4 papers
Saliency-Guided Replay for Continual Learning in Deep Spiking Neural Networks
Francesco Panetta, Federico Di Valerio, Michela Proietti
Spiking Neural Networks (SNNs) are promising for energy-efficient neuromorphic computing, but they still suffer from catastrophic forgetting when learning tasks sequentially. This…
Vision Models Capture Complementary Representations in Children That Converge in Adults
Domenic Bersch, Timothy Schaumlöffel, Michela Proietti +11
How the relationship between image-text aligned and vision-only models changes across development remains unknown. We applied time-resolved representational similarity analysis to…
CIP-Net: Continual Interpretable Prototype-based Network
Federico Di Valerio, Michela Proietti, Alessio Ragno +1
Continual learning constrains models to learn new tasks over time without forgetting what they have already learned. A key challenge in this setting is catastrophic forgetting, whe…
Fine-grained Analysis of Brain-LLM Alignment through Input Attribution
Michela Proietti, Roberto Capobianco, Mariya Toneva
Understanding the alignment between large language models (LLMs) and human brain activity can reveal computational principles underlying language processing. We introduce a fine-gr…