Search Results for author: Wen Su

Found 8 papers, 1 papers with code

Crowd counting with crowd attention convolutional neural network

no code implementations15 Apr 2022 Jiwei Chen, Wen Su, Zengfu Wang

Crowd counting is a challenging problem due to the scene complexity and scale variation.

Crowd Counting

SSR-HEF: Crowd Counting with Multi-Scale Semantic Refining and Hard Example Focusing

no code implementations15 Apr 2022 Jiwei Chen, Kewei Wang, Wen Su, Zengfu Wang

Existing methods with simple Euclidean distance algorithm indiscriminately optimize the hard and easy examples so that the densities of hard examples are usually incorrectly predicted to be lower or even zero, which results in large counting errors.

Crowd Counting

Optimization Models and Interpretations for Three Types of Adversarial Perturbations against Support Vector Machines

no code implementations7 Apr 2022 Wen Su, Qingna Li, Chunfeng Cui

In this paper, we investigate the optimization models and the interpretations for three types of adversarial perturbations against support vector machines, including sample-adversarial perturbations (sAP), class-universal adversarial perturbations (cuAP) as well as universal adversarial perturbations (uAP).

Default Distances Based on the CEV-KMV Model

no code implementations21 Jul 2021 Wen Su

This paper presents a new method to assess default risk based on applying the CEV process to the KMV model.

Pricing Exchange Option Based on Copulas by MCMC Algorithm

no code implementations21 Jul 2021 Wen Su

This paper focus on pricing exchange option based on copulas by MCMC algorithm.

Volatility of S&P500: Estimation and Evaluation

no code implementations20 Jul 2021 Wen Su

The rolling estimation window is recommended when using the historical volatility.

MCNE: An End-to-End Framework for Learning Multiple Conditional Network Representations of Social Network

no code implementations27 May 2019 Hao Wang, Tong Xu, Qi Liu, Defu Lian, Enhong Chen, Dongfang Du, Han Wu, Wen Su

Recently, the Network Representation Learning (NRL) techniques, which represent graph structure via low-dimension vectors to support social-oriented application, have attracted wide attention.

Multi-Task Learning Representation Learning

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