
How Bayesian Should Bayesian Optimisation Be?
Bayesian optimisation (BO) uses probabilistic surrogate models  usually...
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ChanVese Reformulation for Selective Image Segmentation
Selective segmentation involves incorporating user input to partition an...
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TransformerEncoder Detector Module: Using Context to Improve Robustness to Adversarial Attacks on Object Detection
Deep neural network approaches have demonstrated high performance in obj...
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A Generalized Asymmetric Dualfront Model for Active Contours and Image Segmentation
The geodesic distancebased dualfront curve evolution model is a powerf...
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Deep covariatelearning: optimising information extraction from terrain texture for geostatistical modelling applications
Where data is available, it is desirable in geostatistical modelling to ...
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Interpreting RNN behaviour via excitable network attractors
Machine learning has become a basic tool in scientific research and for ...
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SeDAR  Semantic Detection and Ranging: Humans can localise without LiDAR, can robots?
How does a person work out their location using a floorplan? It is proba...
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Concept Drift and Anomaly Detection in Graph Streams
Graph representations offer powerful and intuitive ways to describe data...
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"Going back to our roots": second generation biocomputing
Researchers in the field of biocomputing have, for many years, successfu...
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A Classification Framework for Partially Observed Dynamical Systems
We present a general framework for classifying partially observed dynami...
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Generalization Bounds for Metric and Similarity Learning
Recently, metric learning and similarity learning have attracted a large...
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Scaling laws in geolocated Twitter data
We observe and report on a systematic relationship between population de...
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Social Sensing of Floods in the UK
"Social sensing" is a form of crowdsourcing that involves systematic an...
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Bayesian analysis of 210Pb dating
In many studies of environmental change of the past few centuries, 210Pb...
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On the Learnability of Programming Language Semantics
Game semantics is a powerful method of semantic analysis for programming...
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Time series kernel similarities for predicting Paroxysmal Atrial Fibrillation from ECGs
We tackle the problem of classifying Electrocardiography (ECG) signals w...
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Emulating dynamic nonlinear simulators using Gaussian processes
In this paper, we examine the emulation of nonlinear deterministic comp...
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Aggregated Sparse Attention for Steering Angle Prediction
In this paper, we apply the attention mechanism to autonomous driving fo...
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Salient Region Segmentation
Saliency prediction is a well studied problem in computer vision. Early ...
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A Bayesian Framework for Assessing the Strength Distribution of Composite Structures with Random Defects
This paper presents a novel stochastic framework to quantify the knock d...
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Anomaly and Change Detection in Graph Streams through ConstantCurvature Manifold Embeddings
Mapping complex input data into suitable lower dimensional manifolds is ...
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Visual Object Tracking: The Initialisation Problem
Model initialisation is an important component of object tracking. Track...
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Change Point Methods on a Sequence of Graphs
The present paper considers a finite sequence of graphs, e.g., coming fr...
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A general theory for preferential sampling in environmental networks
This paper presents a general model framework for detecting the preferen...
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Automatic hyperparameter selection in Autodock
Autodock is a widely used molecular modeling tool which predicts how sma...
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OnPolicy Trust Region Policy Optimisation with Replay Buffers
Building upon the recent success of deep reinforcement learning methods,...
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Modelling Numerical Systems with Two Distinct Labelled Output Classes
We present a new method of modelling numerical systems where there are t...
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Deep Adversarial Learning in Intrusion Detection: A Data Augmentation Enhanced Framework
Intrusion detection systems (IDSs) play an important role in identifying...
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Emulating computer models with stepdiscontinuous outputs using Gaussian processes
In many realworld applications, we are interested in approximating func...
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How are emergent constraints quantifying uncertainty and what do they leave behind?
The use of emergent constraints to quantify uncertainty for key policy r...
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Contextual Relabelling of Detected Objects
Contextual information, such as the cooccurrence of objects and the spa...
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A Comprehensive Hidden Markov Model for Hourly Rainfall Time Series
For hydrological applications, such as urban flood modelling, it is ofte...
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SODECL: An Open Source Library for Calculating Multiple Orbits of a System of Stochastic Differential Equations in Parallel
Stochastic differential equations (SDEs) are widely used to model system...
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A Hierarchical Modelling Framework for Correcting Delayed Reporting in SpatioTemporal Disease Surveillance Data
Delayed reporting is a well established problem in the surveillance of i...
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Future Proofing a Building Design Using History Matching Inspired Level Set Techniques
History Matching is a technique used to calibrate complex computer model...
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Classification of Computer Models with Labelled Outputs
Classification is a vital tool that is important for modelling many comp...
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Stochastic Simulators: An Overview with Opportunities
In modern science, deterministic computer models are often used to under...
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εshotgun: εgreedy Batch Bayesian Optimisation
Bayesian optimisation is a popular, surrogate modelbased approach for o...
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LocationEnabled IoT (LEIoT): A Survey of Positioning Techniques, Error Sources, and Mitigation
The Internet of Things (IoT) has started to empower the future of many i...
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On the Combined Impact of Population Size and Subproblem Selection in MOEA/D
This paper intends to understand and to improve the working principle of...
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What do you Mean? The Role of the Mean Function in Bayesian Optimisation
Bayesian optimisation is a popular approach for optimising expensive bla...
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A continuoustime statespace model for rapid qualitycontrol of Argos locations from animalborne tags
Statespace models are important tools for quality control of errorpron...
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Crossvalidation based adaptive sampling for Gaussian process models
In many realworld applications, we are interested in approximating blac...
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A framework for probabilistic weather forecast postprocessing across models and lead times using machine learning
Forecasting the weather is an increasingly data intensive exercise. Nume...
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Complex networks for event detection in heterogeneous high volume news streams
Detecting important events in high volume news streams is an important t...
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Scoring and Assessment in Medical VR Training Simulators with Dynamic Time Series Classification
This research proposes and evaluates scoring and assessment methods for ...
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Accelerating Uncertainty Quantification of Groundwater Flow Modelling Using Deep Neural Networks
Quantifying the uncertainty in model parameters and output is a critical...
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UserOriented MultiTask Federated Deep Learning for Mobile Edge Computing
Federated Learning (FL) is a recent approach for collaboratively trainin...
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Fast Adaptive Task Offloading in Edge Computing based on Meta Reinforcement Learning
Multiaccess edge computing (MEC) aims to extend cloud service to the ne...
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Online Service Migration in Edge Computing with Incomplete Information: A Deep Recurrent ActorCritic Method
Multiaccess Edge Computing (MEC) is a key technology in the fifthgener...
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University of Exeter
The University of Exeter is a public research university in Exeter, Devon, South West England, United Kingdom.