Tianle Zhang

Tianle Zhang
University of Exeter | UoE · College of Engineering, Mathematics and Physical Sciences

PhD in Computer Science

About

9
Publications
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162
Citations
Introduction
I am currently a Ph.D student supervised by Dr. Wenjie Ruan and Prof. Jonathan Fieldsend at Exeter Trustworthy AI Lab, University of Exeter. My current research interests are focused on: (i) Robustness and Safety on Deep Neural Networks; (ii) Machine Learning, Statistical Learning and their applications. My personal website: tianlezhang.com

Publications

Publications (9)
Preprint
Full-text available
In safety-critical deep learning applications robustness measurement is a vital pre-deployment phase. However, existing robustness verification methods are not sufficiently practical for deploying machine learning systems in the real world. On the one hand, these methods attempt to claim that no perturbations can ``fool'' deep neural networks (DNNs...
Article
Full-text available
The stability of rock slopes is a difficult problem in the field of geotechnical and geological engineering. Less than 20% of all landslides are predictable each year, so a simple, fast, reliable and low-cost method to predict the stability of slopes is urgently needed. This study investigates a new regularized online sequential extreme learning ma...
Article
Extreme Learning Machine (ELM) is a type of machine learning algorithm for training single hidden layer feed forward neural network. Randomly initializing the weight between the input layer and the hidden layer and the threshold of each hidden layer neuron, the weight matrix of hidden layer can be calculated by the least squares method. The efficie...
Article
Full-text available
To provide a new numerical algorithm for solving elliptic partial differential equations (PDEs), the Legendre neural network (LNN) and improved extreme learning machine (IELM) algorithm are introduced to propose a Legendre improved extreme learning machine (L-IELM) method, which is applied to solving elliptic PDEs in this paper. The product of two...
Article
Full-text available
Classifying land-use scenes with high quality and accuracy is an important research direction in current hyperspectral remote sensing images, which is conducive to scientific management and utilization of land. An effective classifier and feature extractor can improve classification stability and accuracy. Therefore, based on deep learning techniqu...
Article
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In this paper, we introduce a new method based on Bernstein Neural Network model (BeNN) and extreme learning machine algorithm to solve the differential equation. In the proposed method, we develop a single-layer functional link BeNN, the hidden layer is eliminated by expanding the input pattern by Bernstein polynomials. The network parameters are...
Article
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Accurate global solar radiation prediction is highly essential for related research on renewable energy sources. The cost implication and measurement expertise of global solar radiation emphasize that intelligence prediction models need to be applied. On the basis of long-term measured daily solar radiation data, this study uses a novel regularized...
Conference Paper
Full-text available
There1 is often a difference between the training library and the test library in the inclusion of noise, which affects the practical application of speech endpoint detection. We propose to use a generalized regularized online sequential extreme learning machine with forgetting factor (GR-OSELM-FF) for voice activation detection to adapt to the dif...
Conference Paper
Full-text available
Forecasting international iron ore is a well-known issue, BIC criterion is used to select the relevant variables of iron ore price. On the basis of the traditional extreme learning machine (ELM), the regular term is introduced to control the complexity of the model, and the genetic algorithm (GA) is used to regularize the extreme learning machine....

Questions

Questions (2)
Question
Hi all,
Currently, I am trying to solve a chance-constrained programming (CCP) problem, i.e.
min𝑓(𝑥,𝜉), s.t.  ℙ(𝑓𝑖(𝑥,𝜉) ≥ 𝛼𝑖 ) ≤ 𝜖𝑖, where 𝑖 = 1, 2, ⋯, 𝑚.
I am wondering are there any solvers, Python packages or implementation to chance-constrained optimization problem using the scenario approach.
Question
How can we verify deep neural networks in terms of reachability, adversarial sample, etc. or from a statistical insight?

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