Conférences et ateliers

Cow-conception - Intégrer l'expérience de l'animal lors de la conception du logement ou de nouvelles pratiques pour maximiser la réponse animale et l'expérience utilisateur (producteur). Divers

2024.

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2023

Diallo, A. B.; Vasseur, E.

Le Projet WELL-E (IA au service du bien-être des bovins laitiers). Divers

2023.

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Vasseur, Elsa; Diallo, Abdoulaye Banire

Capsule vidéo Well-E : Intelligence artificielle au service du bien-être des bovins laitiers Divers

2023.

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Vliet, Rachel; Wolfe, Tania; Arsenault, Sandrine

WELL-E Posters Divers

2023.

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Julliot, C.; Dallago, G. M.; Nejati, A.; Diallo, A. B.; Vasseur, E.

Utiliser la cinématique pour quantifier les attributs de la démarche et prédire un score de démarche chez les vaches laitières Divers

2023.

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Julliot, C.; Dallago, G. M.; Nejati, Amir; Diallo, A. B.; Vasseur, E.

Décrypter les secrets de la démarche : comment la cinématique peut prédire les scores de démarche chez la vache laitière Divers

2023.

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Muszik, Jasmine; Cellier, M.; Aigueperse, N.; Vasseur, E.

Émotions Animales : Un Regard Approfondi sur la Motivation, l'Anticipation, et la Frustration Divers

2023.

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Cellier, M.; Aigueperse, N.; Vasseur, E.

Alors, on sort? Caractérisation de l'anticipation des vaches laitières pour un accès à l'extérieure Divers

2023.

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Mrabah, Nairouz; Amar, Mohamed Mahmoud; Bouguessa, Mohamed; Diallo, Abdoulaye Banire

Toward Convex Manifolds: A Geometric Perspective for Deep Graph Clustering of Single-cell RNA-seq Data Article d'actes

Dans: Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, p. 4855–4863, International Joint Conferences on Artificial Intelligence Organization, Macau, SAR China, 2023, ISBN: 9781956792034.

Résumé | Liens | BibTeX | Étiquettes:

Zhu, J.; Lacroix, R.; Wade, K.

Automated extraction of domain knowledge for transition-cow management Divers

2023.

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Mokhtarnazif, S.; Shepley, E.; Nejati, A.; Dallago, G. M.; Vasseur, E.

The effect of limited outdoor access frequencies on gait score, hoof lesions and hoof surface temperature of non-clinically lame cows housed in a movement-restricted environment. Divers

2023.

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Julliot, C.; Dallago, G. M.; Nejati, A.; Diallo, A. B.; Vasseur, E.

Using kinematic to quantify gait attributes and predict gait score in dairy cows. Divers

2023.

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Cellier, M.; Aigueperse, N.; Vasseur, E.

To go or not to go? Assessing anticipation for outdoor access in dairy cows Divers

2023.

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Dallago, G. M.

Panel discussion on opportunity and challenges related to use of sensors for health and welfare. Divers

2023.

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Dallago, G. M.

Using 3D coordinates and machine learning to predict dairy cow locomotor ability Divers

2023.

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Vliet, Rachel; Aigueperse, Nadege; Vasseur, Elsa

CAREGIVERS ENGAGING IN MONKEY BUSINESS: EVALUATING THE USE OF CAREGIVERS AS SOCIAL ENRICHMENT FOR CAPTIVE RHESUS AND CYNOMOLGUS MACAQUES Divers

2023.

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Mrabah, Nairouz; Amar, Mohamed Mahmoud; Bouguessa, Mohamed; Diallo, Abdoulaye Banire

Exploring the Interaction between Local and Global Latent Configurations for Clustering Single-Cell RNA-Seq: A Unified Perspective Article d'actes

Dans: Proceedings of the AAAI Conference on Artificial Intelligence, p. 9235–9242, 2023.

Résumé | Liens | BibTeX | Étiquettes:

@inproceedings{mrabah_exploring_2023,

title = {Exploring the Interaction between Local and Global Latent Configurations for Clustering Single-Cell RNA-Seq: A Unified Perspective},

author = {Nairouz Mrabah and Mohamed Mahmoud Amar and Mohamed Bouguessa and Abdoulaye Banire Diallo},

url = {https://ojs.aaai.org/index.php/AAAI/article/view/26107},

doi = {10.1609/aaai.v37i8.26107},

year  = {2023},

date = {2023-06-01},

urldate = {2024-10-16},

booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence},

volume = {37},

pages = {9235–9242},

abstract = {The most recent approaches for clustering single-cell RNA-sequencing data rely on deep auto-encoders. However, three major challenges remain unaddressed. First, current models overlook the impact of the cumulative errors induced by the pseudo-supervised embedding clustering task (Feature Randomness). Second, existing methods neglect the effect of the strong competition between embedding clustering and reconstruction (Feature Drift). Third, the previous deep clustering models regularly fail to consider the topological information of the latent data, even though the local and global latent configurations can bring complementary views to the clustering task. To address these challenges, we propose a novel approach that explores the interaction between local and global latent configurations to progressively adjust the reconstruction and embedding clustering tasks. We elaborate a topological and probabilistic filter to mitigate Feature Randomness and a cell-cell graph structure and content correction mechanism to counteract Feature Drift. The Zero-Inflated Negative Binomial model is also integrated to capture the characteristics of gene expression profiles. We conduct detailed experiments on real-world datasets from multiple representative genome sequencing platforms. Our approach outperforms the state-of-the-art clustering methods in various evaluation metrics.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

Fermer

Cellier, M.; Aigueperse, N.; Vasseur, E.

Sortir ou ne pas sortir ? Évaluation de l'anticipation de l'accès à l'extérieur chez les vaches laitières Divers

2023.

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