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Personalized Productive Engagement Recognition in Robot-Mediated Collaborative Learning

Publications associées (199)

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Models of Reward-Modulated Spike-Timing-Dependent Plasticity

Nicolas Frémaux

How do animals learn to repeat behaviors that lead to the obtention of food or other “rewarding” objects? As a biologically plausible paradigm for learning in spiking neural networks, spike-timing dependent plasticity (STDP) has been shown to perform well ...

EPFL2013

Learning Categories from Few Examples with Multi Model Knowledge Transfer

Barbara Caputo, Tatiana Tommasi

Learning a visual object category from few samples is a compelling and challenging problem. In several real-world applications collecting many annotated data is costly and not always possible. However a small training set does not allow to cover the high i ...

Idiap2013

Learning Separable Filters with Shared Parts

Bugra Tekin

Learned image features can provide great accuracy in many Computer Vision tasks. However, when the convolution filters used to learn image features are numerous and not separable, feature extraction becomes computationally demanding and impractical to use ...

2013

Real-time model learning using Incremental Sparse Spectrum Gaussian Process Regression

Novel applications in unstructured and non-stationary human environments require robots that learn from experience and adapt autonomously to changing conditions. Predictive models therefore not only need to be accurate, but should also be updated increment ...

2013

Autonomous reinforcement learning with experience replay

Ajay Kumar Tanwani

This paper considers the issues of efficiency and autonomy that are required to make reinforcement learning suitable for real-life control tasks. A real-time reinforcement learning algorithm is presented that repeatedly adjusts the control policy with the ...

Pergamon-Elsevier Science Ltd2013

Unsupervised adaptation for acceleration-based activity recognition: Robustness to sensor displacement and rotation

José del Rocio Millán Ruiz, Ricardo Andres Chavarriaga Lozano, Hamidreza Bayati

A common assumption in activity recognition is that the system remains unchanged between its design and its posterior operation. However, many factors affect the data distribution between two different experimental sessions. One of these factors is the pot ...

Springer Verlag2013

Non-Linear Domain Adaptation with Boosting

Pascal Fua, Carlos Joaquin Becker, Christos Marios Christoudias

A common assumption in machine vision is that the training and test samples are drawn from the same distribution. However, there are many problems when this assumption is grossly violated, as in bio-medical applications where different acquisitions can gen ...

2013

Modelling and Optimizing Mathematics Learning in Children

This study introduces a student model and control algorithm, optimizing mathematics learning in children. The adaptive system is integrated into a computer-based training system for enhancing numerical cognition aimed at children with developmental dyscalc ...

2013

Multi-round computation of type-threshold functions in collocated Gaussian networks

Michael Christoph Gastpar, Chien-Yi Wang

In wireless sensor networks, various applications involve learning one or multiple functions of the measurements observed by sensors, rather than the measurements themselves. This paper focuses on the computation of type-threshold functions which include t ...

2013

On Actively Teaching the Crowd to Classify

Is it possible to teach workers while crowdsourcing classification tasks? Amongst the challenges: (a) workers have different (unknown) skills, competence, and learning rate to which the teaching must be adapted, (b) feedback on the workers’ progress is lim ...

2013