28 citations · 35 across the 14 of their papers we have counts for
16 papers
Benchmarking Graph Neural Networks in Solving Hard Constraint Satisfaction Problems
Geri Skenderi, Lorenzo Buffoni, Francesco D'Amico +6
Graph neural networks (GNNs) are increasingly applied to hard optimization problems, often claiming superiority over classical heuristics. However, such claims risk being unsolid d…
Hyperbolic Graph Neural Networks Under the Microscope: The Role of Geometry-Task Alignment
Dionisia Naddeo, Jonas Linkerhägner, Nicola Toschi +2
Many complex networks exhibit hierarchical, tree-like structures, making hyperbolic space a natural candidate wherein to learn representations of them. Based on this observation, H…
A Geometric Perspective on the Difficulties of Learning GNN-based SAT Solvers
Geri Skenderi
Graph Neural Networks (GNNs) have gathered increasing interest as learnable solvers of Boolean Satisfiability Problems (SATs), operating on graph representations of logical formula…
Sub-graph Based Diffusion Model for Link Prediction
Hang Li, Wei Jin, Geri Skenderi +4
Denoising Diffusion Probabilistic Models (DDPMs) represent a contemporary class of generative models with exceptional qualities in both synthesis and maximizing the data likelihood…
Disentangled Latent Spaces Facilitate Data-Driven Auxiliary Learning
Geri Skenderi, Luigi Capogrosso, Andrea Toaiari +3
Auxiliary tasks facilitate learning in situations where data is scarce or the principal task of interest is extremely complex. This idea is primarily inspired by the improved gener…
Graph-level Representation Learning with Joint-Embedding Predictive Architectures
Geri Skenderi, Hang Li, Jiliang Tang +1
Joint-Embedding Predictive Architectures (JEPAs) have recently emerged as a novel and powerful technique for self-supervised representation learning. They aim to learn an energy-ba…
Neuro-symbolic Empowered Denoising Diffusion Probabilistic Models for Real-time Anomaly Detection in Industry 4.0
Luigi Capogrosso, Alessio Mascolini, Federico Girella +10
Industry 4.0 involves the integration of digital technologies, such as IoT, Big Data, and AI, into manufacturing and industrial processes to increase efficiency and productivity. A…
On the use of learning-based forecasting methods for ameliorating fashion business processes: A position paper
Geri Skenderi, Christian Joppi, Matteo Denitto +1
The fashion industry is one of the most active and competitive markets in the world, manufacturing millions of products and reaching large audiences every year. A plethora of busin…
Leveraging commonsense for object localisation in partial scenes
Francesco Giuliari, Geri Skenderi, Marco Cristani +2
We propose an end-to-end solution to address the problem of object localisation in partial scenes, where we aim to estimate the position of an object in an unknown area given only…
Toward Smart Doors: A Position Paper
Luigi Capogrosso, Geri Skenderi, Federico Girella +2
Conventional automatic doors cannot distinguish between people wishing to pass through the door and people passing by the door, so they often open unnecessarily. This leads to the…
Pose Forecasting in Industrial Human-Robot Collaboration
Alessio Sampieri, Guido D'Amely, Andrea Avogaro +5
Pushing back the frontiers of collaborative robots in industrial environments, we propose a new Separable-Sparse Graph Convolutional Network (SeS-GCN) for pose forecasting. For the…
POP: Mining POtential Performance of new fashion products via webly cross-modal query expansion
Christian Joppi, Geri Skenderi, Marco Cristani
We propose a data-centric pipeline able to generate exogenous observation data for the New Fashion Product Performance Forecasting (NFPPF) problem, i.e., predicting the performance…
The multi-modal universe of fast-fashion: the Visuelle 2.0 benchmark
Geri Skenderi, Christian Joppi, Matteo Denitto +2
We present Visuelle 2.0, the first dataset useful for facing diverse prediction problems that a fast-fashion company has to manage routinely. Furthermore, we demonstrate how the us…
Spatial Commonsense Graph for Object Localisation in Partial Scenes
Francesco Giuliari, Geri Skenderi, Marco Cristani +2
We solve object localisation in partial scenes, a new problem of estimating the unknown position of an object (e.g. where is the bag?) given a partial 3D scan of a scene. The propo…
MovingFashion: a Benchmark for the Video-to-Shop Challenge
Marco Godi, Christian Joppi, Geri Skenderi +1
Retrieving clothes which are worn in social media videos (Instagram, TikTok) is the latest frontier of e-fashion, referred to as "video-to-shop" in the computer vision literature.…
Well Googled is Half Done: Multimodal Forecasting of New Fashion Product Sales with Image-based Google Trends
Geri Skenderi, Christian Joppi, Matteo Denitto +1
New fashion product sales forecasting is a challenging problem that involves many business dynamics and cannot be solved by classical forecasting approaches. In this paper, we inve…