ArXiv TLDR

Alberto Marchisio

2 papers · Latest:

Machine Learning

Focus Session: Hardware and Software Techniques for Accelerating Multimodal Foundation Models

This paper presents a multi-layered hardware/software co-design methodology to efficiently accelerate multimodal foundation models, reducing computational and memory needs.

2604.21952
Machine Learning

How Embeddings Shape Graph Neural Networks: Classical vs Quantum-Oriented Node Representations

This paper benchmarks classical and quantum-oriented node embeddings for GNNs, finding quantum embeddings better for structure-driven data and classical for social graphs.

2604.15273

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