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Beyond Knowledge Tracing. Modeling Skill Topologies with Bayesian Networks

Related publications (32)

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Mechanisms used in privacy-preserving machine learning often aim to guarantee differential privacy (DP) during model training. Practical DP-ensuring training methods use randomization when fitting model parameters to privacy-sensitive data (e.g., adding Ga ...

New York2023

Mechanical intelligence for learning embodied sensor-object relationships

Ahalya Prabhakar

Intelligence involves processing sensory experiences into representations useful for prediction. Understanding sensory experiences and building these contextual representations without prior knowledge of sensor models and environment is a challenging unsup ...

NATURE PORTFOLIO2022

Combining hypothesis- and data-driven neuroscience modeling in FAIR workflows

Daniel Keller

Modeling in neuroscience occurs at the intersection of different points of view and approaches. Typically, hypothesis-driven modeling brings a question into focus so that a model is constructed to investigate a specific hypothesis about how the system work ...

eLIFE SCIENCES PUBL LTD2022

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Vehicle trajectory prediction tasks have been commonly tackled from two distinct perspectives: either with knowledge-driven methods or more recently with data-driven ones. On the one hand, we can explicitly implement domain-knowledge or physical priors suc ...

2021

Lifelong Machine Learning with Data Efficiency and Knowledge Retention

Fei Mi

Artificial intelligence (AI) and machine learning (ML) have become de facto tools in many real-life applications to offer a wide range of benefits for individuals and our society. A classic ML model is typically trained with a large-scale static dataset in ...

EPFL2021

Optimal performance of civil infrastructure is an important aspect of liveable cities. A judicious combination of physics-based models with monitoring data in a validated methodology that accounts for uncertainties is explored in this paper. This methodolo ...

2021

Deep neural networks have been empirically successful in a variety of tasks, however their theoretical understanding is still poor. In particular, modern deep neural networks have many more parameters than training data. Thus, in principle they should over ...

EPFL2020

Back analysis can provide engineers with important information for better decision-making. Over the years, research on back analysis has focused mainly on optimisation techniques, while comparative studies of data-interpretation methodologies have seldom b ...

2020

The Learnability of the Grammar of Jazz: Bayesian Inference of Hierarchical Structures in Harmony

Daniel Harasim

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

Accurate and efficient interpretation of load-test data for asset-management

Sai Ganesh Sarvotham Pai

Increasing demand for new infrastructure and ageing of existing infrastructure has made management of infrastructure a key challenge of this century. Replacement of all ageing civil infrastructure is economically and environmentally unsustainable. Civil in ...

EPFL2019