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Publications associées (29)

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An Ordinal Latent Variable Model of Conflict Intensity

Robert West

Measuring the intensity of events is crucial for monitoring and tracking armed conflict. Advances in automated event extraction have yielded massive data sets of '' who did what to whom '' micro-records that enable datadriven approaches to monitoring confl ...

Assoc Computational Linguistics-Acl2023

Automatic Parameterization for Aerodynamic Shape Optimization via Deep Geometric Learning

Pascal Fua, Zhen Wei

We propose two deep learning models that fully automate shape parameterization for aerodynamic shape optimization. Both models are optimized to parameterize via deep geometric learning to embed human prior knowledge into learned geometric patterns, elimina ...

2023

Learning Linearized Degradation of Health Indicators using Deep Koopman Operator Approach

Olga Fink, Sergei Garmaev

With the current trend of increasing complexity of industrial systems, the construction and monitoring of health indicators becomes even more challenging. Given that health indicators are commonly employed to predict the end of life, a crucial criterion fo ...

Research Publishing2023

Dual-frequency spectral radar retrieval of snowfall microphysics: a physics-driven deep-learning approach

Alexis Berne, Anne-Claire Marie Billault--Roux, Gionata Ghiggi

The use of meteorological radars to study snowfall microphysical properties and processes is well established, in particular via a few distinct techniques: the use of radar polarimetry, of multi-frequency radar measurements, and of the radar Doppler spectr ...

COPERNICUS GESELLSCHAFT MBH2023

Non-intrusive data-driven reduced-order modeling for time-dependent parametrized problems

Jan Sickmann Hesthaven, Junming Duan

Reduced-order models are indispensable for multi-query or real-time problems. However, there are still many challenges to constructing efficient ROMs for time-dependent parametrized problems. Using a linear reduced space is inefficient for time-dependent n ...

Academic Press Inc Elsevier Science2023

Using Gaming Footage as a Source of Internet Latency Information

Aikaterini Argyraki, Catalina Paz Alvarez Inostroza

Keeping track of Internet latency is a classic measurement problem. Open measurement platforms like RIPE Atlas are a great solution, but they also face challenges: preventing network overload that may result from uncontrolled active measurements, and maint ...

2023

Technology Strategy in Dynamic Environments: A Computational Analysis of the Automation of Routines, the Organization of AI, and the Evaluation of Technology Risk

Maximilian Wieland Hofer

In this dissertation, I develop theory and evidence to argue that new technologies are central to how firms organize to create and capture value. I use computational methods such as reinforcement learning and probabilistic topic modeling to investigate thr ...

EPFL2022

Robustness, replicability and scalability in topic modelling

Orion B Penner, Omar Ballester Gonzalez

Approaches for estimating the similarity between individual publications are an area of long -standing interest in the scientometrics and informetrics communities. Traditional techniques have generally relied on references and other metadata, while text mi ...

ELSEVIER2022

Model-Based Clustering of Trends and Cycles of Nitrate Concentrations in Rivers Across France

Camille Roland Marie Minaudo

Elevated nitrate from human activity causes ecosystem and economic harm globally. The factors that control the spatiotemporal dynamics of riverine nitrate concentration remain difficult to describe and predict. We analyzed nitrate concentration from 4450 s ...

SPRINGER2023

Latent Mechanisms of Polarization Switching from In Situ Electron Microscopy Observations

Vasiliki Tileli, Reinis Ignatans

In situ scanning transmission electron microscopy enables observation of the domain dynamics in ferroelectric materials as a function of externally applied bias and temperature. The resultant data sets contain a wealth of information on polarization switch ...

WILEY-V C H VERLAG GMBH2022

Une approche computationnelle du cadastre napoléonien de Venise

Frédéric Kaplan, Isabella Di Lenardo, Raphaël Barman, Federica Pardini

At the beginning of the 19th century, the Napoleonic administration introduced a new standardised description system to give an objective account of the form and functions of the city of Venice. The cadastre, deployed on a European scale, was offering for ...

2021

Discovering Tonal Profiles with Latent Dirichlet Allocation

Martin Alois Rohrmeier, Fabian Claude Moss

Music analysis, in particular harmonic analysis, is concerned with the way pitches are organized in pieces of music, and a range of empirical applications have been developed, for example, for chord recognition or key finding. Naturally, these approaches r ...

2021

Neural Design Space Exploration

Pierre Dillenbourg, Richard Lee Davis, Kevin Gonyop Kim, Wei Jiang

We have developed a new tool that makes it possible for people with zero programming experience to intentionally and meaningfully explore the latent space of a GAN. This tool was developed to provide a means for designers to explore the "design space" of t ...

2021

Contextually Plausible and Diverse 3D Human Motion Prediction

Mathieu Salzmann

We tackle the task of diverse 3D human motion prediction, that is, forecasting multiple plausible future 3D poses given a sequence of observed 3D poses. In this context, a popular approach consists of using a Conditional Variational Autoencoder (CVAE). How ...

IEEE2021

Towards Recognizing Emotion in the Latent Space

Emotion recognition is usually achieved by collecting features (physiological signals, events, facial expressions, etc.) to predict an emotional ground truth. This ground truth is arguably unreliable due to its subjective nature. In this paper, we introduc ...

IEEE2021

Learning Urban Nightlife Routines from Mobile Data

Daniel Gatica-Perez, Thanh Trung Phan

The use of smartphone sensing for public health studies is appealing to understand routines. We present an approach to learn nightlife routines in a smartphone sensing dataset volunteered by 184 young people (1586 weekend nights with location data captured ...

ACM2020

Multi-scale sequential network for semantic text segmentation and localization

Jean-Marc Odobez, Olivier Canévet, Michael Villamizar

We present a novel method for semantic text document analysis which in addition to localizing text it labels the text in user-defined semantic categories. More precisely, it consists of a fully-convolutional and sequential network that we apply to the part ...

2020

Evolution of Topics and Novelty in Science

Orion B Penner, Omar Ballester Gonzalez

Methods of estimating the similarity between individual publications is an area of long-standing interest in the scientometrics community. Traditional methods have generally relied on references and other metadata, while text mining approaches based on tit ...

INT SOC SCIENTOMETRICS & INFORMETRICS-ISSI2019

DeepSuccess - Predict the Success of Tech Startups

Many interesting applications emerged with the increasing popularity of deep learning. This project explored natural language processing and visualization techniques as well as two neural network architectures to classify ICOs. The first network focused on ...

2019

Discovering Eating Routines in Context with a Smartphone App

Daniel Gatica-Perez, Joan Isaac Biel Tres

In everyday life, eating follows patterns and occurs in context. We present an approach to discover daily eating routines of a population following a multidimensional representation of eating episodes, using data collected with the Bites'n'Bits smartphone ...

ASSOC COMPUTING MACHINERY2019