Multimodal Parameter-Efficient Few-Shot Class Incremental Learning

no code implementations • 8 Mar 2023 • Marco D'Alessandro, Alberto Alonso, Enrique Calabrés, Mikel Galar

Few-Shot Class Incremental Learning (FSCIL) is a challenging continual learning task, where limited training examples are available during several learning sessions.

Few-Shot Class-Incremental Learning Incremental Learning +1

Amortized Bayesian model comparison with evidential deep learning

1 code implementation • 22 Apr 2020 • Stefan T. Radev, Marco D'Alessandro, Ulf K. Mertens, Andreas Voss, Ullrich Köthe, Paul-Christian Bürkner

This makes the method particularly effective in scenarios where model fit needs to be assessed for a large number of datasets, so that per-dataset inference is practically infeasible. Finally, we propose a novel way to measure epistemic uncertainty in model comparison problems.

A state space approach to dynamic modeling of mouse-tracking data

1 code implementation • 19 Jul 2019 • Antonio Calcagnì, Luigi Lombardi, Marco D'Alessandro

Mouse-tracking recording techniques are becoming very attractive in experimental psychology.

Applications Computation 62M10, 60G35, 62M05, 62P15