Ten Years of Research on Intelligent Educational Games for Learning Spelling and Mathematics
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Limb amputation is characterized by complex and intermingled brain reorganization processes combining sensorimotor deprivation induced by the loss of the limb per se, and compensatory behaviors, such as the over-use of the intact or remaining limb. While a ...
Characters do not convey meaning, but sequences of characters do. We propose an unsupervised distributional method to learn the abstract meaning-bearing units in a sequence of characters. Rather than segmenting the sequence, this model discovers continuous ...
Sequence modeling for signs and gestures is an open research problem. In thatdirection, there is a sustained effort towards modeling signs and gestures as a se-quence of subunits. In this paper, we develop a novel approach to infer movementsubunits in a da ...
We review the notion of a linearity-generating (LG) process introduced by Gabaix and relate LG processes to linear-rational (LR) models studied by Filipović et al. We show that every LR model can be represented as an LG process and vice versa. We find that ...
This paper proposes a representational model for image pairs such as consecutive video frames that are related by local pixel displacements, in the hope that the model may shed light on motion perception in primary visual cortex (V1). The model couples the ...
In this paper, we propose and compare personalized models for Productive Engagement (PE) recognition. PE is defined as the level of engagement that maximizes learning. Previously, in the context of robot-mediated collaborative learning, a framework of prod ...
Deep learning algorithms are responsible for a technological revolution in a variety oftasks including image recognition or Go playing. Yet, why they work is not understood.Ultimately, they manage to classify data lying in high dimension – a feat generical ...
Automated analyses of the outcome of a simulation have been an important part of atomistic modeling since the early days, addressing the need of linking the behavior of individual atoms and the collective properties that are usually the final quantity of i ...
In discrete choice modeling (DCM), model misspecifications may lead to limited predictability and biased parameter estimates. In this paper, we propose a new approach for estimating choice models in which we divide the systematic part of the utility specif ...
In several machine learning settings, the data of interest are well described by graphs. Examples include data pertaining to transportation networks or social networks. Further, biological data, such as proteins or molecules, lend themselves well to graph- ...