Publications related to A data-driven approach for lightning nowcasting with deep learning

Related publications (29)

Extending Capabilities of Attention-based Models

Deep learning has brought incredible progress in areas where traditional algorithms struggle. It can be because they need rules that are sometimes hard to conceptualize, it can also be that the number and manner of combining these rules are too complex to ...

EPFL2024

Computational complexity of deep learning: fundamental limitations and empirical phenomena

Ludovic Théo Stephan, Alessandro Favero

This manuscript is the lecture notes of B. Barak’s course in the Les Houches ‘Statistical Physics and Machine Learning’ summer school in 2022. It surveys various proxies for computational hardness in random planted problems, from the low-degree likelihood ...

2024

Uncertainty quantification for a deep learning models for image-based crack segmentation

Onaïa Joseph Savary, Katrin Beyer, Bryan German Pantoja Rosero, Ketson Roberto Maximiano Dos Santos

Recent advancements in deep learning have found compelling applications in the image-based inspection of infrastructure systems. However, the efficacy of these models hinges significantly on the quality and diversity of the input data used for training. In ...

2024

FSscore: A Personalized Machine Learning-Based Synthetic Feasibility Score

Bruno Emanuel Ferreira De Sousa Correia, Philippe Schwaller, Rebecca Manuela Neeser

Determining whether a molecule can be synthesized is crucial in chemistry and drug discovery, as it guides experimental prioritization and molecule ranking in de novo design tasks. Existing scoring approaches to assess synthetic feasibility struggle to ext ...

2024

Connectome embedding in multidimensional graph spaces

Mathieu Mach, Dimitri Nestor Alice Van De Ville, Alessandra Griffa, Maria Giulia Preti, Enrico Amico, Raphaël Pierre Liégeois

Nodal properties are of particular importance when investigating patterns in brain networks. Nodal information is usually studied by comparing a few nodal measurements (up to three), resulting in analyses in three-dimensional spaces, at maximum. We offer a ...

Massachusetts Institute of Technology2024

Detection of Malicious Agents in Social Learning

Ali H. Sayed, Mert Kayaalp, Valentina Shumovskaia

Non-Bayesian social learning is a framework for distributed hypothesis testing aimed at learning the true state of the environment. Traditionally, the agents are assumed to receive observations conditioned on the same true state, although it is also possib ...

IEEE Institute of Electrical and Electronics Engineers2024

Domain knowledge-informed synthetic fault sample generation with health data map for cross-domain planetary gearbox fault diagnosis

Olga Fink

Extensive research has been conducted on fault diagnosis of planetary gearboxes using vibration signals and deep learning (DL) approaches. However, DL-based methods are susceptible to the domain shift problem caused by varying operating conditions of the g ...

ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD2023

Detecting Anomalies and Obstacles in Road Scenes

Krzysztof Maciej Lis

We address the problem of segmenting anomalies and unusual obstacles in road scenes for the purpose of self-driving safety. The objects in question are not present in the common training sets as it is not feasible to collect and annotate examples for every ...

EPFL2023

Prediction of plasma volume and total hemoglobin mass with machine learning

Jean-Marc Vesin

Hemoglobin concentration ([Hb]) is used for the clinical diagnosis of anemia, and in sports as a marker of blood doping. [Hb] is however subject to significant variations mainly due to shifts in plasma volume (PV). This study proposes a newly developed mod ...

WILEY2023

Automated post-earthquake damage assessment of stone masonry buildings integrating machine learning, computer vision, and physics-based modeling

Bryan German Pantoja Rosero

Current post-earthquake damage assessment methodologies are not only time-consuming but also subjective in nature and difficult to document. Recent advancements in artificial intelligence and technological devices make it possible to accomplish this task a ...

EPFL2023

Early decoding of walking tasks with minimal set of EMG channels

Silvestro Micera

Objective. Powered lower-limb prostheses relying on decoding motor intentions from non-invasive sensors, like electromyographic (EMG) signals, can significantly improve the quality of life of amputee subjects. However, the optimal combination of high decod ...

IOP Publishing Ltd2023

Genetic Algorithms for the Discovery of Homogeneous Catalysts

Anne-Clémence Corminboeuf, Rubén Laplaza Solanas, Simone Gallarati, Puck Elisabeth van Gerwen, Alexandre Alain Schöpfer

In this account, we discuss the use of genetic algorithms in the inverse design process of homogeneous catalysts for chemical transformations. We describe the main components of evolutionary experiments, specifically the nature of the fitness function to o ...

2023

Dense Image-based Predictions for Comics Analysis

Deblina Bhattacharjee

Dense image-based prediction methods have advanced tremendously in recent years. Their remarkable development has been possible due to the ample availability of real-world imagery. While these methods work well on photographs, their abilities do not genera ...

EPFL2023

Improving Object Detection under Domain Shifts

Vidit Vidit

Object detection plays a critical role in various computer vision applications, encompassing domains like autonomous vehicles, object tracking, and scene understanding. These applica- tions rely on detectors that generate bounding boxes around known object ...

EPFL2023

Electromagnetic wave-based extreme deep learning with nonlinear time-Floquet entanglement

Romain Christophe Rémy Fleury, Ali Momeni

Wave-based analog signal processing holds the promise of extremely fast, on-the-fly, power-efficient data processing, occurring as a wave propagates through an artificially engineered medium. Yet, due to the fundamentally weak non-linearities of traditiona ...

Nature Research2022

The role of convolutional neural networks in scanning probe microscopy: a review

Georg Fantner

Progress in computing capabilities has enhanced science in many ways. In recent years, various branches of machine learning have been the key facilitators in forging new paths, ranging from categorizing big data to instrumental control, from materials desi ...

BEILSTEIN-INSTITUT2021

A machine-learning algorithm correctly classifies cortical evoked potentials from both visual stimulation and electrical stimulation of the optic nerve

Diego Ghezzi, Eleonora Borda, Vivien Gaillet, Elodie Geneviève Zollinger

Objective. Optic nerve's intraneural stimulation is an emerging neuroprosthetic approach to provide artificial vision to totally blind patients. An open question is the possibility to evoke individual non-overlapping phosphenes via selective intraneural op ...

IOP PUBLISHING LTD2021

Gaussian Process Regression for Materials and Molecules

Michele Ceriotti, David Mark Wilkins

We provide an introduction to Gaussian process regression (GPR) machinelearning methods in computational materials science and chemistry. The focus of the present review is on the regression of atomistic properties: in particular, on the construction of in ...

AMER CHEMICAL SOC2021

Unsupervised classification of snowflake images using a generative adversarial network and K-medoids classification

Alexis Berne, Jussi Sakari Leinonen

The increasing availability of sensors imaging cloud and precipitation particles, like the Multi-Angle Snowflake Camera (MASC), has resulted in datasets comprising millions of images of falling snowflakes. Automated classification is required for effective ...

2020

Multi-armed Bandits in Action

Farnood Salehi

Making decisions is part and parcel of being human. Among a set of actions, we want to choose the one that has the highest reward. But the uncertainty of the outcome prevents us from always making the right decision. Making decisions under uncertainty can ...

EPFL2020