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

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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 ...

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 ...

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 ...

Wearable Sensor (WS)-based monitoring systems detect minute patient movements/ demands and abnormalities through periodic sensing and imaging. Sensor data observed over different intervals is not constant or available based on operating sequences. Due to v ...

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 ...

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 ...

A computer-implemented method of training a neural network for generating upper or lower limb motion control signals to operate a virtual, robotic or prosthetic upper or lower limb, comprising providing or receiving, at a neural network, electromyographic ...

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 ...

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 ...

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 ...

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 ...

Wearable solutions based on Deep Learning (DL) for real-time ECG monitoring are a promising alternative to detect life-threatening arrhythmias. However, DL models suffer of a large memory footprint, which hampers their adoption in portable technologies. Th ...

In reinforcement learning (RL), an agent makes sequential decisions to maximise the reward it can obtain from an environment. During learning, the actual and expected outcomes are compared to tell whether a decision was good or bad. The difference between ...

Federated learning is a useful framework for centralized learning from distributed data under practical considerations of heterogeneity, asynchrony, and privacy. Federated architectures are frequently deployed in deep learning settings, which generally giv ...

The slowing down of Moore's law and the emergence of new technologies puts an increasing pressure on the field of EDA. There is a constant need to improve optimization algorithms. However, finding and implementing such algorithms is a difficult task, espec ...

Neuromorphic systems provide brain-inspired methods of computing. In a neuromorphic architecture, inputs are processed by a network of neurons receiving operands through synaptic interconnections, tuned in the process of learning. Neurons act simultaneousl ...

Earthquake loads can induce out-of-plane deformations in thin reinforced concrete (RC) walls that may lead to member out-of-plane failure or cause a premature in-plane collapse. Such instability phenomena have been observed after the recent earthquakes in ...

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

Chattez avec Graph Search

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

Comparison of Reservoir Computing topologies using the Recurrent Kernel approach

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

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

Robust semantic learning for precise medical image segmentation

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

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

Wearable electromyographic device and system and methods for motion decoding of body parts

The inductive bias of deep learning: Connecting weights and functions

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

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

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

Hardware-oriented pruning and quantization of Deep Learning models to detect life-threatening arrhythmias

Neural Correlates of Reinforcement Learning: eligibility trace, reward prediction error, novelty and surprise

Second-Order Guarantees In Federated Learning

Deep Learning for Logic Optimization Algorithms

Unsupervised Learning of Phase-Change-Based Neuromorphic Systems

Out-of-plane behaviour of reinforced concrete members with single reinforcement layer subjected to cyclic axial loading: beam-column element simulation