Brain-Grasp: Graph-Based Saliency Priors for Improved FMRI-Based Visual Brain Decoding
Conference
Mohammad Moradi, Morteza Moradi, Marco Grassia, Giuseppe Mangioni
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1 min readLatent Geometry-Driven Network Automata for Complex Network Dismantling
Complex networks model the structure and function of critical technological, biological, and communication systems. Network dismantling, the targeted removal of nodes to fragment a …
Conference
Thomas Adler, Marco Grassia, Ziheng Liao, Giuseppe Mangioni et al.
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1 min readWhat is Wrong with Visual Brain Decoding? A Saliency-based Investigation
Recent advancements in diffusion-based image generation and large vision/language models have revolutionized visual brain decoding (VBD), driving progress in neuroscience and …
Conference
Mohammad Moradi, Morteza Moradi, Marco Grassia, Giuseppe Mangioni
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1 min readSaliency Matters: From Nodes to Objects
Due to their intrinsic capabilities in capturing, modeling, and representing relationships between pieces of information, graphs have been used in a wide variety of application …
Conference
Mohammad Moradi, Morteza Moradi, Marco Grassia, Giuseppe Mangioni
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1 min readMachine Learning Supernovae’s Progenitor Characterization
In this work, we present a Deep Learning framework to predict the progenitor star’s characteristics of Supernovae (SNe) from their observed light curves. This task is crucial for …
Conference
Marco Grassia, Stefano Pio Cosentino, Maria Letizia Pumo, Giuseppe Mangioni
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1 min readHow Well is Human Attention Preserved in fMRI-Based VisualBrain Decoding?
Visual brain decoding (VBD) seeks to reconstruct visual stimuli from neural signals, yet current evaluation methods primarily focus on pixel-level or semantic fidelity, often …
Conference
Mohammad Moradi, Morteza Moradi, Marco Grassia, Giuseppe Mangioni
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1 min readHow Well is Human Attention Preserved in fMRI-Based Visual Brain Decoding?
This paper investigates the preservation of human visual attention in brain activity patterns captured through fMRI during visual decoding tasks. We explore how well attention …
Conference
Mohammad Moradi, Morteza Moradi, Marco Grassia, Giuseppe Mangioni
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1 min readEdge Dismantling with Geometric Reinforcement Learning
The robustness of networks plays a crucial role in various applications. Network dismantling, the process of strategically removing nodes or edges to maximize damage, is a known …
Conference
Marco Grassia, Giuseppe Mangioni
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1 min readCoreGDM: Geometric Deep Learning Network Decycling and Dismantling
Network dismantling deals with the removal of nodes or edges to disrupt the largest connected component of a network. In this work we introduce CoreGDM, a trainable algorithm for …
Conference
Marco Grassia, Giuseppe Mangioni
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1 min readGeometric Deep Learning Graph Pruning to Speed-Up the Run-Time of Maximum Clique Enumerarion Algorithms
In this paper we propose a method to reduce the running time to solve the Maximum Clique Enumeration (MCE) problem. Specifically, given a network we employ geometric deep learning …
Conference
Andrea Arciprete, Vincenza Carchiolo, Daniela Chiavetta, Marco Grassia et al.
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1 min read