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Agile, Versatile, and Comprehensive Social Media Platform for Creating, Sharing, Exploiting, and Archiving Personal Learning Spaces, Artifacts, and Traces

Publications associées (37)

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This book provides evidence-informed and practical advice on how to design, teach, and facilitate hands-on, experiential learning in practical higher education settings. With rich case studies and carefully considered analysis tasks, all underpinned by res ...

Routledge2021

Informative sample generation using class aware generative adversarial networks for classification of chest Xrays

Jean-Philippe Thiran

Training robust deep learning (DL) systems for disease detection from medical images is challenging due to limited images covering different disease types and severity. The problem is especially acute, where there is a severe class imbalance. We propose an ...

2019

Learning Approach to Delineation of Curvilinear Structures in 2D and 3D Images

Agata Justyna Mosinska

Detection of curvilinear structures has long been of interest due to its wide range of applications. Large amounts of imaging data could be readily used in many fields, but it is practically not possible to analyze them manually. Hence, the need for automa ...

EPFL2019

Tracing Teacher Collaborative Learning and Innovation Adoption: a Case Study in an Inquiry Learning Platform

Denis Gillet, Maria Jesus Rodriguez Triana, Luis Pablo Prieto Santos

Social processes play an important role in teachers' ongoing professional learning: through interactions with peers and experts to solve problems or co-create materials, teachers internalize knowledge developed in their communities. However, these social p ...

International Society of Learning Sciences2019

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Studying how neural circuits orchestrate limbed behaviors requires the precise measurement of the positions of each appendage in 3-dimensional (3D) space. Deep neural networks can estimate 2-dimensional (2D) pose in freely behaving and tethered animals. Ho ...

2019

Efficient Active Learning for Image Classification and Segmentation using a Sample Selection and Conditional Generative Adversarial Network

Jean-Philippe Thiran

Training robust deep learning (DL) systems for medical image classification or segmentation is challenging due to limited images covering different disease types and severity. We propose an active learning (AL) framework to select most informative samples ...

Springer2018

Active Interdisciplinary Learning in a Design Thinking Course: Going to Class for a Reason

Denis Gillet, Marc Laperrouza, Adrian Christian Holzer

New technologies such as MOOCs make it crucial for universities to find the added value of bringing students to campus. Designing lectures that foster more interactions could address this issue, as student engagement and interaction are harder to replicate ...

IEEE2018

Learning Active Learning from Data

Pascal Fua, Raphael Sznitman, Ksenia Konyushkova

In this paper, we suggest a novel data-driven approach to active learning (AL). The key idea is to train a regressor that predicts the expected error reduction for a candidate sample in a particular learning state. By formulating the query selection proced ...

2017

Active Learning and Proofreading for Delineation of Curvilinear Structures

Pascal Fua, Agata Justyna Mosinska, Jakub Tarnawski

Many state-of-the-art delineation methods rely on supervised machine learning algorithms. As a result, they require manually annotated training data, which is tedious to obtain. Furthermore, even minor classification errors may significantly affect the top ...

2017

We consider the problem of estimating the underlying graph associated with a Markov random field, with the added twist that the decoding algorithm can iteratively choose which subsets of nodes to sample based on the previous samples, resulting in an active ...

2017