Search Results for author: Leslie Kanthan

Found 7 papers, 1 papers with code

evoML Yellow Paper: Evolutionary AI and Optimisation Studio

no code implementations20 Dec 2022 Lingbo Li, Leslie Kanthan, Michail Basios, Fan Wu, Manal Adham, Vitali Avagyan, Alexis Butler, Paul Brookes, Rafail Giavrimis, Buhong Liu, Chrystalla Pavlou, Matthew Truscott, Vardan Voskanyan

Additionally, a key feature of evoML is that it embeds code and model optimisation into the model development process, and includes multi-objective optimisation capabilities.


Real-time Detection of Practical Universal Adversarial Perturbations

no code implementations16 May 2021 Kenneth T. Co, Luis Muñoz-González, Leslie Kanthan, Emil C. Lupu

Universal Adversarial Perturbations (UAPs) are a prominent class of adversarial examples that exploit the systemic vulnerabilities and enable physically realizable and robust attacks against Deep Neural Networks (DNNs).

Blocking Image Classification +2

IEO: Intelligent Evolutionary Optimisation for Hyperparameter Tuning

no code implementations10 Sep 2020 Yuxi Huan, Fan Wu, Michail Basios, Leslie Kanthan, Lingbo Li, Baowen Xu

In this paper, we introduce an intelligent evolutionary optimisation algorithm which applies machine learning technique to the traditional evolutionary algorithm to accelerate the overall optimisation process of tuning machine learning models in classification problems.

BIG-bench Machine Learning

Cryptocurrency Trading: A Comprehensive Survey

no code implementations25 Mar 2020 Fan Fang, Carmine Ventre, Michail Basios, Leslie Kanthan, Lingbo Li, David Martinez-Regoband, Fan Wu

This paper provides a comprehensive survey of cryptocurrency trading research, by covering 146 research papers on various aspects of cryptocurrency trading (e. g., cryptocurrency trading systems, bubble and extreme conditions, prediction of volatility and return, crypto-assets portfolio construction and crypto-assets, technical trading and others).


Universal Adversarial Robustness of Texture and Shape-Biased Models

1 code implementation23 Nov 2019 Kenneth T. Co, Luis Muñoz-González, Leslie Kanthan, Ben Glocker, Emil C. Lupu

Increasing shape-bias in deep neural networks has been shown to improve robustness to common corruptions and noise.

Adversarial Robustness Image Classification

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