of the unknown parameter, which shows that the asymptotic variance convergences to zero in a rate of the inverse of the number of the events instead of the inverse of the full data sample size.
Reproducible Science with LaTeX
This paper proposes a procedure to execute external source codes from a LaTeX document and include the calculation outputs in the resulting Portable Document Format (pdf) file automatically.
Maximum sampled conditional likelihood for informative subsampling
Subsampling is a computationally effective approach to extract information from massive data sets when computing resources are limited.
A Switched View of Retinex: Deep Self-Regularized Low-Light Image Enhancement
The generated reflectance, which is assumed to be irrelevant of illumination by Retinex, is treated as enhanced brightness.
Shed Various Lights on a Low-Light Image: Multi-Level Enhancement Guided by Arbitrary References
This paper proposes a neural network for multi-level low-light image enhancement, which is user-friendly to meet various requirements by selecting different images as brightness reference.
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 enjoys numerous applications, such as surveillance, remote sensing and computational photography.
SAR-U-Net: squeeze-and-excitation block and atrous spatial pyramid pooling based residual U-Net for automatic liver segmentation in Computed Tomography
Background and objective: In this paper, a modified U-Net based framework is presented, which leverages techniques from Squeeze-and-Excitation (SE) block, Atrous Spatial Pyramid Pooling (ASPP) and residual learning for accurate and robust liver CT segmentation, and the effectiveness of the proposed method was tested on two public datasets LiTS17 and SLiver07.
Taylor saves for later: disentanglement for video prediction using Taylor representation
We propose a two-branch seq-to-seq deep model to disentangle the Taylor feature and the residual feature in video frames by a novel recurrent prediction module (TaylorCell) and residual module.
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 information as much as possible and ensure that the extracted feature embedding and other modal feature embedding have an excellent mutual understanding.
EAR-U-Net: EfficientNet and attention-based residual U-Net for automatic liver segmentation in CT
In the Sliver07 evaluation, the proposed method achieved the best segmentation performance on all five standard metrics.
Nonuniform Negative Sampling and Log Odds Correction with Rare Events Data
We first prove that, with imbalanced data, the available information about unknown parameters is only tied to the relatively small number of positive instances, which justifies the usage of negative sampling.
