Thomas Gerald

According to our database1, Thomas Gerald authored at least 16 papers between 2017 and 2023.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

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Links

On csauthors.net:

Bibliography

2023
A hyperbolic approach for learning communities on graphs.
Data Min. Knowl. Discov., May, 2023

CoSPLADE : Adaptation d'un Modèle Neuronal Basé sur des Représentations Parcimonieuses pour la Recherche d'Information Conversationnelle.
Proceedings of the Actes de CORIA-TALN 2023. Actes de la 18e Conférence en Recherche d'Information et Applications, 2023

Sélectionner les ?bons? passages pour créer les ?bonnes? questions : Analyse et Évaluation d'un nouveau Corpus de Questions et Réponses pour l'Éducation.
Proceedings of the Extraction et Gestion des Connaissances, 2023

CoSPLADE: Contextualizing SPLADE for Conversational Information Retrieval.
Proceedings of the Advances in Information Retrieval, 2023

2022
MLIA-DAC@TREC CAsT 2022: Sparse Contextualized Query Embedding.
Proceedings of the Thirty-First Text REtrieval Conference, 2022

Continual Learning of Long Topic Sequences in Neural Information Retrieval.
Proceedings of the Advances in Information Retrieval, 2022

Does Structure Matter? Leveraging Data-to-Text Generation for Answering Complex Information Needs.
Proceedings of the Advances in Information Retrieval, 2022

Continual Learning of Long Topic Sequences in Neural Information Retrieval - abstract.
Proceedings of the 2nd Joint Conference of the Information Retrieval Communities in Europe (CIRCLE 2022), 2022

Does Structure Matter? Leveraging Data-to-Text Generation for Answering Complex Information Needs - Abstract.
Proceedings of the 2nd Joint Conference of the Information Retrieval Communities in Europe (CIRCLE 2022), 2022

2021
MLIA-LIP6@TREC-CAST2021: Feature augmentation for query recontextualization and passage ranking.
Proceedings of the Thirtieth Text REtrieval Conference, 2021

2020
Representation Learning for Large Scale Classification. (Apprentissage de représentation pour la classification large échelle).
PhD thesis, 2020

Geomstats: A Python Package for Riemannian Geometry in Machine Learning.
CoRR, 2020

A Practical Hands-on for Learning Graph Data Communities on Manifolds.
Proceedings of the Geometric Structures of Statistical Physics, Information Geometry, and Learning, 2020

Introduction to Geometric Learning in Python with Geomstats.
Proceedings of the 19th Python in Science Conference 2020 (SciPy 2020), Virtual Conference, July 6, 2020

2019
Binary Stochastic Representations for Large Multi-class Classification.
CoRR, 2019

2017
Binary Stochastic Representations for Large Multi-class Classification.
Proceedings of the Neural Information Processing - 24th International Conference, 2017


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