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

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Reflective writing is known as a useful method in learning sciences to improve the metacognitive skills of students. However, students struggle to structure their reflections properly, limiting the possible learning gains. Previous works in educational tec ...

Digital solutions play a key role for enabling blended learning in higher engineering education, where the acquisition and the appropriation of core competences and transversal skills require interactive resources offering a high-level and quasi-profession ...

Distributed learning has become a promising computational parallelism paradigm that enables a wide scope of intelligent applications from the Internet of Things (IoT) to autonomous driving and the healthcare industry. This paper studies distributed learnin ...

This work studies the learning process over social networks under partial and random information sharing. In traditional social learning models, agents exchange full belief information with each other while trying to infer the true state of nature. We stud ...

In this thesis we explore the applications of projective geometry, a mathematical theory of the relation between 3D scenes and their 2D images, in modern learning-based computer vision systems. This is an interesting research question which contradicts the ...

Distributed learning is the key for enabling training of modern large-scale machine learning models, through parallelising the learning process. Collaborative learning is essential for learning from privacy-sensitive data that is distributed across various ...

Modern neuroscience research is generating increasingly large datasets, from recording thousands of neurons over long timescales to behavioral recordings of animals spanning weeks, months, or even years. Despite a great variety in recording setups and expe ...

In the past few years, Machine Learning (ML) techniques have ushered in a paradigm shift, allowing the harnessing of ever more abundant sources of data to automate complex tasks. The technical workhorse behind these important breakthroughs arguably lies in ...

Machine learning (ML) enables artificial intelligent (AI) agents to learn autonomously from data obtained from their environment to perform tasks. Modern ML systems have proven to be extremely effective, reaching or even exceeding human intelligence. Altho ...

Bebras tasks are considered to develop Computational Thinking (CT) and are currently used for this purpose in many studies. However, the relationship between Bebras tasks and CT is recent and, given the scarcity of validated instruments for assessing CT th ...

In this thesis, we study two closely related directions: robustness and generalization in modern deep learning. Deep learning models based on empirical risk minimization are known to be often non-robust to small, worst-case perturbations known as adversari ...

Epilepsy, a major neurological disease, requires careful diagnosis and treatment. However, the detection of epileptic seizures remains a significant challenge. Current clinical practice relies on expert analysis of EEG signals, a process that is time-consu ...

Computer simulations are often used as support material for science education, as they can engage students through inquiry-based learning, promote their active interaction in the experimentation phase, and help them visualize abstract concepts. For instanc ...

Modern computing has enhanced our understanding of how social interactions shape collective behaviour in animal societies. Although analytical models dominate in studying collective behaviour, this study introduces a deep learning model to assess social in ...

In the rapidly evolving landscape of machine learning research, neural networks stand out with their ever-expanding number of parameters and reliance on increasingly large datasets. The financial cost and computational resources required for the training p ...

Monitoring forests, in particular their response to climate and land use change, requires studying long time scales. While efficient deep learning methods have been developed to process short time series of satellite imagery, leveraging long time series of ...

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 ...

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

Chattez avec Graph Search

Metacognition meets AI: Empowering reflective writing with large language models

SUPPORTING THE FULL LIFE CYCLE OF RICH OPEN EDUCATIONAL RESOURCES WITH A LEARNING EXPERIENCE PLATFORM

One-Bit Byzantine-Tolerant Distributed Learning via Over-The-Air Computation

Social Opinion Formation and Decision Making Under Communication Trends

Incorporating Projective Geometry into Deep Learning

Optimization Algorithms for Decentralized, Distributed and Collaborative Machine Learning

Robust machine learning for neuroscientific inference

Topics in statistical physics of high-dimensional machine learning

Few-shot Learning for Efficient and Effective Machine Learning Model Adaptation

A Bebras Computational Thinking (ABC-Thinking) program for primary school: Evaluation using the competent computational thinking test

Understanding generalization and robustness in modern deep learning

Resource-Efficient Continual Learning for Personalized Online Seizure Detection

Teacher-Mediated and Student-Led Interaction with a Physics Simulation: Effects on the Learning Experience

Predicting the long-term collective behaviour of fish pairs with deep learning

Random matrix methods for high-dimensional machine learning models

Multi-temporal forest monitoring in the Swiss Alps with knowledge-guided deep learning

A KL Divergence-Based Loss for In Vivo Ultrafast Ultrasound Image Enhancement with Deep Learning: Dataset (1/6)

A KL Divergence-Based Loss for In Vivo Ultrafast Ultrasound Image Enhancement with Deep Learning: Dataset (4/6)

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

A KL Divergence-Based Loss for In Vivo Ultrafast Ultrasound Image Enhancement with Deep Learning: Dataset (5/6)