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Metacognition meets AI: Empowering reflective writing with large language models

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Publications associées (22)

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Source-Free Domain-Invariant Performance Prediction

Mathieu Salzmann

Accurately estimating model performance poses a significant challenge, particularly in scenarios where the source and target domains follow different data distributions. Most existing performance prediction methods heavily rely on the source data in their ...

Springer Nature2025

Towards multimodal foundation models in molecular cell biology

Maria Brbic

The rapid advent of high-throughput omics technologies has created an exponential growth in biological data, often outpacing our ability to derive molecular insights. Large-language models have shown a way out of this data deluge in natural language proces ...

2025

Deep Neural Networks: Large-Width Behavior and Generalization Bounds

Evgenii Golikov

During several decades, neural network architectures have undergone considerable evolution: from shallow to deep, from fully connected to structured (e.g. convolutional, recurrent, or residual), from unnormalized to normalized. The present thesis contribut ...

EPFL2025

Reweighting simulated events using machine-learning techniques in the CMS experiment

Jan Steggemann, Konstantin Androsov, Anna Mascellani, Joanna Malgorzata Wanczyk, Paul Théodore Gabriel Pablo De Bryas Dexmiers d'Archiac

Data analyses in particle physics rely on an accurate simulation of particle collisions and a detailed simulation of detector effects to extract physics knowledge from the recorded data. Event generators together with a geant-based simulation of the detect ...

2025

Technology Legalization and Optimization for Adiabatic Quantum-Flux Parametron

Giovanni De Micheli, Alessandro Tempia Calvino

Adiabatic quantum-flux parametron (AQFP) is an energy-efficient superconducting technology. Before physical design can be performed, AQFP technology mapping involves not only mapping logic into supported gate types but also legalizing the circuit to fulfil ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2025

Mice Use Prediction Errors to Adapt to Visuomotor Perturbations in Virtual Reality

Sébastien Benjamin Hausmann

Motor adaptation allows organisms to adjust their movements in response to environmental changes, ensuring the successful execution of actions. While detailed behavioral studies in humans have inferred mechanisms underlying motor adaptationâ such as the r ...

EPFL2025

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

Yuning Jiang

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 ...

2024

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

Denis Gillet, Jérémy Alain La Scala-Jayet, Basile Maurice Pierre Spaenlehauer

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 ...

European Society for Engineering Education (SEFI)2024

AdaNCA: Neural Cellular Automata as Adaptors for More Robust Vision Transformer

Sabine Süsstrunk, Tong Zhang, Yitao Xu

Vision Transformers (ViTs) demonstrate remarkable performance in image classification through visual-token interaction learning, particularly when equipped with local information via region attention or convolutions. Although such architectures improve the ...

2024

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

Devis Tuia, Marc Conrad Russwurm, Thiên-Anh Claris Nguyen, Gaston Jean Lenczner

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 ...

Elsevier Science Inc2024

Federated learning with uncertainty-based client clustering for fleet-wide fault diagnosis

Olga Fink, Hao Lu

Operators from various industries have been pushing the adoption of wireless sensing nodes for industrial monitoring, and such efforts have produced sizeable condition monitoring datasets that can be used to build diagnosis algorithms capable of warning ma ...

2024

An Investigation of Manifold-Based Direct Control for a Brain-to-Body Neural Bypass

Silvestro Micera, Solaiman Shokur, Evgenia Vladimirova Roussinova

Objective: Brain-body interfaces (BBIs) have emerged as a very promising solution for restoring voluntary hand control in people with upper-limb paralysis. The BBI module decoding motor commands from brain signals should provide the user with intuitive, ac ...

Ieee-Inst Electrical Electronics Engineers Inc2024

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

Helena Kovacs, Alexandra Corina Niculescu, Tamara Milosevic

There is an everlasting effort in education to successfully ensure that the intended learning objectives are clearly taught and effectively learnt at the end of the educational cycle. This has been especially difficult in teaching and learning complex sets ...

TU Dublin2023

Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems

Colin Neil Jones, Melanie Nicole Zeilinger, Roland Schwan, Xuan Truong Nghiem

Physics-informed machine learning (PIML) is a set of methods and tools that systematically integrate machine learning (ML) algorithms with physical constraints and abstract mathematical models developed in scientific and engineering domains. As opposed to ...

IEEE2023

TeSLA: Test-Time Self-Learning With Automatic Adversarial Augmentation

Jean-Philippe Thiran, Seyedbehzad Bozorgtabar, Guillaume Marc Georges Vray, Devavrat Tomar

Most recent test-time adaptation methods focus on only classification tasks, use specialized network architectures, destroy model calibration or rely on lightweight information from the source domain. To tackle these issues, this paper proposes a novel Tes ...

Los Alamitos2023

Forest Drought Impact Prediction based on Spatio-temporal Satellite Imagery and Weather Forecasts -- A Spatio-Temporal Approach using Convolutional LSTM Models

Devis Tuia

2023

Bayes-optimal Learning of Deep Random Networks of Extensive-width

Lenka Zdeborová, Florent Gérard Krzakala, Hugo Chao Cui

We consider the problem of learning a target function corresponding to a deep, extensive-width, non-linear neural network with random Gaussian weights. We consider the asymptotic limit where the number of samples, the input dimension and the network width ...

2023

One-shot Retinal Artery and Vein Segmentation via Cross-modality Pretraining

Jiancheng Yang

Purpose: To perform one-shot retinal artery and vein segmentation with cross-modality artery-vein (AV) soft -label pretraining.Design: Cross-sectional study.Subjects: The study included 6479 color fundus photography (CFP) and arterial-venous fundus fluores ...

Elsevier2023

Learning of physical systems: from inference to control

Babak Rahmani

To characterize a physical system to behave as desired, either its underlying governing rules must be known a priori or the system itself be accurately measured. The complexity of full measurements of the system scales with its size. When exposed to real-w ...

EPFL2022

Robotic automation and unsupervised cluster assisted modeling for solving the forward and reverse design problem of paper airplanes

Josephine Anna Eleanor Hughes, Kai Christian Junge, Nana Obayashi, Stefan Ilic

Although often regarded a childhood toy, the design of paper airplanes is subtly complex. The design space and mapping from geometry to distance flown is highly nonlinear and probabilistic where a single airplane design exhibits a multitude of trajectory f ...

Nature Research2022