Complex Representation Learning with Graph Convolutional Networks for Knowledge Graph Alignment

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The increasing adoption of measurement units in electrical power distribution grids has enabled the deployment of data-driven and measurement-based control schemes. Such schemes rely on measurement-based estimated models, where the models are first estimat ...

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

2

-layer diagonal linear networks. This rudimentary architecture, which consists of a two layer feedforward linear network with a diagonal ...

Throughout history, the pace of knowledge and information sharing has evolved into an unthinkable speed and media. At the end of the XVII century, in Europe, the ideas that would shape the "Age of Enlightenment" were slowly being developed in coffeehouses, ...

In humans and animals, surprise is a physiological reaction to an unexpected event, but how surprise can be linked to plausible models of neuronal activity is an open problem. We propose a self-supervised spiking neural network model where a surprise signa ...

Modern neuroscience research is generating increasingly large datasets, from recording thousands of neurons over long timescales to behavioral recordings of animals spanning weeks, months, or even years. Despite a great variety in recording setups and expe ...

Many feedforward neural networks (NNs) generate continuous and piecewise-linear (CPWL) mappings. Specifically, they partition the input domain into regions on which the mapping is affine. The number of these so-called linear regions offers a natural metric ...

The occurrence of manufacturing defects in wind turbine blade (WTB) production can result in significant increases in operation and maintenance costs of WTBs, reduce capacity factors of wind farms, and occasionally lead to severe and disastrous consequence ...

Machine learning (ML) enables artificial intelligent (AI) agents to learn autonomously from data obtained from their environment to perform tasks. Modern ML systems have proven to be extremely effective, reaching or even exceeding human intelligence. Altho ...

Silicate glasses have played a major role as structural and functional materials in human civilization since ancient Egypt. Despite their widespread use and importance in modern society, silica glasses with complex geometries are only fabricated in automat ...

The integration of the new plasma position reflectometer in the RFX-mod2 experiment (the upgraded version of the previous RFX-mod that operated until 2015) is presented in this contribution. Particular attention has been devoted to the high field side subs ...

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, ...

Recently, cutting-edge brain-machine interfaces (BMIs) have revealed the potential of decoders such as recurrent neural networks (RNNs) in predicting attempted handwriting [1] or speech [2], enabling rapid communication recovery after paralysis. However, c ...

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 ...

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 ...

Accurate forecasting of photovoltaic (PV) power production is crucial for the integration of more renewable energy sources into the power grid. PV power production is highly intermittent, due to the stochastic cloud behaviour and cloud dynamics. Previous w ...

Complex Representation Learning with Graph Convolutional Networks for Knowledge Graph Alignment

Chattez avec Graph Search

Measurement - Based/Model-Less Estimation of Voltage Sensitivity Coefficients by Feedforward and LSTM Neural Networks in Power Distribution Grids

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Machine Learning for Modeling Stock Returns

Fast adaptation to rule switching using neuronal surprise

Robust machine learning for neuroscientific inference

On the number of regions of piecewise linear neural networks

Non-contact sensing for anomaly detection in wind turbine blades: A focus-SVDD with complex-valued auto-encoder approach

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

Multimaterial Volumetric Printing of Silica-Based Glasses

Technology challenges and integration of the plasma position reflectometer in RFX-mod2

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

33.3 MiBMI: A 192/512-Channel 2.46mm² Miniaturized Brain-Machine Interface Chipset Enabling 31-Class Brain-to-Text Conversion Through Distinctive Neural Codes

Performing and Detecting Backdoor Attacks on Face Recognition Algorithms

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

Network time series forecasting in photovoltaics power production