111 citations
- Institute for Infocomm ResearchSG23 papers
- Centre National de la Recherche ScientifiqueFR20 papers
- National University of SingaporeSG19 papers
- Université Toulouse III - Paul SabatierFR10 papers
- Agency for Science, Technology and ResearchSG9 papers
- Institut Polytechnique de BordeauxFR8 papers
- Institut de Recherche en Informatique de ToulouseFR7 papers
- Université Toulouse-I-CapitoleFR7 papers
- Université Toulouse - Jean JaurèsFR7 papers
- Bioinformatics InstituteSG6 papers
- Equipes Traitement de l'Information et SystèmesFR6 papers
- Nanyang Technological UniversitySG6 papers
11 papers · 1 filter
Developmental Predictive Coding Model for Early Infancy Mono and Bilingual Vocal Continual Learning
Xiaodan Chen, Alexandre Pitti, Mathias Quoy +1
Understanding how infants perceive speech sounds and language structures is still an open problem. Previous research in artificial neural networks has mainly focused on large datas…
Methods for Mitigating Uncertainty in Real-Time Operations of a Connected Microgrid
Subrat Prasad Panda, Blaise Genest, Arvind Easwaran +2
In this paper, we compare the effectiveness of a two-stage control strategy for the energy management system (EMS) of a grid-connected microgrid under uncertain solar irradiance an…
From Neuronal Packets to Thoughtseeds: A Hierarchical Model of Embodied Cognition in the Global Workspace
Prakash Chandra Kavi, Gorka Zamora-López, Daniel Ari Friedman
The emergence of cognition requires a framework that bridges evolutionary principles with neurocomputational mechanisms. This paper introduces the novel "thoughtseed" framework, pr…
LapisGS: Layered Progressive 3D Gaussian Splatting for Adaptive Streaming
Yuang Shi, Géraldine Morin, Simone Gasparini +1
The rise of Extended Reality (XR) requires efficient streaming of 3D online worlds, challenging current 3DGS representations to adapt to bandwidth-constrained environments. This pa…
Vanilla Gradient Descent for Oblique Decision Trees
Subrat Prasad Panda, Blaise Genest, Arvind Easwaran +1
Decision Trees (DTs) constitute one of the major highly non-linear AI models, valued, e.g., for their efficiency on tabular data. Learning accurate DTs is, however, complicated, es…
Function+Data Flow: A Framework to Specify Machine Learning Pipelines for Digital Twinning
Eduardo de Conto, Blaise Genest, Arvind Easwaran
The development of digital twins (DTs) for physical systems increasingly leverages artificial intelligence (AI), particularly for combining data from different sources or for creat…