Publications associées à Acquiring musculoskeletal skills with curriculum-based reinforcement learning - model weights

Publications associées (22)

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

Nils Frédérik Heiden Olsen, Alexandre Massoud Alahi, Pascal Turberg

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 ...

2025

Comparison of Reservoir Computing topologies using the Recurrent Kernel approach

Jonathan Yuelin Dong

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 ...

2025

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

Jan Sickmann Hesthaven, Mariella Kast, Philipp Christoph Weder

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 ...

2025

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

Jan Sickmann Hesthaven

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 ...

2025

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

Zaffar Ahmed Shaikh

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 ...

Springer Science and Business Media LLC2025

Robust semantic learning for precise medical image segmentation

Seyedbehzad Bozorgtabar

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 ...

2025

Les Houches lectures on deep learning at large and infinite width

Vittorio Erba, Christian Keup

These lectures, presented at the 2022 Les Houches Summer School on Statistical Physics and Machine Learning, focus on the infinite-width limit and large-width regime of deep neural networks. Topics covered include the various statistical and dynamical prop ...

2024

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

Olga Fink, Ismail Nejjar

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 ...

Neural information processing systems foundation2024

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

Alice Julie Bruel

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 ...

EPFL2024

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

Silvestro Micera, Fiorenzo Artoni, Vincent Alexandre Mendez

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 ...

2024

The inductive bias of deep learning: Connecting weights and functions

Guillermo Ortiz Jimenez

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 ...

EPFL2023

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

Frédéric Kaplan, Isabella Di Lenardo, Rémi Guillaume Petitpierre, Beatrice Vaienti

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 ...

2023

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

David Julian Gonon

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 ...

EPFL2023

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

Volkan Cevher, Fanghui Liu, Luca Viano

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 ...

2022

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

Michele Ceriotti, Anne-Clémence Corminboeuf, Alberto Fabrizio, Benjamin André René Meyer, Raimon Fabregat I De Aguilar-Amat, Edgar Albert Engel, Veronika Juraskova

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 ...

2022

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

Alexandre Schmid

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 ...

IEEE2021

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

He Xu

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 ...

EPFL2020

Second-Order Guarantees In Federated Learning

Ali H. Sayed, Stefan Vlaski, Elsa Rizk

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 ...

IEEE2020

Deep Learning for Logic Optimization Algorithms

Giovanni De Micheli, Sabine Süsstrunk, Frédéric Kaplan, Mathias Soeken, Winston Jason Haaswijk, Benoît Laurent Auguste Seguin, Edo Collins

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 ...

IEEE2018

Unsupervised Learning of Phase-Change-Based Neuromorphic Systems

Stanislaw Andrzej Wozniak

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 ...

EPFL2017