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
49 papers · 1 filter
A note on regularised NTK dynamics with an application to PAC-Bayesian training
Eugenio Clerico, Benjamin Guedj
We establish explicit dynamics for neural networks whose training objective has a regularising term that constrains the parameters to remain close to their initial value. This keep…
Canaries and Whistles: Resilient Drone Communication Networks with (or without) Deep Reinforcement Learning
Chris Hicks, Vasilios Mavroudis, Myles Foley +3
Communication networks able to withstand hostile environments are critically important for disaster relief operations. In this paper, we consider a challenging scenario where drone…
Triadic percolation induces dynamical topological patterns in higher-order networks
Ana P. Millán, Hanlin Sun, Joaquìn J. Torres +1
Triadic interactions are higher-order interactions that occur when a set of nodes affects the interaction between two other nodes. Examples of triadic interactions are present in t…
Wearable data from subjects playing Super Mario, sitting university exams, or performing physical exercise help detect acute mood episodes via self-supervised learning
Filippo Corponi, Bryan M. Li, Gerard Anmella +13
Personal sensing, leveraging data passively and near-continuously collected with wearables from patients in their ecological environment, is a promising paradigm to monitor mood di…
Are NLP Models Good at Tracing Thoughts: An Overview of Narrative Understanding
Lixing Zhu, Runcong Zhao, Lin Gui +1
Narrative understanding involves capturing the author's cognitive processes, providing insights into their knowledge, intentions, beliefs, and desires. Although large language mode…
Full-scale modal testing of a Hawk T1A aircraft for benchmarking vibration-based methods
Marcus Haywood-Alexander, Robin S. Mills, Max D. Champneys +4
Research developments for structural dynamics in the fields of design, system identification and structural health monitoring (SHM) have dramatically expanded the bounds of what ca…
Comparing Comparators in Generalization Bounds
Fredrik Hellström, Benjamin Guedj
We derive generic information-theoretic and PAC-Bayesian generalization bounds involving an arbitrary convex comparator function, which measures the discrepancy between the trainin…
It's About Time: Temporal References in Emergent Communication
Olaf Lipinski, Adam J. Sobey, Federico Cerutti +1
Emergent communication enables agents to develop bespoke languages that improve communication efficiency. Despite the known importance of temporal structure in natural language, th…
Cine cardiac MRI reconstruction using a convolutional recurrent network with refinement
Yuyang Xue, Yuning Du, Gianluca Carloni +3
Cine Magnetic Resonance Imaging (MRI) allows for understanding of the heart's function and condition in a non-invasive manner. Undersampling of the -space is employed to reduce…
Physics-informed Bayesian inference of external potentials in classical density-functional theory
Antonio Malpica-Morales, Peter Yatsyshin, Miguel A. Duran-Olivencia +1
The swift progression of machine learning (ML) has not gone unnoticed in the realm of statistical mechanics. ML techniques have attracted attention by the classical density-functio…
Generalization Bounds: Perspectives from Information Theory and PAC-Bayes
Fredrik Hellström, Giuseppe Durisi, Benjamin Guedj +1
A fundamental question in theoretical machine learning is generalization. Over the past decades, the PAC-Bayesian approach has been established as a flexible framework to address t…
FaceTouch: Detecting hand-to-face touch with supervised contrastive learning to assist in tracing infectious disease
Mohamed R. Ibrahim, Terry Lyons
Through our respiratory system, many viruses and diseases frequently spread and pass from one person to another. Covid-19 served as an example of how crucial it is to track down an…
Probing magnetic fields in the circumgalactic medium using polarization data from MIGHTEE
K. Böckmann, M. Brüggen, V. Heesen +10
The detection and study of magnetic fields surrounding galaxies is important to understand galaxy evolution since magnetic fields are tracers for dynamical processes in the circumg…
Topology and dynamics of higher-order multiplex networks
Sanjukta Krishnagopal, Ginestra Bianconi
Higher-order networks are gaining significant scientific attention due to their ability to encode the many-body interactions present in complex systems. However, higher-order netwo…
Non-parametric learning critical behavior in Ising partition functions: PCA entropy and intrinsic dimension
Rajat K. Panda, Roberto Verdel, Alex Rodriguez +3
We provide and critically analyze a framework to learn critical behavior in classical partition functions through the application of non-parametric methods to data sets of thermal…
Network science Ising states of matter
Hanlin Sun, Rajat Kumar Panda, Roberto Verdel +3
Network science provides very powerful tools for extracting information from interacting data. Although recently the unsupervised detection of phases of matter using machine learni…
Transition to anomalous dynamics in a simple random map
Jin Yan, Moitrish Majumdar, Stefano Ruffo +3
The famous Bernoulli shift (or dyadic transformation) is perhaps the simplest deterministic dynamical system exhibiting chaotic dynamics. It is a piecewise linear time-discrete map…
Recovering high-quality FODs from a reduced number of diffusion-weighted images using a model-driven deep learning architecture
J Bartlett, C E Davey, L A Johnston +1
Fibre orientation distribution (FOD) reconstruction using deep learning has the potential to produce accurate FODs from a reduced number of diffusion-weighted images (DWIs), decrea…
Table inference for combinatorial origin-destination choices in agent-based population synthesis
Ioannis Zachos, Theodoros Damoulas, Mark Girolami
A key challenge in agent-based mobility simulations is the synthesis of individual agent socioeconomic profiles. Such profiles include locations of agent activities, which dictate…
Identifying contributors to supply chain outcomes in a multi-echelon setting: a decentralised approach
Stefan Schoepf, Jack Foster, Alexandra Brintrup
Organisations often struggle to identify the causes of change in metrics such as product quality and delivery duration. This task becomes increasingly challenging when the cause li…
Shaping New Norms for AI
Andrea Baronchelli
As Artificial Intelligence (AI) becomes increasingly integrated into our lives, the need for new norms is urgent. However, AI evolves at a much faster pace than the characteristic…
Onboarding Citizens to Digital Identity Systems
Tasos Spiliotopoulos, Al Tariq Sheik, Debora Gottardello +1
Digital Identity (DI) technologies have the potential to enhance the quality of life of citizens through the provision of seamless services, improve the effectiveness of public ser…
Fast exact simulation of the first-passage event of a subordinator
Jorge Ignacio González Cázares, Feng Lin, Aleksandar Mijatović
This paper provides an exact simulation algorithm for the sampling from the joint law of the first-passage time, the undershoot and the overshoot of a subordinator crossing a non-i…
Inferring unknown unknowns: Regularized bias-aware ensemble Kalman filter
Andrea Nóvoa, Alberto Racca, Luca Magri
Because of physical assumptions and numerical approximations, low-order models are affected by uncertainties in the state and parameters, and by model biases. Model biases, also kn…