11 citations · 15 across the 7 of their papers we have counts for
7 papers
SeRpEnt: Selective Resampling for Expressive State Space Models
Stefano Rando, Luca Romani, Matteo Migliarini +3
State Space Models (SSMs) have recently enjoyed a rise to prominence in the field of deep learning for sequence modeling, especially as an alternative to Transformers. Their succes…
Hyperbolic Learning with Multimodal Large Language Models
Paolo Mandica, Luca Franco, Konstantinos Kallidromitis +2
Hyperbolic embeddings have demonstrated their effectiveness in capturing measures of uncertainty and hierarchical relationships across various deep-learning tasks, including image…
Hyperbolic Active Learning for Semantic Segmentation under Domain Shift
Luca Franco, Paolo Mandica, Konstantinos Kallidromitis +4
We introduce a hyperbolic neural network approach to pixel-level active learning for semantic segmentation. Analysis of the data statistics leads to a novel interpretation of the h…
HYperbolic Self-Paced Learning for Self-Supervised Skeleton-based Action Representations
Luca Franco, Paolo Mandica, Bharti Munjal +1
Self-paced learning has been beneficial for tasks where some initial knowledge is available, such as weakly supervised learning and domain adaptation, to select and order the train…
Under the Hood of Transformer Networks for Trajectory Forecasting
Luca Franco, Leonardo Placidi, Francesco Giuliari +3
Transformer Networks have established themselves as the de-facto state-of-the-art for trajectory forecasting but there is currently no systematic study on their capability to model…
Deep learning for laboratory earthquake prediction and autoregressive forecasting of fault zone stress
Laura Laurenti, Elisa Tinti, Fabio Galasso +2
Earthquake forecasting and prediction have long and in some cases sordid histories but recent work has rekindled interest based on advances in early warning, hazard assessment for…
Space-Time-Separable Graph Convolutional Network for Pose Forecasting
Theodoros Sofianos, Alessio Sampieri, Luca Franco +1
Human pose forecasting is a complex structured-data sequence-modelling task, which has received increasing attention, also due to numerous potential applications. Research has main…