arXiv subjects

Wen Su

Publications and source records attributed to Wen Su.

24 records · Page 2Linked to original sources

arXiv2022

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

Adversarial perturbations have drawn great attentions in various deep neural networks. Most of them are computed by iterations and cannot be interpreted very well. In contrast, little attentions are paid to basic machine learning models such as support vector machines. 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). For linear binary/multi classification support vector machines (SVMs), we derive the explicit solutions for sAP, cuAP and uAP (binary case), and approximate solution for uAP of multi-classification. We also obtain the upper bound of fooling rate for uAP. Such results not only increase the interpretability of the three adversarial perturbations, but also provide great convenience in computation since iterative process can be avoided. Numerical results show that our method is fast and effective in calculating three types of adversarial perturbations.

cs.LG↗

arXiv2021

Pricing Exchange Option Based on Copulas by MCMC Algorithm

This paper focus on pricing exchange option based on copulas by MCMC algorithm. Initially, we introduce the methodologies concerned about risk-netural pricing, copulas and MCMC algorithm. After the basic knowledge, we compare the option prices given by different models, the results show except Gumbel copula, the other model provide similar estimation.

q-fin.MF↗

arXiv2021

Volatility of S&P500: Estimation and Evaluation

In an era when derivatives is getting popular, risk management has gradually become the core content of modern finance. In order to study how to accurately estimate the volatility of the S&P 500 index, after introducing the theoretical background of several methods, this paper uses the historical volatility method, GARCH model method and implied volatility method to estimate the real volatility respectively. At the same time, two ways of adjusting the estimation window, rolling and increasing, are also considered. The unbiased test and goodness of fit test are used to evaluate these methods. The empirical result shows that the implied volatility is the best estimator of the real volatility. The rolling estimation window is recommended when using the historical volatility. On the contrary, the estimation window is supposed to be increased when using the GARCH model.

q-fin.MF↗

arXiv2019

Simple Pose: Rethinking and Improving a Bottom-up Approach for Multi-Person Pose Estimation

We rethink a well-know bottom-up approach for multi-person pose estimation and propose an improved one. The improved approach surpasses the baseline significantly thanks to (1) an intuitional yet more sensible representation, which we refer to as body parts to encode the connection information between keypoints, (2) an improved stacked hourglass network with attention mechanisms, (3) a novel focal L2 loss which is dedicated to hard keypoint and keypoint association (body part) mining, and (4) a robust greedy keypoint assignment algorithm for grouping the detected keypoints into individual poses. Our approach not only works straightforwardly but also outperforms the baseline by about 15% in average precision and is comparable to the state of the art on the MS-COCO test-dev dataset. The code and pre-trained models are publicly available online.

cs.CV↗

arXiv2019

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

Recently, the Network Representation Learning (NRL) techniques, which represent graph structure via low-dimension vectors to support social-oriented application, have attracted wide attention. Though large efforts have been made, they may fail to describe the multiple aspects of similarity between social users, as only a single vector for one unique aspect has been represented for each node. To that end, in this paper, we propose a novel end-to-end framework named MCNE to learn multiple conditional network representations, so that various preferences for multiple behaviors could be fully captured. Specifically, we first design a binary mask layer to divide the single vector as conditional embeddings for multiple behaviors. Then, we introduce the attention network to model interaction relationship among multiple preferences, and further utilize the adapted message sending and receiving operation of graph neural network, so that multi-aspect preference information from high-order neighbors will be captured. Finally, we utilize Bayesian Personalized Ranking loss function to learn the preference similarity on each behavior, and jointly learn multiple conditional node embeddings via multi-task learning framework. Extensive experiments on public datasets validate that our MCNE framework could significantly outperform several state-of-the-art baselines, and further support the visualization and transfer learning tasks with excellent interpretability and robustness.

cs.SI↗

arXiv2011

Formalising the Continuous/Discrete Modeling Step

Formally capturing the transition from a continuous model to a discrete model is investigated using model based refinement techniques. A very simple model for stopping (eg. of a train) is developed in both the continuous and discrete domains. The difference between the two is quantified using generic results from ODE theory, and these estimates can be compared with the exact solutions. Such results do not fit well into a conventional model based refinement framework; however they can be accommodated into a model based retrenchment. The retrenchment is described, and the way it can interface to refinement development on both the continuous and discrete sides is outlined. The approach is compared to what can be achieved using hybrid systems techniques.

cs.SE↗