Publications associées à Logique épistémique

Analysing the potential for modal shiftbased on the logic of modal choice

Vincent Kaufmann, Eloi Antoine Maël Bernier, Florian Lucien Jacques Masse, Ludy Juliana González Villamizar

This article explores how the logic underlying modal practices tends to modulate the population’s responsiveness to improvements in global transport supply. Based on a quantitative survey conducted in 2018–2019 among the working population of the cantons o ...

2023

4M: Massively Multimodal Masked Modeling

Shuqing Teresa Yeo, Amir Roshan Zamir, Oguzhan Fatih Kar, Roman Christian Bachmann, David Mizrahi

Current machine learning models for vision are often highly specialized and limited to a single modality and task. In contrast, recent large language models exhibit a wide range of capabilities, hinting at a possibility for similarly versatile models in co ...

Neural Information Processing Systems (Nips)2023

SimMMDG: A Simple and Effective Framework for Multi-modal Domain Generalization

Olga Fink, Ismail Nejjar, Han Sun, Hao Dong

In real-world scenarios, achieving domain generalization (DG) presents significant challenges as models are required to generalize to unknown target distributions. Generalizing to unseen multi-modal distributions poses even greater difficulties due to the ...

2023

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

Daniel Gatica-Perez, Lakmal Buddika Meegahapola

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 ...

New York2023

MultiMAE: Multi-modal Multi-task Masked Autoencoders

Amir Roshan Zamir, Roman Christian Bachmann, David Mizrahi, Andrei Atanov

We propose a pre-training strategy called Multi-modal Multi-task Masked Autoencoders (MultiMAE). It differs from standard Masked Autoencoding in two key aspects: I) it can optionally accept additional modalities of information in the input besides the RGB ...

SPRINGER INTERNATIONAL PUBLISHING AG2022

Late Fusion of the Available Lexicon and Raw Waveform-based Acoustic Modeling for Depression and Dementia Recognition

Petr Motlicek, Mathew Magimai Doss, Julian David Fritsch, Subrahmanya Pavankumar Dubagunta

Mental disorders, e.g. depression and dementia, are categorized as priority conditions according to the World Health Organization (WHO). When diagnosing, psychologists employ structured questionnaires/interviews, and different cognitive tests. Although acc ...

ISCA-INT SPEECH COMMUNICATION ASSOC2021

Empirical observations of multi-modal network-level models: Insights from the pNEUMA experiment

Nikolaos Geroliminis, Emmanouil Barmpounakis

Multi-modal interactions at the network-level remain unexplored due to the lack of highresolution data for all transportation modes involved. The current work investigates the effect of multi-modal interactions at space-mean network speed for each mode usi ...

PERGAMON-ELSEVIER SCIENCE LTD2021

Temporal analysis of multimodal data to predict collaborative learning outcomes

Kshitij Sharma, Jennifer Kaitlyn Olsen

The analysis of multiple data streams is a long-standing practice within educational research. Both multimodal data analysis and temporal analysis have been applied successfully, but in the area of collaborative learning, very few studies have investigated ...

WILEY2020

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Logique épistémique

QuNex-An integrative platform for reproducible neuroimaging analytics

Analysing the potential for modal shiftbased on the logic of modal choice

4M: Massively Multimodal Masked Modeling

SimMMDG: A Simple and Effective Framework for Multi-modal Domain Generalization

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

MultiMAE: Multi-modal Multi-task Masked Autoencoders

Late Fusion of the Available Lexicon and Raw Waveform-based Acoustic Modeling for Depression and Dementia Recognition

Empirical observations of multi-modal network-level models: Insights from the pNEUMA experiment

Temporal analysis of multimodal data to predict collaborative learning outcomes

Dynamic Quantification of the Effect of Traffic Congestion on a Multilayer Network

Graph Chatbot

Chattez avec Graph Search

QuNex-An integrative platform for reproducible neuroimaging analytics

Dynamic Quantification of the Effect of Traffic Congestion on a Multilayer Network

SimMMDG: A Simple and Effective Framework for Multi-modal Domain Generalization

Empirical observations of multi-modal network-level models: Insights from the pNEUMA experiment

Analysing the potential for modal shiftbased on the logic of modal choice

MultiMAE: Multi-modal Multi-task Masked Autoencoders

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

Late Fusion of the Available Lexicon and Raw Waveform-based Acoustic Modeling for Depression and Dementia Recognition

Temporal analysis of multimodal data to predict collaborative learning outcomes

4M: Massively Multimodal Masked Modeling