1.8k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Turing InstituteGB178 papers
- University of OxfordGB114 papers
- University of WarwickGB102 papers
- Queen Mary University of LondonGB91 papers
- University of CambridgeGB89 papers
- University College LondonGB81 papers
- Imperial College LondonGB60 papers
- University of EdinburghGB54 papers
- University of ManchesterGB30 papers
- University of ExeterGB25 papers
- University of SouthamptonGB20 papers
- Centre National de la Recherche ScientifiqueFR19 papers
47 papers · 1 filter
Kernel Methods for the Approximation of the Eigenfunctions of the Koopman Operator
Jonghyeon Lee, Boumediene Hamzi, Boya Hou +3
The Koopman operator provides a linear framework to study nonlinear dynamical systems. Its spectra offer valuable insights into system dynamics, but the operator can exhibit both d…
Quantifying the benefit of load uncertainty reduction for the design of district energy systems under grid constraints using the Value of Information
Max Langtry, Ruchi Choudhary
Load uncertainty must be accounted for during design to ensure building energy systems can meet energy demands during operation. Reducing building load uncertainty allows for impro…
Ideology and polarization set the agenda on social media
Edoardo Loru, Alessandro Galeazzi, Anita Bonetti +6
The abundance of information on social media has reshaped public discussions, shifting attention to the mechanisms that drive online discourse. This study analyzes large-scale Twit…
Learning via Surrogate PAC-Bayes
Antoine Picard-Weibel, Roman Moscoviz, Benjamin Guedj
PAC-Bayes learning is a comprehensive setting for (i) studying the generalisation ability of learning algorithms and (ii) deriving new learning algorithms by optimising a generalis…
AI for Explosive Ordnance Detection in Clearance Operations: The State of Research
Björn Kischelewski, Gregory Cathcart, David Wahl +1
The detection and clearance of explosive ordnance (EO) continues to be a predominantly manual and high-risk process that can benefit from advances in technology to improve its effi…
Causal machine learning for predicting treatment outcomes
Stefan Feuerriegel, Dennis Frauen, Valentyn Melnychuk +7
Causal machine learning (ML) offers flexible, data-driven methods for predicting treatment outcomes including efficacy and toxicity, thereby supporting the assessment and safety of…
Emergent social conventions and collective bias in LLM populations
Ariel Flint Ashery, Luca Maria Aiello, Andrea Baronchelli
Social conventions are the backbone of social coordination, shaping how individuals form a group. As growing populations of artificial intelligence (AI) agents communicate through…
Autonomous Network Defence using Reinforcement Learning
Myles Foley, Chris Hicks, Kate Highnam +1
In the network security arms race, the defender is significantly disadvantaged as they need to successfully detect and counter every malicious attack. In contrast, the attacker nee…
Rationalising data collection for supporting decision making in building energy systems using Value of Information analysis
Max Langtry, Chaoqun Zhuang, Rebecca Ward +5
The use of data collection to support decision making through the reduction of uncertainty is ubiquitous in the management, operation, and design of building energy systems. Howeve…
Reconstructing unsteady flows from sparse, noisy measurements with a physics-constrained convolutional neural network
Yaxin Mo, Luca Magri
Data from fluid flow measurements are typically sparse, noisy, and heterogeneous, often from mixed pressure and velocity measurements, resulting in incomplete datasets. In this pap…
General theory for extended-range percolation on simple and multiplex networks
Lorenzo Cirigliano, Claudio Castellano, Ginestra Bianconi
Extended-range percolation is a robust percolation process that has relevance for quantum communication problems. In extended-range percolation nodes can be trusted or untrusted. U…
What if? Causal Machine Learning in Supply Chain Risk Management
Mateusz Wyrembek, George Baryannis, Alexandra Brintrup
The penultimate goal for developing machine learning models in supply chain management is to make optimal interventions. However, most machine learning models identify correlations…
Mapping the individual, social, and biospheric impacts of Foundation Models
Andrés Domínguez Hernández, Shyam Krishna, Antonella Maia Perini +9
Responding to the rapid roll-out and large-scale commercialization of foundation models, large language models, and generative AI, an emerging body of work is shedding light on the…
Mitigating Deep Reinforcement Learning Backdoors in the Neural Activation Space
Sanyam Vyas, Chris Hicks, Vasilios Mavroudis
This paper investigates the threat of backdoors in Deep Reinforcement Learning (DRL) agent policies and proposes a novel method for their detection at runtime. Our study focuses on…
E(2)-Equivariant Features in Machine Learning for Morphological Classification of Radio Galaxies
Natalie E. P. Lines, Joan Font-Quer Roset, Anna M. M. Scaife
With the growth of data from new radio telescope facilities, machine-learning approaches to the morphological classification of radio galaxies are increasingly being utilised. Howe…
Normative brain mapping of 3-dimensional morphometry imaging data using skewed functional data analysis
Marco Palma, Shahin Tavakoli, Julia Brettschneider +2
Tensor-based morphometry (TBM) aims at showing local differences in brain volumes with respect to a common template. TBM images are smooth but they exhibit (especially in diseased…
Private Collaborative Edge Inference via Over-the-Air Computation
Selim F. Yilmaz, Burak Hasircioglu, Li Qiao +1
We consider collaborative inference at the wireless edge, where each client's model is trained independently on its local dataset. Clients are queried in parallel to make an accura…
Improving probabilistic forecasts of extreme wind speeds by training statistical post-processing models with weighted scoring rules
Jakob Benjamin Wessel, Christopher A. T. Ferro, Gavin R. Evans +1
Accurate forecasts of extreme wind speeds are of high importance for many applications. Such forecasts are usually generated by ensembles of numerical weather prediction (NWP) mode…
Higher-order triadic percolation on random hypergraphs
Hanlin Sun, Ginestra Bianconi
In this work, we propose a comprehensive theoretical framework combining percolation theory with nonlinear dynamics in order to study hypergraphs with a time-varying giant componen…
Extracting self-similarity from data
Nikos Bempedelis, Luca Magri, Konstantinos Steiros
Identifying self-similarity is key to understanding and modelling a plethora of phenomena in fluid mechanics. Unfortunately, this is not always possible to perform formally in high…
Evaluating the Ability of Large Language Models to Reason about Cardinal Directions
Anthony G Cohn, Robert E Blackwell
We investigate the abilities of a representative set of Large language Models (LLMs) to reason about cardinal directions (CDs). To do so, we create two datasets: the first, co-crea…
Multi-Layer Ranking with Large Language Models for News Source Recommendation
Wenjia Zhang, Lin Gui, Rob Procter +1
To seek reliable information sources for news events, we introduce a novel task of expert recommendation, which aims to identify trustworthy sources based on their previously quote…
The AI Community Building the Future? A Quantitative Analysis of Development Activity on Hugging Face Hub
Cailean Osborne, Jennifer Ding, Hannah Rose Kirk
Open model developers have emerged as key actors in the political economy of artificial intelligence (AI), but we still have a limited understanding of collaborative practices in t…
How Can We Effectively Expand the Vocabulary of LLMs with 0.01GB of Target Language Text?
Atsuki Yamaguchi, Aline Villavicencio, Nikolaos Aletras
Large language models (LLMs) have shown remarkable capabilities in many languages beyond English. Yet, LLMs require more inference steps when generating non-English text due to the…