Bridging the Gap between Semantics and Multimedia Processing

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Blood pressure (BP) is a crucial indicator of cardiovascular health. Hypertension is a common life-threatening condition and a key factor of cardiovascular diseases (CVDs). Identifying abnormal BP fluctuations can allow for early detection and management o ...

Control design for robotic systems is complex and often requires solving an optimization to follow a trajectory accurately. Online optimization approaches like Model Predictive Control (MPC) have been shown to achieve great tracking performance, but requir ...

Metal-organic frameworks (MOFs) are a class of crystalline porous materials that exhibit a vast chemical space owing to their tunable molecular building blocks with diverse topologies. An unlimited number of MOFs can, in principle, be synthesized. Machine ...

Smartphones enable understanding human behavior with activity recognition to support peoples daily lives. Prior studies focused on using inertial sensors to detect simple activities (sitting, walking, running, etc.) and were mostly conducted in homogeneous ...

The Euclid Space Telescope will provide deep imaging at optical and near-infrared wavelengths, along with slitless near-infrared spectroscopy, across similar to 15 000deg(2) of the sky. Euclid is expected to detect similar to 12 billion astronomical source ...

We introduce contextual stochastic bilevel optimization (CSBO) -- a stochastic bilevel optimization framework with the lower-level problem minimizing an expectation conditioned on some contextual information and the upper-level decision variable. This fram ...

Owing to the diminishing returns of deep learning and the focus on model accuracy, machine learning for chemistry might become an endeavour exclusive to well-funded institutions and industry. Extending the focus to model efficiency and interpretability wil ...

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 ...

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 ...

Metal-based Laser Powder Bed Fusion (LPBF) has made fabricating intricate components easier. Yet, assessing part quality is inefficient, relying on costly Computed Tomography (CT) scans or time-consuming destructive tests. Also, intermittent inspection of ...

Machine learning (ML) models for molecules and materials commonly rely on a decomposition of the global target quantity into local, atom-centered contributions. This approach is convenient from a computational perspective, enabling large-scale ML-driven si ...

Algorithms are everywhere. The recipe for the frangipane cake is an algorithm. If all the listed ingredients are available and the cook is sufficiently deft, after a finite number of small, well-defined steps a delicious dessert will exit the oven. Now, wh ...

Organocatalysis has evolved significantly over the last decades, becoming a pillar of synthetic chemistry, but traditional theoretical approaches based on quantum mechanical computations to investigate reaction mechanisms and provide rationalizations of ca ...

Some governance functions traditionally performed by humans are increasingly informed and sometimes automatically executed by machine learning algorithms (governance by machine learning) to benefit society. Therefore, it is necessary to think also about th ...

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 ...

Laser Powder Bed Fusion (LPBF) is an Additive Manufacturing (AM) process consolidating parts layer by layer, from a metallic powder bed. It allows no limitation in terms of geometry and is therefore of particular interest to various industries. Metallic LP ...

Inexpensive and accessible sensors are accelerating data acquisition in animal ecology. These technologies hold great potential for large-scale ecological understanding, but are limited by current processing approaches which inefficiently distill data into ...

Machine learning models trained with passive sensor data from mobile devices can be used to perform various inferences pertaining to activity recognition, context awareness, and health and well-being. Prior work has improved inference performance through t ...

The HyperDimensional Computing (HDC) Machine Learning (ML) paradigm is highly interesting for applications involving continuous, semi-supervised learning for long-term monitoring. However, its accuracy is not yet on par with other ML approaches, necessitat ...

One of the main goal of Artificial Intelligence is to develop models capable of providing valuable predictions in real-world environments. In particular, Machine Learning (ML) seeks to design such models by learning from examples coming from this same envi ...

Bridging the Gap between Semantics and Multimedia Processing

Chattez avec Graph Search

Data-driven approaches for non-invasive cuffless blood pressure monitoring

Training Efficient Controllers via Analytic Policy Gradient

A multi-modal pre-training transformer for universal transfer learning in metal-organic frameworks

Complex Daily Activities, Country-Level Diversity, and Smartphone Sensing: A Study in Denmark, Italy, Mongolia, Paraguay, and UK

Euclid preparation XXII. Selection of quiescent galaxies from mock photometry using machine learning

Contextual Stochastic Bilevel Optimization

Aiming beyond slight increases in accuracy

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

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

Optimizing in-situ monitoring for laser powder bed fusion process: Deciphering acoustic emission and sensor sensitivity with explainable machine learning

Robustness of Local Predictions in Atomistic Machine Learning Models

Practical Byzantine-resilient Stochastic Gradient Descent

Enriching the Computational Toolbox for Organocatalysis

Nine recommendations for the governance of AI systems

Machine Learning Methods for Robust Uncertainty Quantification and Controller Approximation

Towards Robust Monitoring of the Laser Powder Bed Fusion Process based on Acoustic Emission combined with Machine Learning Solutions

Perspectives in machine learning for wildlife conservation

Keep Sensors in Check: Disentangling Country-Level Generalization Issues in Mobile Sensor-Based Models with Diversity Scores

HDTorch: Accelerating Hyperdimensional Computing with GP-GPUs for Design Space Exploration

Predicting in Uncertain Environments: Methods for Robust Machine Learning