31 citations · 73 across the 14 of their papers we have counts for
15 papers
Evolving Excellence: Automated Optimization of LLM-based Agents
Paul Brookes, Vardan Voskanyan, Rafail Giavrimis +18
Agentic AI systems built on large language models (LLMs) offer significant potential for automating complex workflows, from software development to customer support. However, LLM a…
Industrial LLM-based Code Optimization under Regulation: A Mixture-of-Agents Approach
Mari Ashiga, Vardan Voskanyan, Fateme Dinmohammadi +7
Recent advancements in Large Language Models (LLMs) for code optimization have enabled industrial platforms to automate software performance engineering at unprecedented scale and…
Tuning LLM-based Code Optimization via Meta-Prompting: An Industrial Perspective
Jingzhi Gong, Rafail Giavrimis, Paul Brookes +8
There is a growing interest in leveraging multiple large language models (LLMs) for automated code optimization. However, industrial platforms deploying multiple LLMs face a critic…
Ensemble Learning for Large Language Models in Text and Code Generation: A Survey
Mari Ashiga, Wei Jie, Fan Wu +5
Generative Pretrained Transformers (GPTs) are foundational Large Language Models (LLMs) for text generation. However, individual LLMs often produce inconsistent outputs and exhibit…
Language Models for Code Optimization: Survey, Challenges and Future Directions
Jingzhi Gong, Vardan Voskanyan, Paul Brookes +7
Language models (LMs) built upon deep neural networks (DNNs) have recently demonstrated breakthrough effectiveness in software engineering tasks such as code generation, completion…
evoML Yellow Paper: Evolutionary AI and Optimisation Studio
Lingbo Li, Leslie Kanthan, Michail Basios +10
Machine learning model development and optimisation can be a rather cumbersome and resource-intensive process. Custom models are often more difficult to build and deploy, and they…
Clone Detection on Large Scala Codebases
Wahidur Rahman, Yisen Xu, Fan Pu +7
Code clones are identical or similar code segments. The wide existence of code clones can increase the cost of maintenance and jeopardise the quality of software. The research comm…
Real-time Detection of Practical Universal Adversarial Perturbations
Kenneth T. Co, Luis Muñoz-González, Leslie Kanthan +1
Universal Adversarial Perturbations (UAPs) are a prominent class of adversarial examples that exploit the systemic vulnerabilities and enable physically realizable and robust attac…
Better Model Selection with a new Definition of Feature Importance
Fan Fang, Carmine Ventre, Lingbo Li +3
Feature importance aims at measuring how crucial each input feature is for model prediction. It is widely used in feature engineering, model selection and explainable artificial in…
IEO: Intelligent Evolutionary Optimisation for Hyperparameter Tuning
Yuxi Huan, Fan Wu, Michail Basios +3
Hyperparameter optimisation is a crucial process in searching the optimal machine learning model. The efficiency of finding the optimal hyperparameter settings has been a big conce…
Cryptocurrency Trading: A Comprehensive Survey
Fan Fang, Carmine Ventre, Michail Basios +4
In recent years, the tendency of the number of financial institutions including cryptocurrencies in their portfolios has accelerated. Cryptocurrencies are the first pure digital as…
Ascertaining price formation in cryptocurrency markets with DeepLearning
Fan Fang, Waichung Chung, Carmine Ventre +4
The cryptocurrency market is amongst the fastest-growing of all the financial markets in the world. Unlike traditional markets, such as equities, foreign exchange and commodities,…
Universal Adversarial Robustness of Texture and Shape-Biased Models
Kenneth T. Co, Luis Muñoz-González, Leslie Kanthan +2
Increasing shape-bias in deep neural networks has been shown to improve robustness to common corruptions and noise. In this paper we analyze the adversarial robustness of texture a…
Revisiting Feller Diffusion: Derivation and Simulation
Ranjiva Munasinghe, Leslie Kanthan, Pathum Kossinna
We propose a simpler derivation of the probability density function of Feller Diffusion using the Fourier Transform and solving the resulting equation via the Method of Characteris…
Darwinian Data Structure Selection
Michail Basios, Lingbo Li, Fan Wu +2
Data structure selection and tuning is laborious but can vastly improve an application's performance and memory footprint. Some data structures share a common interface and enjoy m…