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Learning Cardiac Electrophysiology with Graph Neural Networks for Fast Data-Driven Personalised Predictions
Efficient modeling of cardiac electrophysiology is essential for advancing personalized medicine and improving treatment strategies. Traditional...
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Improving trust and confidence in medical skin lesion diagnosis through explainable deep learning
A key issue in critical contexts such as medical diagnosis is the interpretability of the deep learning models adopted in decision-making systems....
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Adversarial Sample Detection Through Neural Network Transport Dynamics
We propose a detector of adversarial samples that is based on the view of neural networks as discrete dynamic systems. The detector tells clean...
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Modelling spatiotemporal dynamics from Earth observation data with neural differential equations
Forecasting complex spatiotemporal dynamics is central in Earth science for modeling a variety of phenomena ranging from atmospheric dynamics to the...
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APHYN-EP: Physics-Based Deep Learning Framework to Learn and Forecast Cardiac Electrophysiology Dynamics
Biophysically detailed mathematical modeling of cardiac electrophysiology is often computationally demanding, for example, when solving problems for...
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Controlling hallucinations at word level in data-to-text generation
Data-to-Text Generation (DTG) is a subfield of Natural Language Generation aiming at transcribing structured data in natural language descriptions....
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Unsupervised domain adaptation with non-stochastic missing data
We consider unsupervised domain adaptation (UDA) for classification problems in the presence of missing data in the unlabelled target domain. More...
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Correction to: Feature Selection With Neural Networks
The article Feature Selection With Neural Networks.
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EP-Net 2.0: Out-of-Domain Generalisation for Deep Learning Models of Cardiac Electrophysiology
Cardiac electrophysiology models achieved good progress in simulating cardiac electrical activity. However, it is still challenging to leverage...
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Differentiable Feature Selection, A Reparameterization Approach
We consider the task of feature selection for reconstruction which consists in choosing a small subset of features from which whole data instances...
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A Principle of Least Action for the Training of Neural Networks
Neural networks have been achieving high generalization performance on many tasks despite being highly over-parameterized. Since classical...
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CycleGAN Through the Lens of (Dynamical) Optimal Transport
Unsupervised Domain Translation (UDT) is the problem of finding a meaningful correspondence between two given domains, without explicit pairings...
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A Hierarchical Model for Data-to-Text Generation
Transcribing structured data into natural language descriptions has emerged as a challenging task, referred to as “data-to-text”. These structures...
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Copy Mechanism and Tailored Training for Character-Based Data-to-Text Generation
In the last few years, many different methods have been focusing on using deep recurrent neural networks for natural language generation. The most...
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Contextualized Embeddings in Named-Entity Recognition: An Empirical Study on Generalization
Contextualized embeddings use unsupervised language model pretraining to compute word representations depending on their context. This is intuitively...
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Contextual bandits with hidden contexts: a focused data capture from social media streams
This paper addresses the problem of real time data capture from social media. Due to different limitations, it is not possible to collect all the...
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Real-time detection of driver distraction: random projections for pseudo-inversion-based neural training
There is an accumulating evidence that distracted driving is a leading cause of vehicle crashes and accidents. In order to support safe driving,...
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Spatio-temporal neural networks for space-time data modeling and relation discovery
We introduce a dynamical spatio-temporal model formalized as a recurrent neural network for modeling time series of spatial processes, i.e., series...
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Time Warp Invariant Dictionary Learning for Time Series Clustering: Application to Music Data Stream Analysis
This work proposes a time warp invariant sparse coding and dictionary learning framework for time series clustering, where both input samples and...
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EP-Net: Learning Cardiac Electrophysiology Models for Physiology-Based Constraints in Data-Driven Predictions
Cardiac electrophysiology (EP) models achieved good pro gress in simulating cardiac electrical activity. However numerical issues and computational...
