2 citations · 2 across the 3 of their papers we have counts for
4 papers
MixMAS: A Framework for Sampling-Based Mixer Architecture Search for Multimodal Fusion and Learning
Abdelmadjid Chergui, Grigor Bezirganyan, Sana Sellami +2
Choosing a suitable deep learning architecture for multimodal data fusion is a challenging task, as it requires the effective integration and processing of diverse data types, each…
Multimodal Learning with Uncertainty Quantification based on Discounted Belief Fusion
Grigor Bezirganyan, Sana Sellami, Laure Berti-Équille +1
Multimodal AI models are increasingly used in fields like healthcare, finance, and autonomous driving, where information is drawn from multiple sources or modalities such as images…
LUMA: A Benchmark Dataset for Learning from Uncertain and Multimodal Data
Grigor Bezirganyan, Sana Sellami, Laure Berti-Équille +1
Multimodal Deep Learning enhances decision-making by integrating diverse information sources, such as texts, images, audio, and videos. To develop trustworthy multimodal approaches…
Linguistic features for sentence difficulty prediction in ABSA
Adrian-Gabriel Chifu, Sébastien Fournier
One of the challenges of natural language understanding is to deal with the subjectivity of sentences, which may express opinions and emotions that add layers of complexity and nua…