Publications associées à Creating a Smart Learning Space: Learning with and learning from student-generated data about learning

Publications associées (12)

Sequential Representation Learning via Static-Dynamic Conditional Disentanglement

This paper explores self-supervised disentangled representation learning within sequential data, focusing on separating time-indep- endent and time-varying factors in videos. We propose a new model that breaks the usual independence assumption between thos ...

Springer Science and Business Media Deutschland GmbH2025

Structural equation models for multivariate extremes

Mario Krali

This thesis contributes to the modelling of multivariate rare events by combining extreme value theory and graphical modelling. The central theme involves structural equation models (SEMs), which are powerful tools used to express causal extremal dependenc ...

EPFL2025

Supplementary files for Machine learning for histological annotation and quantification of cortical layers

Olivier Burri, Julie Meystre

Creators Meystre Julie Olivier Burri Contributors Jean Jacquemier Description This dataset contains 7 QuPath projects. The raw data images linked to these projects and located in other Zenodo datasets need to be downloaded as well. The raw data contains im ...

Zenodo2024

Machine learning-aided generative molecular design

Philippe Schwaller, Jeff Guo

Machine learning has provided a means to accelerate early-stage drug discovery by combining molecule generation and filtering steps in a single architecture that leverages the experience and design preferences of medicinal chemists. However, designing mach ...

Nature Portfolio2024

An Efficient Machine Learning Model for Lightning Localization via Lightning-Induced Voltages on Transmission Lines

Farhad Rachidi-Haeri, Marcos Rubinstein, Hamidreza Karami

In this paper, three machine learning (ML)-based approaches—XGBoost, Artificial Neural Network (ANN), and Random Forest algorithms—are compared for the localization of lightning through the analysis of lightninginduced voltages on power transmission lines. ...

URSI – International Union of Radio Science2024

Learning to Remove Cuts in Integer Linear Programming

Volkan Cevher, Grigorios Chrysos, Efstratios Panteleimon Skoulakis

Cutting plane methods are a fundamental approach for solving integer linear programs (ILPs). In each iteration of such methods, additional linear constraints (cuts) are introduced to the constraint set with the aim of excluding the previous fractional opti ...

2024

A machine learning method for real-time numerical simulations of cardiac electromechanics

Alfio Quarteroni

We propose a machine learning-based method to build a system of differential equations that approximates the dynamics of 3D electromechanical models for the human heart, accounting for the dependence on a set of parameters. Specifically, our method permits ...

ELSEVIER SCIENCE SA2022

Supervised Feature Learning for Curvilinear Structure Segmentation

Pascal Fua, Vincent Lepetit, Roberto Rigamonti, Carlos Joaquin Becker

Springer2013

A Social Media Platform in Higher Education

Denis Gillet, Christophe Salzmann, Sandy Ingram, Na Li, Evgeny Bogdanov

This paper reports on the successful use of Graasp, a social media platform, by university students for their collaborative work. Graasp features a number of innovations, such as administrator-free creation of collaborative spaces, context-aware recommenda ...

2012

Enhancement of Geomatics Education by Using Internet Based Techniques

Bertrand Merminod, Pierre-Yves Gilliéron, Arnaud Deshogues

The Swiss Federal Institute of Technology in Lausanne (EPFL) has introduced the Moodle Learning Management System (LMS) in 2005. Since then, the number of teachers and users has increased rapidly. With more than 350 active courses hosted on Moodle for this ...

2010

Neuroevolution: from architectures to learning

Dario Floreano, Claudio Mattiussi, Peter Dürr

Artificial neural networks are applied to many real-world problems, ranging from pattern classification to robot control. In order to design a neural network for a particular task, the choice of an architecture (including the choice of a neuron model), and ...

2008

Learning, Self-Diagnosis and Multi-Objective Control of an Active Tensegrity Structure

Ian Smith, Bernard Adam

Springer2006