Collaboration and abstract representations: towards predictive models based on raw speech and eye-tracking data

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.

Estimating ecosystem-atmosphere fluxes such as evapotranspiration (ET) in a robust manner and at a global scale remains a challenge. Methods based on machine learning (ML) have shown promising results in achieving such upscaling, providing a complementary ...

Computing servers have played a key role in developing and processing emerging compute-intensive applications in recent years. Consolidating multiple virtual machines (VMs) inside one server to run various applications introduces severe competence for limi ...

A frequency scaling governor is critical for the performance management of cloud servers, as it enhances energy efficiency and helps to control operational temperatures, thereby ensuring system reliability. However, our in-depth analysis of the application ...

The acquisition of survey responses is a crucial component in conducting research aimed at comprehending public opinion. However, survey data collection can be arduous, time-consuming, and expensive, with no assurance of an adequate response rate. In this ...

We present a two-level implementation of an infrastructure that allows performance maximization under a power-cap on multi-application environments with minimal user intervention. At the application level, we integrate BAR (Power Budget-Aware Runtime Sched ...

While systems designers are increasingly turning to hardware accelerators for performance gains, realizing these gains is painstaking and error-prone. It can take several person-months to determine if a given accelerator is a good fit for a given piece of ...

MALTA is a depleted monolithic active pixel sensor (DMAPS) developed in the Tower Semiconductor 180-nm CMOS imaging process. Monolithic CMOS sensors offer advantages over current hybrid imaging sensors in terms of both increased tracking performance due to ...

This paper presents the cooling concept for a medium voltage modular multilevel converter cell. To optimize power density, the metallic enclosure is used to dissipate the heat generated by semiconductor losses, while maintaining maximum temperatures below ...

Background:Short-term forecasts of infectious disease burden can contribute to situational awareness and aid capacity planning. Based on best practice in other fields and recent insights in infectious disease epidemiology, one can maximise the predictive p ...

This thesis is situated at the crossroads between machine learning and control engineering. Our contributions are both theoretical, through proposing a new uncertainty quantification methodology in a kernelized context; and experimental, through investigat ...

Designing turbocompressors is a complex and challenging task, as it involves balancing conflicting objectives such as efficiency, stability, and robustness against manufacturing deviations. This paper proposes an integrated design methodology for turbocomp ...

Artificial intelligence and machine learning algorithms have become ubiquitous. Although they offer a wide range of benefits, their adoption in decision-critical fields is limited by their lack of interpretability, particularly with textual data. Moreover, ...

Learning to predict accurately from a few data samples is a central challenge in modern data-hungry machine learning. On natural images, human vision typically outperforms deep learning approaches on few-shot learning. However, we hypothesize that aerial a ...

The shear resistance of headed studs is of paramount importance for the design of steel-concrete composite structures and an accurate predictive model is highly needed. Ensemble learning is expected to be a powerful solution while it relies on laborious se ...

Neural networks are ubiquitous in applied machine learning for education. Their pervasive success in predictive performance comes alongside a severe weakness, the lack of explainability of their decisions, especially relevant in human-centric fields. We im ...

This thesis consists of three applications of machine learning techniques to risk management. The first chapter proposes a deep learning approach to estimate physical forward default intensities of companies. Default probabilities are computed using artifi ...

Personalized ranking methods are at the core of many systems that learn to produce recommendations from user feedbacks. Their primary objective is to identify relevant items from very large vocabularies and to assist users in discovering new content. These ...

Over the past decade, interatomic potentials based on machine learning (ML) techniques have become an indispensable tool in the atomic-scale modeling of materials. Trained on energies and forces obtained from electronic-structure calculations, they inherit ...

Rationale: Given the expanding number of COVID-19 cases and the potential for new waves of infection, there is an urgent need for early prediction of the severity of the disease in intensive care unit (ICU) patients to optimize treatment strategies.Objecti ...

An attempt is made to translate five generic key performance indicators (KPIs) into a continuous real-time cost function in a batch order-based manufacturing environment. The challenge of controlling and optimizing resource utilization, production efficien ...

Collaboration and abstract representations: towards predictive models based on raw speech and eye-tracking data

Chattez avec Graph Search

On the added value of sequential deep learning for the upscaling of evapotranspiration

CloudProphet: A Machine Learning-Based Performance Prediction for Public Clouds

Is the powersave governor really saving power?

Predicting Survey Response with Quotation-based Modeling: A Case Study on Favorability towards the United States

Dynamic power budget redistribution under a power cap on multi-application environments

The Case for Performance Interfaces for Hardware Accelerators

Radiation Hardness of MALTA2, a Monolithic Active Pixel Sensor for Tracking Applications

Thermal Design of a Medium Voltage Modular Multilevel Converter Cell

Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations

Machine Learning Methods for Robust Uncertainty Quantification and Controller Approximation

An Integrated Approach to Designing Robust Turbocompressors on Gas Bearings Through Surrogate Modeling and Constrained Multi-Objective Optimization

Textual Explanations and Critiques in Recommendation Systems

Humans are Poor Few-Shot Classifiers for Sentinel-2 Land Cover

Auto-tuning ensemble models for estimating shear resistance of headed studs in concrete

Evaluating the Explainers: Black-Box Explainable Machine Learning for Student Success Prediction in MOOCs

Financial Risk Management with Machine Learning

Dynamic Personalized Ranking

Beyond potentials: Integrated machine learning models for materials

Using Machine Learning to Predict Mortality for COVID-19 Patients on Day 0 in the ICU

Cost-Based Decision Support System: A Dynamic Cost Estimation of Key Performance Indicators in Manufacturing