PePTM: An Efficient and Accurate Personalized P2P Learning Algorithm for Home Thermal Modeling

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Buildings play a pivotal role in the ongoing worldwide energy transition, accounting for 30% of the global energy consumption. With traditional engineering solutions reaching their limits to tackle such large-scale problems, data-driven methods and Machine ...

In this thesis we explore the applications of projective geometry, a mathematical theory of the relation between 3D scenes and their 2D images, in modern learning-based computer vision systems. This is an interesting research question which contradicts the ...

Healthcare sector buildings, particularly hospitals, significantly contribute to global energy consumption and greenhouse gas emissions. Hospitals require a substantial amount of steam, including the essential hygienic or " clean steam, " that is currently ...

The scientific progress is significantly transforming contemporary society with the introduction and widespread application of technologies like artificial intelligence and quantum computing. Despite their profound impact, these technologies necessitate en ...

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 ...

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 aluminium sector relies on natural gas for the conversion of recycled scrap into new feedstock, which results in substantial atmospheric emissions. Hydric resources are also impacted, as they serve as heat sinks for the waste heat generated during the ...

Mitigating the energy requirements of artificial intelligence requires novel physical substrates for computation. Phononic metamaterials have vanishingly low power dissipation and hence are a prime candidate for green, always-on computers. However, their u ...

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 ...

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 ...

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, ...

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 ...

Energy piles represent an innovative technology that can help provide sustainable geothermal heating or cooling energy for thermal conditioning purposes. In hot-dominated climates, the interest is to inject heat in the ground and extract energy for space-c ...

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 ability to reason, plan and solve highly abstract problems is a hallmark of human intelligence. Recent advancements in artificial intelligence, propelled by deep neural networks, have revolutionized disciplines like computer vision and natural language ...

This work studies the learning process over social networks under partial and random information sharing. In traditional social learning models, agents exchange full belief information with each other while trying to infer the true state of nature. We stud ...

In the rapidly evolving landscape of machine learning research, neural networks stand out with their ever-expanding number of parameters and reliance on increasingly large datasets. The financial cost and computational resources required for the training p ...

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 ...

Frontal polymerisation has the potential to bring unprecedented reductions in energy demand and process time to produce fibre reinforced polymer composites. Production of epoxy-based fibre reinforced polymer parts with high fibre volume content, commonly e ...

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 ...

PePTM: An Efficient and Accurate Personalized P2P Learning Algorithm for Home Thermal Modeling

Chattez avec Graph Search

Fusing Pre-existing Knowledge and Machine Learning for Enhanced Building Thermal Modeling and Control

Incorporating Projective Geometry into Deep Learning

Solar-assisted clean steam generator, a cleaner production approach for sustainable healthcare facilities

Electrical and Optical Manifestations of Flat Band Physics in Van der Waals Materials

Topics in statistical physics of high-dimensional machine learning

Optimization Algorithms for Decentralized, Distributed and Collaborative Machine Learning

Renewable energy integration and waste heat valorization in aluminum remelting for co-producing kerosene and methanol

In-Sensor Passive Speech Classification with Phononic Metamaterials

Safe Deep Neural Networks

Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks

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

Robust machine learning for neuroscientific inference

Performance of energy piles foundation in hot-dominated climate: A case study in Dubai

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Infusing structured knowledge priors in neural models for sample-efficient symbolic reasoning

Social Opinion Formation and Decision Making Under Communication Trends

Random matrix methods for high-dimensional machine learning models

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

Self-catalysed frontal polymerisation enables fast and low-energy processing of fibre reinforced polymer composites

Network time series forecasting in photovoltaics power production