Classification of Tropical Deforestation Drivers with Machine Learning and Satellite Image Time Series

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Recent years have been marked with the fast-pace diversification and increasing ubiquity of machine learning (ML) applications. Yet, a firm theoretical understanding of the surprising efficiency of neural networks (NNs) to learn from high-dimensional data ...

Reduced-order models (ROMs) are widely used in scientific computing to tackle high-dimensional systems. However, traditional ROM methods may only partially capture the intrinsic geometric characteristics of the data. These characteristics encompass the und ...

To date, several methods have been developed to explain deep learning algorithms for classification tasks. Recently, an adaptation of two of such methods has been proposed to generate instance-level explainable maps in a semantic segmentation scenario, suc ...

Approximate multipliers (AppMults) are widely employed in deep neural network (DNN) accelerators to reduce the area, delay, and power consumption. However, the inaccuracies of AppMults degrade DNN accuracy, necessitating a retraining process to recover acc ...

The rates of tropical deforestation remain high, resulting in carbon emissions, biodiversity loss, and impacts on local communities. To design effective policies to tackle this, it is necessary to know what the drivers behind deforestation are. Since drive ...

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 ...

Machine-learning models based on a point-cloud representation of a physical object are ubiquitous in scientific applications and particularly well-suited to the atomic-scale description of molecules and materials. Among the many different approaches that h ...

Koopmans spectral functionals are a powerful extension of Kohn-Sham density-functional theory (DFT) that enables the prediction of spectral properties with state-of-the-art accuracy. The success of these functionals relies on capturing the effects of elect ...

The global construction industry contributes to 37% of carbon emissions associated to both building operations and construction. To help achieve the net-zero targets set by 2050, it is mandated to achieve a 50% reduction in carbon emissions by 2030. As we ...

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 ...

Cross-view geo-localization aims at localizing a ground-level query image by matching it to its corresponding geo-referenced aerial view. In real-world scenarios, the task requires accommodating diverse ground images captured by users with varying orientat ...

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 the past few years, Machine Learning (ML) techniques have ushered in a paradigm shift, allowing the harnessing of ever more abundant sources of data to automate complex tasks. The technical workhorse behind these important breakthroughs arguably lies in ...

Recent advancements in deep learning have revolutionized 3D computer vision, enabling the extraction of intricate 3D information from 2D images and video sequences. This thesis explores the application of deep learning in three crucial challenges of 3D com ...

Permanent CO storage in basalts through mineralisation offers a promising solution for reducing carbon emissions and mitigating climate change. This study focuses on the impact of potential mineralisation on the flow properties of the basaltic material. Fl ...

Epilepsy, a major neurological disease, requires careful diagnosis and treatment. However, the detection of epileptic seizures remains a significant challenge. Current clinical practice relies on expert analysis of EEG signals, a process that is time-consu ...

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

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

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 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 ...

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 ...

Classification of Tropical Deforestation Drivers with Machine Learning and Satellite Image Time Series

Chattez avec Graph Search

High-dimensional learning of narrow neural networks

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Exploiting XAI Maps to Improve MS Lesion Segmentation and Detection in MRI

Gradient Approximation of Approximate Multipliers for High-Accuracy Deep Neural Network Retraining

Mapping drivers of tropical forest loss with satellite image time series and machine learning

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

Wigner kernels: Body-ordered equivariant machine learning without a basis

Predicting electronic screening for fast Koopmans spectral functional calculations

An explorative method to support design decisions based on carbon constraints and daylight sufficiency needs

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

ConGeo: Robust Cross-View Geo-Localization Across Ground View Variations

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

Topics in statistical physics of high-dimensional machine learning

Advancing Self-Supervised Deep Learning for 3D Scene Understanding

Impact of CO2-rich seawater injection on the flow properties of basalts

Resource-Efficient Continual Learning for Personalized Online Seizure Detection

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

Safe Deep Neural Networks

Partial discharge localization in power transformer tanks using machine learning methods