How to Tell Ancient Signs Apart? Recognizing and Visualizing Maya Glyphs with CNNs

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The capabilities of deep learning systems have advanced much faster than our ability to understand them. Whilst the gains from deep neural networks (DNNs) are significant, they are accompanied by a growing risk and gravity of a bad outcome. This is troubli ...

Cell segmentation is a critical step for quantitative single-cell analysis in microscopy images. Existing cell segmentation methods are often tailored to specific modalities or require manual interventions to specify hyper-parameters in different experimen ...

Monitoring forests, in particular their response to climate and land use change, requires studying long time scales. While efficient deep learning methods have been developed to process short time series of satellite imagery, leveraging long time series of ...

We present a finite elements-neural network approach for the numerical approximation of parametric partial differential equations. The algorithm generates training data from finite element simulations, and uses a data -driven (supervised) feedforward neura ...

Distributed learning is the key for enabling training of modern large-scale machine learning models, through parallelising the learning process. Collaborative learning is essential for learning from privacy-sensitive data that is distributed across various ...

The field of biometrics, and especially face recognition, has seen a wide-spread adoption the last few years, from access control on personal devices such as phones and laptops, to automated border controls such as in airports. The stakes are increasingly ...

Efficient sampling and approximation of Boltzmann distributions involving large sets of binary variables, or spins, are pivotal in diverse scientific fields even beyond physics. Recent advances in generative neural networks have significantly impacted this ...

Catastrophic overfitting (CO) in single-step adversarial training (AT) results in abrupt drops in the adversarial test accuracy (even down to 0%). For models trained with multi-step AT, it has been observed that the loss function behaves locally linearly w ...

This paper presents a comparison of machine learning (ML) methods used for three-dimensional localization of partial discharges (PD) in a power transformer tank. The study examines ML and deep learning (DL) methods, ranging from support vector machines (SV ...

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, ...

The performance of machine learning algorithms is conditioned by the availability of training datasets, which is especially true for the field of nondestructive evaluation. Here we propose one reconfigurable specimen instead of numerous reference specimens ...

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 ...

How to Tell Ancient Signs Apart? Recognizing and Visualizing Maya Glyphs with CNNs

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Safe Deep Neural Networks

The multimodality cell segmentation challenge: toward universal solutions

Multi-temporal forest monitoring in the Swiss Alps with knowledge-guided deep learning

Error assessment of an adaptive finite elements-neural networks method for an elliptic parametric PDE

Optimization Algorithms for Decentralized, Distributed and Collaborative Machine Learning

Performing and Detecting Backdoor Attacks on Face Recognition Algorithms

Sparse autoregressive neural networks for classical spin systems

Efficient local linearity regularization to overcome catastrophic overfitting

Partial discharge localization in power transformer tanks using machine learning methods

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

Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks

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