Metacognition meets AI: Empowering reflective writing with large language models

Chattez avec Graph Search

Posez n’importe quelle question sur les cours, conférences, exercices, recherches, actualités, etc. de l’EPFL ou essayez les exemples de questions ci-dessous.

AVERTISSEMENT : Le chatbot Graph n'est pas programmé pour fournir des réponses explicites ou catégoriques à vos questions. Il transforme plutôt vos questions en demandes API qui sont distribuées aux différents services informatiques officiellement administrés par l'EPFL. Son but est uniquement de collecter et de recommander des références pertinentes à des contenus que vous pouvez explorer pour vous aider à répondre à vos questions.

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

This paper presents our approach to predicting future error-related events in a robot-mediated gamified phys- ical training activity for stroke patients. The ability to predict future error under such conditions suggests the existence of distinguishable fe ...

Metacognition meets AI: Empowering reflective writing with large language models

Chattez avec Graph Search

Source-Free Domain-Invariant Performance Prediction

Towards multimodal foundation models in molecular cell biology

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

Technology Legalization and Optimization for Adiabatic Quantum-Flux Parametron

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

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

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

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

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

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

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

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

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

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

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

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

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

Learning of physical systems: from inference to control

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

Predictive Analysis of Errors During Robot-Mediated Gamified Training