Planned, taught, learnt: analysis of transversal skills through curriculum using portfolio

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Artificial intelligence, particularly the subfield of machine learning, has seen a paradigm shift towards data-driven models that learn from and adapt to data. This has resulted in unprecedented advancements in various domains such as natural language proc ...

This dataset contains a collection of ultrafast ultrasound acquisitions from nine volunteers and the CIRS 054G phantom. For a comprehensive understanding of the dataset, please refer to the paper: Viñals, R.; Thiran, J.-P. A KL Divergence-Based Loss for In ...

Superresolution T2-weighted fetal-brain magnetic-resonance imaging (FBMRI) traditionally relies on the availability of several orthogonal low-resolution series of 2-dimensional thick slices (volumes). In practice, only a few low-resolution volumes are acqu ...

Earth scientists study a variety of problems with remote sensing data, but they most often consider them in isolation from each other, which limits information flows across disciplines. In this work, we present METEOR, a meta-learning methodology for Earth ...

Recent advancements in deep learning have revolutionized 3D computer vision, enabling the extraction of intricate 3D information from 2D images and video sequences. This thesis explores the application of deep learning in three crucial challenges of 3D com ...

Satellite remote sensing has become a key technology for monitoring Earth and the processes occurring at its surface. It relies on state-of-the-art machine learning models that require large annotated datasets to capture the extreme diversity of the proble ...

Simulation is a powerful approach that plays a significant role in science and technology. Computational models that simulate learner interactions and data hold great promise for educational technology as well. Amongst others, simulated learners can be use ...

Finding optimal bidding strategies for generation units in electricity markets would result in higher profit. However, it is a challenging problem due to the system uncertainty which is due to the lack of knowledge of the strategies of other generation uni ...

Natural language processing and other artificial intelligence fields have witnessed impressive progress over the past decade. Although some of this progress is due to algorithmic advances in deep learning, the majority has arguably been enabled by scaling ...

There is a growing recognition that electronic band structure is a local property of materials and devices, and there is steep growth in capabilities to collect the relevant data. New photon sources, from small-laboratory-based lasers to free electron lase ...

Incomplete labels are common in multi-task learning for biomedical applications due to several practical difficulties, e.g., expensive annotation efforts by experts, limit of data collection, different sources of data. A naive approach to enable joint lear ...

Background Early exposure to Computer Science (CS) and Computational Thinking (CT) for all is critical to broaden participation and promote equity in the field. But how does the introduction of CS and CT into primary school curricula impact learning, perce ...

What conceptions do teachers hold about learning activities to develop students’ transversal skills? This qualitative exploration at a research-intensive engineering school draws on interviews and focus groups to explore teachers’ ideas about developing in ...

Over the past few decades, the debates have shifted from whether to how Computer Science (CS) should be introduced into formal education. Given the diverse ways to introduce CS into formal education, and the struggles many countries have faced, considerabl ...

Planned, taught, learnt: analysis of transversal skills through curriculum using portfolio

Chattez avec Graph Search

Breaking the Curse of Dimensionality in Deep Neural Networks by Learning Invariant Representations

A KL Divergence-Based Loss for In Vivo Ultrafast Ultrasound Image Enhancement with Deep Learning: Dataset (3/6)

A KL Divergence-Based Loss for In Vivo Ultrafast Ultrasound Image Enhancement with Deep Learning: Dataset (2/6)

SELF-SUPERVISED ISOTROPIC SUPERRESOLUTION FETAL BRAIN MRI

Meta-learning to address diverse Earth observation problems across resolutions

Advancing Self-Supervised Deep Learning for 3D Scene Understanding

Meteor: Meta-learning connecting earth problems observed from space

Simulated Learners in Educational Technology: A Systematic Literature Review and a Turing-like Test

Multi-agent reinforcement learning with graph convolutional neural networks for optimal bidding strategies of generation units in electricity markets

Text Representation Learning for Low Cost Natural Language Understanding

Transfer learning application of self-supervised learning in ARPES

MULTI-TASK CURRICULUM LEARNING FOR PARTIALLY LABELED DATA

How are primary school computer science curricular reforms contributing to equity? Impact on student learning, perception of the discipline, and gender gaps

Are engineering teachers ready to leverage the power of play to teach transversal skills?

Co-constructing an Effective, Sustainable and Scalable Primary School Computer Science Curricular Reform