Active Subdivision Surfaces for the Semiautomatic Segmentation of Biomedical Volumes

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Adiabatic quantum-flux parametron (AQFP) is an energy-efficient superconducting technology. Before physical design can be performed, AQFP technology mapping involves not only mapping logic into supported gate types but also legalizing the circuit to fulfil ...

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 ...

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 ...

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, ...

Photometric stereo, a computer vision technique for estimating the 3D shape of objects through images captured under varying illumination conditions, has been a topic of research for nearly four decades. In its general formulation, photometric stereo is an ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

In this work, we investigate the effect of momentum on the optimisation trajectory of gradient descent. We leverage a continuous-time approach in the analysis of momentum gradient descent with step size

\gamma

and momentum parameter

\beta

that allows u ...

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 ...

While momentum-based accelerated variants of stochastic gradient descent (SGD) are widely used when training machine learning models, there is little theoretical understanding on the generalization error of such methods. In this work, we first show that th ...

In this thesis, we study two closely related directions: robustness and generalization in modern deep learning. Deep learning models based on empirical risk minimization are known to be often non-robust to small, worst-case perturbations known as adversari ...

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 ...

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 ...

Assessing the individual risk of Major Adverse Cardiac Events (MACE) is of major importance as cardiovascular diseases remain the leading cause of death worldwide. Quantitative Myocardial Perfusion Imaging (MPI) parameters such as stress Myocardial Blood F ...

The transition from fossil-based to renewable energy sources requires the adoption of intermittent, decentralized energy generation technologies. Therefore, the integration of energy storage technologies is becoming increasingly important. The impact of op ...

The recent developments of deep learning cover a wide variety of tasks such as image classification, text translation, playing go, and folding proteins. All these successful methods depend on a gradient-based learning algorithm to train a model on massive ...

Active Subdivision Surfaces for the Semiautomatic Segmentation of Biomedical Volumes

Chattez avec Graph Search

Technology Legalization and Optimization for Adiabatic Quantum-Flux Parametron

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Topics in statistical physics of high-dimensional machine learning

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

Aggregating Spatial and Photometric Context for Photometric Stereo

Generalization of Scaled Deep ResNets in the Mean-Field Regime

Residual-based attention in physics-informed neural networks

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

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

Optimization Algorithms for Decentralized, Distributed and Collaborative Machine Learning

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

Leveraging Continuous Time to Understand Momentum When Training Diagonal Linear Networks

Fast and Future: Towards Efficient Forecasting in Video Semantic Segmentation

On the Generalization of Stochastic Gradient Descent with Momentum

Understanding generalization and robustness in modern deep learning

Robust machine learning for neuroscientific inference

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

Comparing various AI approaches to traditional quantitative assessment of the myocardial perfusion in [82Rb] PET for MACE prediction

Thermal Energy Storage in Multi-Energy System Optimization: How to Consider Temperatures?

Transformer Models for Vision