Neural Tanget Kernel: Convergence and Generalization in Neural Networks

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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 ...

Deriving health indicators of rotating machines is crucial for their maintenance. However, this process is challenging for the prevalent adopted intelligent methods since they may take the whole data distributions, not only introducing noise interference b ...

We propose a local, non -intrusive model order reduction technique to accurately approximate the solution of coupled multi -component parametrized systems governed by partial differential equations. Our approach is based on the approximation of the boundar ...

Sharing data across institutions for genome-wide association studies (GWAS) would enhance the discovery of genetic variation linked to health and disease1,2. However, existing data-sharing regulations limit the scope of such collaborations3. Although crypt ...

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 ...

In this paper, we study sampling from a posterior derived from a neural network. We propose a new probabilistic model consisting of adding noise at every pre- and post-activation in the network, arguing that the resulting posterior can be sampled using an ...

Federated learning (FL) is a machine learning approach where nodes collaboratively train a global model. As more nodes participate in a round of FL, the effectiveness of individual model updates by nodes also diminishes. In this study, we increase the effe ...

Cyclic peptides are attractive for drug discovery due to their excellent binding properties and the potential to cross cell membranes. However, by far not all cyclic peptides are cell permeable, and measuring or predicting their membrane permeability is no ...

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 ...

Decentralized learning (DL) is an emerging approach that enables nodes to collaboratively train a machine learning model without sharing raw data. In many application domains, such as healthcare, this approach faces challenges due to the high level of hete ...

Dynamic aperture is an important concept for the study of non-linear beam dynamics in circular accelerators. It describes the extent of the phase-space region where a particle's motion remains bounded over a given number of turns. Understanding the feature ...

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 ...

The process of protein aggregation, central to neurodegenerative diseases like Huntington’s, is challenging to study due to its unpredictable nature and relatively rapid kinetics. Understanding its biomechanics is crucial for unraveling its role in disease ...

Turning pass-through network architectures into iterative ones, which use their own output as input, is a well-known approach for boosting performance. In this paper, we argue that such architectures offer an additional benefit: The convergence rate of the ...

We develop a model-free approach to optimally control stochastic, Markovian systems subject to a reach-avoid constraint. Specifically, the state trajectory must remain within a safe set while reaching a target set within a finite time horizon. Due to the t ...

Nuclear Magnetic Resonance (NMR) chemical shifts are powerful probes of local atomic and electronic structure that can be used to resolve the structures of powdered or amorphous molecular solids. Chemical shift driven structure elucidation depends critical ...

Topographical disorientation refers to the selective inability to orient oneself in familiar surroundings. However, to date its neural correlates remain poorly understood. Here we use quantitative lesion analysis and a lesion network mapping approach in or ...

Unmanned vehicles have become indispensable tools in various fields such as agriculture, environmental monitoring, and disaster response. A crucial component of these platforms is sensor fusion. As measurements are inherently noisy, certain variables canno ...

Neural Tanget Kernel: Convergence and Generalization in Neural Networks

Chattez avec Graph Search

Partial discharge localization in power transformer tanks using machine learning methods

Classifier-free diffusion-based weakly-supervised approach for health indicator derivation in rotating machines: Advancing early fault detection and condition monitoring

Model reduction of coupled systems based on non-intrusive approximations of the boundary response maps

Secure and federated genome-wide association studies for biobank-scale datasets

Leveraging Continuous Time to Understand Momentum When Training Diagonal Linear Networks

Gibbs sampling the posterior of neural networks

Harnessing Increased Client Participation with Cohort-Parallel Federated Learning

Bulk measurement of membrane permeability for random cyclic peptides in living cells to guide drug development

Performing and Detecting Backdoor Attacks on Face Recognition Algorithms

Fair Decentralized Learning

Optimizing Dynamic Aperture Studies with Active Learning

Aggregating Spatial and Photometric Context for Photometric Stereo

Self-driving microscopy detects the onset of protein aggregation and enables intelligent Brillouin imaging

Enabling Uncertainty Estimation in Iterative Neural Networks

A learning-based approach to stochastic optimal control under reach-avoid constraint

A Deep Learning Model for Chemical Shieldings in Molecular Organic Solids Including Anisotropy

The neural correlates of topographical disorientation-a lesion analysis study

Factor-graph Optimisation with Vehicle Dynamic Models for Mapping & Sensing Applications