34 Result(s)
within
Chapter
Conference Paper
Luca Rossi
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Chapter and Conference Paper
C2N-ABDP: Cluster-to-Node Attention-Based Differentiable Pooling
Graph neural networks have achieved state-of-the-art performance in various graph based tasks, including classification and regression at both node and graph level. In the context of graph classification, grap...
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Chapter and Conference Paper
MONstEr: A Deep Learning-Based System for the Automatic Generation of Gaming Assets
In recent years, we have witnessed the spread of computer graphics techniques, used as a background map for movies and video games. Nevertheless, when creating 3D models with conventional computer graphics sof...
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Chapter and Conference Paper
FakeNED: A Deep Learning Based-System for Fake News Detection from Social Media
Social networks are increasingly present in our daily life. They allow us to remain in contact with friends regardless of distances, to share posts, images, videos, to be part of communities or come across art...
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Chapter and Conference Paper
A Novel Graph Kernel Based on the Wasserstein Distance and Spectral Signatures
Spectral signatures have been used with great success in computer vision to characterise the local and global topology of 3D meshes. In this paper, we propose to use two widely used spectral signatures, the He...
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Chapter and Conference Paper
Classifying Me Softly: A Novel Graph Neural Network Based on Features Soft-Alignment
Graph neural networks are increasingly becoming the framework of choice for graph-based machine learning. In this paper we propose a new graph neural network architecture based on the soft-alignment of the gra...
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Chapter and Conference Paper
A Deep Learning-Based System for Product Recognition in Intelligent Retail Environment
This work proposes a pipeline that aims to recognize the products in a shelf, at the level of the single SKU (Stock Kee** Unit), starting from a photo of that shelf. It is composed of a first neural network ...
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Chapter and Conference Paper
Estimating the Manifold Dimension of a Complex Network Using Weyl’s Law
The dimension of the space underlying real-world networks has been shown to strongly influence the networks structural properties, from the degree distribution to the way the networks respond to diffusion and ...
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Chapter and Conference Paper
People Counting on Low Cost Embedded Hardware During the SARS-CoV-2 Pandemic
Detecting and tracking people is a challenging task in a persistent crowded environment as retail, airport or station, for human behaviour analysis of security purposes. Especially during the global spread of ...
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Chapter and Conference Paper
The Average Mixing Kernel Signature
We introduce the Average Mixing Kernel Signature (AMKS), a novel signature for points on non-rigid three-dimensional shapes based on the average mixing kernel and continuous-time quantum walks. The average mix...
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Chapter and Conference Paper
GMNet: Graph Matching Network for Large Scale Part Semantic Segmentation in the Wild
The semantic segmentation of parts of objects in the wild is a challenging task in which multiple instances of objects and multiple parts within those objects must be detected in the scene. This problem remain...
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Chapter and Conference Paper
A Mixed Entropy Local-Global Reproducing Kernel for Attributed Graphs
In this paper, we develop a new mixed entropy local-global reproducing kernel for vertex attributed graphs based on depth-based representations that naturally reflect both local and global entropy based graph ...
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Chapter and Conference Paper
A Preliminary Survey of Analyzing Dynamic Time-Varying Financial Networks Using Graph Kernels
In this paper, we investigate whether graph kernels can be used as a means of analyzing time-varying financial market networks. Specifically, we aim to identify the significant financial incident that changes ...
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Chapter and Conference Paper
Measuring Vertex Centrality Using the Holevo Quantity
In recent years, the increasing availability of data describing the dynamics of real-world systems led to a surge of interest in the complex networks of interactions that emerge from such systems. Several meas...
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Chapter and Conference Paper
Adaptive Feature Selection Based on the Most Informative Graph-Based Features
In this paper, we propose a novel method to adaptively select the most informative and least redundant feature subset, which has strong discriminating power with respect to the target label. Unlike most tradit...
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Chapter and Conference Paper
A Nested Alignment Graph Kernel Through the Dynamic Time War** Framework
In this paper, we propose a novel nested alignment graph kernel drawing on depth-based complexity traces and the dynamic time war** framework. Specifically, for a pair of graphs, we commence by computing the...
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Chapter and Conference Paper
The Average Mixing Matrix Signature
Laplacian-based descriptors, such as the Heat Kernel Signature and the Wave Kernel Signature, allow one to embed the vertices of a graph onto a vectorial space, and have been successfully used to find the opti...
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Chapter and Conference Paper
Edge Centrality via the Holevo Quantity
In the study of complex networks, vertex centrality measures are used to identify the most important vertices within a graph. A related problem is that of measuring the centrality of an edge. In this paper, we...
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Chapter and Conference Paper
A Quantum Jensen-Shannon Graph Kernel Using Discrete-Time Quantum Walks
In this paper, we develop a new graph kernel by using the quantum Jensen-Shannon divergence and the discrete-time quantum walk. To this end, we commence by performing a discrete-time quantum walk to compute a ...
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Chapter and Conference Paper
An Edge-Based Matching Kernel Through Discrete-Time Quantum Walks
In this paper, we propose a new edge-based matching kernel for graphs by using discrete-time quantum walks. To this end, we commence by transforming a graph into a directed line graph. The reasons of using the...
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Chapter and Conference Paper
Transitive State Alignment for the Quantum Jensen-Shannon Kernel
Kernel methods provide a convenient way to apply a wide range of learning techniques to complex and structured data by shifting the representational problem from one of finding an embedding of the data to that...
