Search Results for author: Haiying Wang

Found 15 papers, 1 papers with code

Nonuniform Negative Sampling and Log Odds Correction with Rare Events Data

no code implementations NeurIPS 2021 Haiying Wang, Aonan Zhang, Chong Wang

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.

Rethinking the constraints of multimodal fusion: case study in Weakly-Supervised Audio-Visual Video Parsing

no code implementations30 May 2021 Jianning Wu, Zhuqing Jiang, Shiping Wen, Aidong Men, Haiying Wang

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.

Semantic Similarity Semantic Textual Similarity +2

Taylor saves for later: disentanglement for video prediction using Taylor representation

no code implementations24 May 2021 Ting Pan, Zhuqing Jiang, Jianan Han, Shiping Wen, Aidong Men, Haiying Wang

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.

Disentanglement Video Prediction

SAR-U-Net: squeeze-and-excitation block and atrous spatial pyramid pooling based residual U-Net for automatic liver segmentation in Computed Tomography

no code implementations11 Mar 2021 Jinke Wang, Peiqing Lv, Haiying Wang, Changfa Shi

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.

Liver Segmentation

Bridge the Vision Gap from Field to Command: A Deep Learning Network Enhancing Illumination and Details

no code implementations20 Jan 2021 Zhuqing Jiang, Chang Liu, Ya'nan Wang, Kai Li, Aidong Men, Haiying Wang, Haiyong Luo

With the goal of tuning up the brightness, low-light image enhancement enjoys numerous applications, such as surveillance, remote sensing and computational photography.

Low-Light Image Enhancement

Shed Various Lights on a Low-Light Image: Multi-Level Enhancement Guided by Arbitrary References

no code implementations4 Jan 2021 Ya'nan Wang, Zhuqing Jiang, Chang Liu, Kai Li, Aidong Men, Haiying Wang

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.

Low-Light Image Enhancement Style Transfer

A Switched View of Retinex: Deep Self-Regularized Low-Light Image Enhancement

no code implementations3 Jan 2021 Zhuqing Jiang, Haotian Li, Liangjie Liu, Aidong Men, Haiying Wang

The generated reflectance, which is assumed to be irrelevant of illumination by Retinex, is treated as enhanced brightness.

Low-Light Image Enhancement

Maximum sampled conditional likelihood for informative subsampling

no code implementations11 Nov 2020 Haiying Wang, Jae Kwang Kim

Subsampling is a computationally effective approach to extract information from massive data sets when computing resources are limited.

Reproducible Science with LaTeX

1 code implementation4 Oct 2020 Haim Bar, Haiying Wang

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.

Logistic Regression for Massive Data with Rare Events

no code implementations ICML 2020 HaiYing Wang

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.


Optimal Distributed Subsampling for Maximum Quasi-Likelihood Estimators with Massive Data

no code implementations21 May 2020 Jun Yu, HaiYing Wang, Mingyao Ai, Huiming Zhang

We first derive optimal Poisson subsampling probabilities in the context of quasi-likelihood estimation under the A- and L-optimality criteria.

Computational Efficiency

An Online Updating Approach for Testing the Proportional Hazards Assumption with Streams of Big Survival Data

no code implementations5 Sep 2018 Yishu Xue, Haiying Wang, Jun Yan, Elizabeth D. Schifano

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.


Incremental Transductive Learning Approaches to Schistosomiasis Vector Classification

no code implementations6 Apr 2017 Terence Fusco, Yaxin Bi, Haiying Wang, Fiona Browne

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.

Classification General Classification +1

Optimal Subsampling for Large Sample Logistic Regression

no code implementations3 Feb 2017 HaiYing Wang, Rong Zhu, Ping Ma

In this paper, we propose fast subsampling algorithms to efficiently approximate the maximum likelihood estimate in logistic regression.


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