Neural Networks and Support Vector Machines
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In this paper, we study the embedded feature selection problem in linear Support Vector Machines (SVMs), in which a cardinality constraint is employed, leading to an interpretable classification model. The problem is NP-hard due to the presence of the cardinality constraint, even though the...
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In this work, we consider minimizing the average of a very large number of smooth and possibly non-convex functions, and we focus on two widely used minibatch frameworks to tackle this optimization problem: Incremental Gradient (IG) and Random Reshuffling (RR). We define ease-controlled...
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Abstract: Many datasets are best represented as graphs of entities connected by relationships rather than as a single uniform dataset or table. Graph Neural Networks (GNNs) have been used to...
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Speaker: Federica Baccini
When: 10 Luglio 2023
Where: Aula Magna - DIAG
Title: Analysis of multiple relations in multilayer and higher-order networks
Abstract: This seminar focuses on the extension of...
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Title: Integrating Graph Representation Learning and Diffusion: Computational Models and Applications in Chemistry and Medicine.AbstractThe talk will focus on recent methodological novelties and challenges in graph...
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The RSTLess research group is a dynamic and innovative team of researchers from Sapienza University of Rome, led by Professor Fabrizio Silvestri.
Our focus is on the cutting-edge fields of Deep Learning, Information Retrieval, Graph Neural Networks, and Natural Language Processing, with a...
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Motivation: Gene-disease associations are fundamental for understanding disease etiology and developing effective interventions and treatments. Identifying genes not yet associated with a disease due to a lack of studies is a challenging task in which prioritization based on prior knowledge is an...
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Here we present EdgeSHAPer, a workflow for explaining graph neural networks by approximating Shapley values using Monte Carlo sampling. In this protocol, we describe steps to execute Python scripts for a chemical dataset from the original publication; however, this approach is also applicable to...
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Graph neural networks (GNNs) recursively propagate signals along the edges of an input graph, integrate node feature information with graph structure, and learn object representations. Like other deep neural network models, GNNs have notorious black box character. For GNNs, only few approaches are...
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Abstract
Given the recent proliferation of disinformation online, there has been growing research interest in automatically debunking rumors, false claims, and "fake news". A number of fact-checking initiatives have been launched so far, both manual and automatic, but...
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