Publications associées à Discovery learning

Robust Distributed Learning: Tight Error Bounds and Breakdown Point under Data Heterogeneity

Rachid Guerraoui, Youssef Allouah, Geovani Rizk, Nirupam Gupta, Rafaël Benjamin Pinot

The theory underlying robust distributed learning algorithms, designed to resist adversarial machines, matches empirical observations when data is homogeneous. Under data heterogeneity however, which is the norm in practical scenarios, established lower bo ...

2023

Multitask Learning Over Graphs: An Approach for Distributed, Streaming Machine Learning

Ali H. Sayed, Stefan Vlaski, Jie Chen, Roula Nassif

The problem of simultaneously learning several related tasks has received considerable attention in several domains, especially in machine learning, with the so-called multitask learning (MTL) problem, or learning to learn problem [1], [2]. MTL is an appro ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

About similar characteristics of visual perceptual learning and LTP

Michael Herzog, Carl Kristoffer Aberg

Perceptual learning is an implicit form of learning which induces long-lasting perceptual enhancements. Perceptual learning shows intriguing characteristics. For example, a minimal number of trials per session is needed for learning and the interleaved pre ...

2012

Classifying Material in the Real World

Barbara Caputo

Classifying materials from their appearance is challenging. Impressive results have been obtained under varying illumination and pose conditions. Still, the effect of scale variations and the possibility to generalise across different material samples are ...

2009

Eye-tracking perspectives of students’ learning trough MOOCs

Pierre Dillenbourg, Patrick Jermann, Kshitij Sharma, Himanshu Verma, Daniela Caballero Díaz

Activating student knowledge (ASK) before receiving learning materials improves their learning outcome (Tormey and LeDuc (2014)). We implement ASK through priming by using two versions of the same pretest in a dual eye-tracking study in a MOOC context. We ...

2014