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A Bayesian Model of Individual Differences and Flexibility in Inductive Reasoning for Categorization of Examples

Publications associées (44)

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L2D 2021: First International Workshop on Enabling Data-Driven Decisions from Learning on the Web

Mirko Marras

By offering courses and resources, learning platforms on the Web have been attracting lots of participants, and the interactions with these systems have generated a vast amount of learning-related data. Their collection, processing and analysis have promot ...

Association for Computing Machinery2021

One of the challenges faced by conversational agents is their inability to identify unstated presumptions of their users' commands, a task trivial for humans due to their common sense. In this paper, we propose a zeroshot commonsense reasoning system for c ...

ASSOC COMPUTATIONAL LINGUISTICS-ACL2021

Federated Learning is explicitly designed for learning a global model from distributed, possibly sensitive non-i.i.d. data at different clients. In reality, this scenario is not always legible since in some cases the heterogeneous needs of clients cannot b ...

2021

Musical grammar describes a set of principles that are used to understand and interpret the structure of a piece according to a musical style. The main topic of this study is grammar induction for harmony --- the process of learning structural principles f ...

EPFL2020

Contextual semantic interpretability

Devis Tuia, Sylvain Lobry, Nicolas Courty

Convolutional neural networks (CNN) are known to learn an image representation that captures concepts relevant to the task, but do so in an implicit way that hampers model interpretability. However, one could argue that such a representation is hidden in t ...

LNCS2020

Learning is a complex, mostly invisible process that happens across spaces, occurring in the physical world but also increasingly in virtual worlds or web-based spaces. In order to explore what happens in such blended learning experience, there is a need ...

CEUR2017

While Ubiquitous Learning Environments (ULEs) have shown several benefits for learning, they pose challenges for orchestration. Teachers need to be aware of the learning process, which is difficult to achieve when it occurs across a heterogeneous set of sp ...

CEUR2017

Exploring students learning approaches in MOOCs

Louis Pierre Faucon

This study aims at understanding different students approaches for solving assignments in MOOCs. It makes use of a large dataset of logs from students interaction with the MOOC platform Coursera on a course about functional programming with Scala. In total ...

2017

metaSMT: focus on your application and not on solver integration

Mathias Soeken, Heinz Riener

Many applications from artificial intelligence and formal methods use decision procedures as their core solving engines. In this context, automated reasoning based on Satisfiability (SAT) or Satisfiability Modulo Theories (SMT) is very effective. For a giv ...

Springer Verlag2017

Decision making and planning with partial state information is a problem faced by all forms of intelligent entities. The formulation of a problem under partial state information leads to an exorbitant set of choices with associated probabilistic outcomes m ...

EPFL2016