Quantification of the suitable rooftop area for solar panel installation from overhead imagery using Convolutional Neural Networks

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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 ...

Recent developments in neural architecture search (NAS) emphasize the significance of considering robust architectures against malicious data. However, there is a notable absence of benchmark evaluations and theoretical guarantees for searching these robus ...

The desire and ability to place AI-enabled applications on the edge has grown significantly in recent years. However, the compute-, area-, and power-constrained nature of edge devices are stressed by the needs of the AI-enabled applications, due to a gener ...

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 ...

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, ...

Driven by the need for more efficient and seamless integration of physical models and data, physics -informed neural networks (PINNs) have seen a surge of interest in recent years. However, ensuring the reliability of their convergence and accuracy remains ...

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 ...

Despite the widespread empirical success of ResNet, the generalization properties of deep ResNet are rarely explored beyond the lazy training regime. In this work, we investigate scaled ResNet in the limit of infinitely deep and wide neural networks, of wh ...

Perovskite solar cells (PSCs) offer an attractive solution to increase the efficiency of PV panels via perovskite/silicon tandems thanks to their potential cost-effectiveness and high efficiency. In inverted PSC architecture (p-i-n), commonly used hole con ...

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 ...

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 ...

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 ...

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 ...

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 ...

Computational chemistry techniques offer tremendous potential for accelerating the discovery of sustainable chemical processes and reactions. However, the environmental impacts of the substantial computing power required for these digital methods are often ...

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 ...

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 ...

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 ...

To assess the meaningfulness of installing solar photovoltaics in buildings and infrastructures, we consider a carbon intensity (CI) balance perspective and assess whether installing PV at different orientations acts as a net CO2 sink or source, when compa ...

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 ...

Quantification of the suitable rooftop area for solar panel installation from overhead imagery using Convolutional Neural Networks

Chattez avec Graph Search

Robust machine learning for neuroscientific inference

Robust NAS under adversarial training: benchmark, theory, and beyond

Exploring High-Performance and Energy-Efficient Architectures for Edge AI-Enabled Applications

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

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

Residual-based attention in physics-informed neural networks

Partial discharge localization in power transformer tanks using machine learning methods

Generalization of Scaled Deep ResNets in the Mean-Field Regime

Identification and Mitigation of Degradation Mechanisms Induced by Heat and Light in NiOx-based Perovskite Solar Cells

Random matrix methods for high-dimensional machine learning models

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Performing and Detecting Backdoor Attacks on Face Recognition Algorithms

Efficient local linearity regularization to overcome catastrophic overfitting

Fast and Future: Towards Efficient Forecasting in Video Semantic Segmentation

Balancing computational chemistry's potential with its environmental impact

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

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

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

The carbon intensity of integrated photovoltaics

Advancing Self-Supervised Deep Learning for 3D Scene Understanding