Enhancing wearable sensor data analysis for patient health monitoring using allied data disparity technique and multi instance ensemble perceptron learning

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Reservoir Computing (RC) has become popular in recent years thanks to its fast and efficient computational capabilities. Standard RC has been shown to be equivalent in the asymptotic limit to Recurrent Kernels, which helps in analyzing its expressive power ...

Rock cuttings from destructive boreholes are a common and cheaper source of drilling materials that can be used to determine underground geology compared to rock core samples. Classifying manually the series of cuttings can be a long and tedious process an ...

We investigate reduced order models for acoustic and electromagnetic wave problems in parametrically defined domains. The parameter-to-solution maps are approximated following the so-called Galerkin POD-NN method, which combines the construction of a reduc ...

Hyperbolic conservation laws play a critical role in various fields, including aerodynamics, physics, and oceanography. However, traditional physics-informed neural networks (PINNs), despite their remarkable capabilities in solving partial differential equ ...

Precisely localizing anomalies in medical images remains a significant challenge due to their heterogeneous nature across modalities and organs. While initial efforts excelled in identifying prominent anomalies, detecting minute target lesions posed signif ...

Regression models often fail to generalize effectively in regions characterized by highly imbalanced label distributions. Previous methods for deep imbalanced regression rely on gradient-based weight updates, which tend to overfit in underrepresented regio ...

Generating large omics datasets has become routine for gaining insights into cellular processes, yet deciphering these datasets to determine metabolic states remains challenging. Kinetic models can help integrate omics data by explicitly linking metabolite ...

Here we provide the weights of the neural network policies used for the analysis presented in our article. The archives whose names start with a number (01 - 32) correspond to the 32 curriculum steps to train the Baoding Balls policy which ranked first at ...

Human movements emerge from complex interactions between the musculoskeletal system, the neural system and the external environment. Their control is realised through a hierarchical neural system composed of the brain, the spinal cord (SC), and remaining n ...

The ability to collect and store ever more massive data, unlabeled in many cases, has been accompanied by the need to process them efficiently in order to extract relevant information and possibly design solutions based on the latter. In various situations ...

Years of a fierce competition have naturally selected the fittest deep learning algorithms. Yet, although these models work well in practice, we still lack a proper characterization of why they do so. This poses serious questions about the robustness, trus ...

In recent decades, we have witnessed great advances on the Internet of Things, mobile devices, sensor-based systems, and resulting big data infrastructures, which have gradually, yet fundamentally influenced the way people interact with and in the digital ...

The thesis at hand is concerned with robots' navigation in human crowds. Specifically, methods are developed for planning a mobile robot's local motion between pedestrians, and they are evaluated in experiments where a robot interacts with real pedestrians ...

The generation of 3D models depicting cities in the past holds great potential for documentation and educational purposes. However, it is often hindered by incomplete historical data and the specialized expertise required. To address these challenges, we p ...

This paper provides a theoretical study of deep neural function approximation in reinforcement learning (RL) with the ϵ-greedy exploration under the online setting. This problem setting is motivated by the successful deep Q-networks (DQN) framework that fa ...

The application of machine learning to theoretical chemistry has made it possible to combine the accuracy of quantum chemical energetics with the thorough sampling of finite-temperature fluctuations. To reach this goal, a diverse set of methods has been pr ...

Many fields make use nowadays of machine learning (ML) enhanced applications for cost optimization, scheduling or forecasting, in- cluding the energy sector. However, these very ML algorithms consume a significant amount of energy, sometimes going against ...

Recent years have seen growing interest in leveraging deep learning models for monitoring epilepsy patients based on electroencephalographic (EEG) signals. However, these approaches often exhibit poor generalization when applied outside of the setting in w ...

A major goal shared by neuroscience and collective behavior is to understand how dynamic interactions between individual elements give rise to behaviors in populations of neurons and animals, respectively. This goal has recently become within reach, thanks ...

Enhancing wearable sensor data analysis for patient health monitoring using allied data disparity technique and multi instance ensemble perceptron learning

Chattez avec Graph Search

Comparison of Reservoir Computing topologies using the Recurrent Kernel approach

X-ray Micro-CT based characterization of rock cuttings with deep learning

Galerkin neural network-POD for acoustic and electromagnetic wave propagation in parametric domains

A coordinate transformation-based physics-informed neural networks for hyperbolic conservation laws

Robust semantic learning for precise medical image segmentation

IM-Context: In-Context Learning for Imbalanced Regression Tasks

Generative machine learning produces kinetic models that accurately characterize intracellular metabolic states

Acquiring musculoskeletal skills with curriculum-based reinforcement learning - model weights

Investigation of the role of spinal circuits in human motor control, from healthy to rehabilitation conditions

Learning to rank anomalies: scalar performance criteria and maximization of rank statistics

The inductive bias of deep learning: Connecting weights and functions

Artificial intelligence and visual analytics in geographical space and cyberspace: Research opportunities and challenges

Geometric and Learning Methods for Robots to Navigate in Human Crowds with Application to Smart Mobility Devices

Machine-Learning-Enhanced Procedural Modeling for 4D Historical Cities Reconstruction

Understanding Deep Neural Function Approximation in Reinforcement Learning via ϵ-Greedy Exploration

Local Kernel Regression and Neural Network Approaches to the Conformational Landscapes of Oligopeptides

On the advantages of P2P ML on mobile devices

M2D2: Maximum-Mean-Discrepancy Decoder for Temporal Localization of Epileptic Brain Activities

Connecting the dots in ethology: applying network theory to understand neural and animal collectives