Germain Forestier
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View article: Self‐Supervised Learning Based Clustering Workflow for Exploring Seismological Data From Dense Networks
Self‐Supervised Learning Based Clustering Workflow for Exploring Seismological Data From Dense Networks Open
A key challenge in environmental seismology is processing seismic data to study source physics, natural and human‐induced forcings, and geological structures such as landslides, glaciers, and volcanoes. Seismic arrays with dozens of statio…
View article: Failure Risk Prediction in a MOOC: A Multivariate Time Series Analysis Approach
Failure Risk Prediction in a MOOC: A Multivariate Time Series Analysis Approach Open
MOOCs offer free and open access to a wide audience, but completion rates remain low, often due to a lack of personalized content. To address this issue, it is essential to predict learner performance in order to provide tailored feedback.…
View article: Automated Coding of French Emergency Calls Using BERT
Automated Coding of French Emergency Calls Using BERT Open
Emergency Medical Dispatch Centres (EMDC) in France are facing a growing number of calls, each requiring a rapid and efficient response, leaving no time for emergency medical dispatchers to code case files. However, this coding is essentia…
View article: Urban trees species classification using Sentinel-2 and Planetscope satellite image time series
Urban trees species classification using Sentinel-2 and Planetscope satellite image time series Open
International audience
View article: Uncovering environmental and other exotic seismic sources with machine learning
Uncovering environmental and other exotic seismic sources with machine learning Open
Seismology, beyond the study of earthquakes, has become an indispensable tool for understanding environmental changes, offering unique insights into a wide range of phenomena and natural risks, from slope instabilities to glacial dynamics …
View article: Deep Neural Network Architectures for Advanced Hiking Map Generation
Deep Neural Network Architectures for Advanced Hiking Map Generation Open
International audience
View article: A Comparative Study of CNNs and Vision-Language Models for Chart Image Classification
A Comparative Study of CNNs and Vision-Language Models for Chart Image Classification Open
International audience
View article: COCALITE: A Hybrid Model COmbining CAtch22 and LITE for Time Series Classification
COCALITE: A Hybrid Model COmbining CAtch22 and LITE for Time Series Classification Open
International audience
View article: Self-supervised learning of seismological data reveals new eruptive sequences at the Mayotte submarine volcano
Self-supervised learning of seismological data reveals new eruptive sequences at the Mayotte submarine volcano Open
SUMMARY Continuous seismological observations provide valuable insights to deepen our understanding of geological processes and geohazards. We present a systematic analysis of two months of seismological records using an AI-based Self-Supe…
View article: Look Into the LITE in Deep Learning for Time Series Classification
Look Into the LITE in Deep Learning for Time Series Classification Open
Deep learning models have been shown to be a powerful solution for Time Series Classification (TSC). State-of-the-art architectures, while producing promising results on the UCR and the UEA archives , present a high number of trainable par…
View article: Improving Urban Tree Species Classification with High Resolution Satellite Imagery and Machine Learning
Improving Urban Tree Species Classification with High Resolution Satellite Imagery and Machine Learning Open
International audience
View article: Establishing a Unified Evaluation Framework for Human Motion Generation: A Comparative Analysis of Metrics
Establishing a Unified Evaluation Framework for Human Motion Generation: A Comparative Analysis of Metrics Open
The development of generative artificial intelligence for human motion generation has expanded rapidly, necessitating a unified evaluation framework. This paper presents a detailed review of eight evaluation metrics for human motion genera…
View article: The impact of data set similarity and diversity on transfer learning success in time series forecasting
The impact of data set similarity and diversity on transfer learning success in time series forecasting Open
Pre-trained models have become pivotal in enhancing the efficiency and accuracy of time series forecasting on target data sets by leveraging transfer learning. While benchmarks validate the performance of model generalization on various ta…
View article: Self-Supervised Learning Strategies for Clustering Continuous Seismic Data
Self-Supervised Learning Strategies for Clustering Continuous Seismic Data Open
Continuous seismological datasets offer insights for the understanding of the dynamics of many geological structures (such as landslides, ice glaciers, and volcanoes) in relation to various forcings (meteorological, climatic, tectonic, ant…
View article: Self-supervised learning for the exploration of continuous seismic records at the Fani Maoré submarine volcano (Mayotte)
Self-supervised learning for the exploration of continuous seismic records at the Fani Maoré submarine volcano (Mayotte) Open
Continuous seismological observations provide valuable information to deepen our understanding of processes occurring in both aerial and submarine volcanoes. However, the wealth of the seismicity recorded near volcanoes makes exhaustive ex…
View article: Deep Learning for Time Series Classification and Extrinsic Regression: A Current Survey
Deep Learning for Time Series Classification and Extrinsic Regression: A Current Survey Open
Time Series Classification and Extrinsic Regression are important and challenging machine learning tasks. Deep learning has revolutionized natural language processing and computer vision and holds great promise in other fields such as time…
View article: Finding Foundation Models for Time Series Classification with a PreText Task
Finding Foundation Models for Time Series Classification with a PreText Task Open
Over the past decade, Time Series Classification (TSC) has gained an increasing attention. While various methods were explored, deep learning - particularly through Convolutional Neural Networks (CNNs)-stands out as an effective approach. …
View article: LITE: Light Inception with boosTing tEchniques for Time Series Classification
LITE: Light Inception with boosTing tEchniques for Time Series Classification Open
International audience
View article: ShapeDBA: Generating Effective Time Series Prototypes using ShapeDTW Barycenter Averaging
ShapeDBA: Generating Effective Time Series Prototypes using ShapeDTW Barycenter Averaging Open
Time series data can be found in almost every domain, ranging from the medical field to manufacturing and wireless communication. Generating realistic and useful exemplars and prototypes is a fundamental data analysis task. In this paper, …
View article: Self-supervised learning of seismological data reveals new eruptive sequences at the Mayotte submarine volcano - Supplementary Materials
Self-supervised learning of seismological data reveals new eruptive sequences at the Mayotte submarine volcano - Supplementary Materials Open
The following files are shared: - The scripts used to train the model and generate the figures of the article - The input images used to train the model as well as the final outputs (embedding matrix and the associated filenames matrix) - …