most citedImproving Artificial Teachers by Considering How People Learn and Forget

12 citations · 14 across the 4 of their papers we have counts for

collaborators

5 papers

cs.CL20211 cited

Tackling Morphological Analogies Using Deep Learning -- Extended Version

Safa Alsaidi, Amandine Decker, Esteban Marquer +2

Analogical proportions are statements of the form "A is to B as C is to D". They constitute an inference tool that provides a logical framework to address learning, transfer, and e…

cs.CL2021

A Neural Approach for Detecting Morphological Analogies

Safa Alsaidi, Amandine Decker, Puthineath Lay +3

Analogical proportions are statements of the form "A is to B as C is to D" that are used for several reasoning and classification tasks in artificial intelligence and natural langu…

cs.CL2021

On the Transferability of Neural Models of Morphological Analogies

Safa Alsaidi, Amandine Decker, Puthineath Lay +3

Analogical proportions are statements expressed in the form "A is to B as C is to D" and are used for several reasoning and classification tasks in artificial intelligence and natu…

cs.HC202112 cited

Improving Artificial Teachers by Considering How People Learn and Forget

Aurélien Nioche, Pierre-Alexandre Murena, Carlos de la Torre-Ortiz +1

The paper presents a novel model-based method for intelligent tutoring, with particular emphasis on the problem of selecting teaching interventions in interaction with humans. Wher…

cs.LG20201 cited

Teaching to Learn: Sequential Teaching of Agents with Inner States

Mustafa Mert Celikok, Pierre-Alexandre Murena, Samuel Kaski

In sequential machine teaching, a teacher's objective is to provide the optimal sequence of inputs to sequential learners in order to guide them towards the best model. In this pap…