A Generalized Representer Theorem for Hilbert Space - Valued Functions

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Modern optimization is tasked with handling applications of increasingly large scale, chiefly due to the massive amounts of widely available data and the ever-growing reach of Machine Learning. Consequently, this area of research is under steady pressure t ...

Much of our knowledge about the brain is derived from extracellular recordings of the electric fields produced by neural activity. However, interpretation of these signals is nontrivial. In par- ticular, it is difficult to link macro-scale observations of ...

In this paper we will consider distributed Linear-Quadratic Optimal Control Problems dealing with Advection-Diffusion PDEs for high values of the Peclet number. In this situation, computational instabilities occur, both for steady and unsteady cases. A Str ...

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 ...

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 ...

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 ...

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 thesis, we propose to formally derive amplitude equations governing the weakly nonlinear evolution of non-normal dynamical systems, when they respond to harmonic or stochastic forcing, or to an initial condition. This approach reconciles the non-mo ...

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 ...

Current transformer-based skeletal action recognition models tend to focus on a limited set of joints and low-level motion patterns to predict action classes. This results in significant performance degradation under small skeleton perturbations or changin ...

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 remarkable ability of deep learning (DL) models to approximate high-dimensional functions from samples has sparked a revolution across numerous scientific and industrial domains that cannot be overemphasized. In sensitive applications, the good perform ...

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 ...

Earth scientists study a variety of problems with remote sensing data, but they most often consider them in isolation from each other, which limits information flows across disciplines. In this work, we present METEOR, a meta-learning methodology for Earth ...

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, ...

A Generalized Representer Theorem for Hilbert Space - Valued Functions

Chattez avec Graph Search

Scalable constrained optimization

Coupled Electromagnetic-Electrophysiological Modeling of Neural Circuits

A Streamline Upwind Petrov-Galerkin Reduced Order Method for Advection-Dominated Partial Differential Equations Under Optimal Control

Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks

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

Generalization of Scaled Deep ResNets in the Mean-Field Regime

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

Non-normal forms

Social Opinion Formation and Decision Making Under Communication Trends

MaskCLR: Attention-Guided Contrastive Learning for Robust Action Representation Learning

Safe Deep Neural Networks

Towards Trustworthy Deep Learning for Image Reconstruction

Performing and Detecting Backdoor Attacks on Face Recognition Algorithms

Meta-learning to address diverse Earth observation problems across resolutions

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