Towards Trustworthy Deep Learning for Image Reconstruction

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Deep learning has revolutionized the field of computer vision, a success largely attributable to the growing size of models, datasets, and computational power. Simultaneously, a critical pain point arises as several computer vision applications are deploye ...

Predicting the evolution of systems with spatio-temporal dynamics in response to external stimuli is essential for scientific progress. Traditional equations-based approaches leverage first principles through the numerical approximation of differential equ ...

The upcoming Square Kilometre Array Observatory will produce images of neutral hydrogen distribution during the epoch of reionization by observing the corresponding 21-cm signal. However, the 21-cm signal will be subject to instrumental limitations such as ...

Example Data for DeepReefMap This dataset contains input videos in MP4 format taken with GoPro Hero 10 Cameras in Reefs in the Red Sea to demonstrate the DeepReefMap tool, which is described in the paper "Scalable Semantic 3D Mapping of Coral Reefs with De ...

Clustering in education, particularly in large-scale online environments like MOOCs, is essential for understanding and adapting to diverse student needs. However, the effectiveness of clustering depends on its interpretability, which becomes challenging w ...

Fluorescence lifetime imaging (FLI) has been receiving increased attention in recent years as a powerful diagnostic technique in biological and medical research. However, existing FLI systems often suffer from a tradeoff between processing speed, accuracy, ...

In this PhD manuscript, we explore optimisation phenomena which occur in complex neural networks through the lens of

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The combination of several interesting characteristics makes metal-organic frameworks (MOFs) a highly sought-after class of nanomaterials for a broad range of applications like gas storage and separation, catalysis, drug delivery, and so on. However, the e ...

The present work proposes a framework for nonlinear model order reduction based on a Graph Convolutional Autoencoder (GCA-ROM). In the reduced order modeling (ROM) context, one is interested in obtaining real -time and many-query evaluations of parametric ...

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Modern computing has enhanced our understanding of how social interactions shape collective behaviour in animal societies. Although analytical models dominate in studying collective behaviour, this study introduces a deep learning model to assess social in ...

This dataset contains a collection of ultrafast ultrasound acquisitions from nine volunteers and the CIRS 054G phantom. For a comprehensive understanding of the dataset, please refer to the paper: Viñals, R.; Thiran, J.-P. A KL Divergence-Based Loss for In ...

Deep learning has emerged as a promising avenue for automatic mapping, demonstrating high efficacy in land cover categorization through various semantic segmentation models. Nonetheless, the practical deployment of these models encounters important challen ...

A range of behavioral and contextual factors, including eating and drinking behavior, mood, social context, and other daily activities, can significantly impact an individual's quality of life and overall well-being. Therefore, inferring everyday life aspe ...

In inverse problems, the task is to reconstruct an unknown signal from its possibly noise-corrupted measurements. Penalized-likelihood-based estimation and Bayesian estimation are two powerful statistical paradigms for the resolution of such problems. They ...

Towards Trustworthy Deep Learning for Image Reconstruction

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Fast and Future: Towards Efficient Forecasting in Video Semantic Segmentation

Learning the intrinsic dynamics of spatio-temporal processes through Latent Dynamics Networks

Deep learning approach for identification of H II regions during reionization in 21-cm observations - II. Foreground contamination

Data for Paper "Scalable Semantic 3D Mapping of Coral Reefs with Deep Learning"

Interpret3C: Interpretable Student Clustering Through Individualized Feature Selection

Coupling a recurrent neural network to SPAD TCSPC systems for real-time fluorescence lifetime imaging

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Inverse design of metal-organic frameworks for direct air capture of CO2via deep reinforcement learning

A graph convolutional autoencoder approach to model order reduction for parametrized PDEs

Few-shot Learning for Efficient and Effective Machine Learning Model Adaptation

Resource-Efficient Continual Learning for Personalized Online Seizure Detection

Predicting the long-term collective behaviour of fish pairs with deep learning

A KL Divergence-Based Loss for In Vivo Ultrafast Ultrasound Image Enhancement with Deep Learning: Dataset (1/6)

Land Cover Mapping From Multiple Complementary Experts Under Heavy Class Imbalance

Generalization and Personalization of Machine Learning for Multimodal Mobile Sensing in Everyday Life

A KL Divergence-Based Loss for In Vivo Ultrafast Ultrasound Image Enhancement with Deep Learning: Dataset (2/6)

Statistical Inference for Inverse Problems: From Sparsity-Based Methods to Neural Networks

A KL Divergence-Based Loss for In Vivo Ultrafast Ultrasound Image Enhancement with Deep Learning: Dataset (3/6)