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Graph Neural Networks for Missing Data Imputation in Time Series from Meteorological Sensors
Giovanna Maria Dimitri;Irene Cappelli;Franco Scarselli;Ada Fort;Marco Gori
Application of Artificial Neural Networks methods for the analysis of sensors data has become a priority nowadays. In this paper, Graph Neural Networks are exploited for the imputation of missing data in meteorological time series. To construct the graphs, a Horizontal Visibility Graphs approach is used. The dataset is populated with the acquisitions of five sensor nodes, deployed for several mont...Show More
Building Bridges of Knowledge: Innovating Education with Automated Crossword Generation
Kamyar Zeinalipour;Tommaso Iaquinta;Giovanni Angelini;Leonardo Rigutini;Marco Maggini;Marco Gori
2023 International Conference on Machine Learning and Applications (ICMLA)
Year: 2023 | Conference Paper |
Cited by: Papers (1)
Educational crossword puzzles enhance critical thinking, vocabulary development, and concept reinforcement. They encourage independent learning, improve memorization, and foster problem-solving skills. With their multisensory approach, crossword puzzles offer a valuable educational experience. With the help of AI technology, creating high-quality, diverse crosswords is now easier, promoting enjoya...Show More
Continual Learning with Pretrained Backbones by Tuning in the Input Space
Simone Marullo;Matteo Tiezzi;Marco Gori;Stefano Melacci;Tinne Tuytelaars
2023 International Joint Conference on Neural Networks (IJCNN)
Year: 2023 | Conference Paper |
The intrinsic difficulty in adapting deep learning models to non-stationary environments limits the applicability of neural networks to real-world tasks. This issue is critical in practical supervised learning settings, such as the ones in which a pre-trained model computes projections toward a latent space where different task predictors are sequentially learned over time. As a matter of fact, in...Show More
PARTIME: Scalable and Parallel Processing Over Time with Deep Neural Networks
Enrico Meloni;Lapo Faggi;Simone Marullo;Alessandro Betti;Matteo Tiezzi;Marco Gori;Stefano Melacci
2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA)
Year: 2022 | Conference Paper |
In this paper, we present PARTIME, a software library written in Python and based on PyTorch, designed specifically to speed up neural networks whenever data is continuously streamed over time, for both learning and inference. Existing libraries are designed to exploit data-level parallelism, assuming that samples are batched, a condition that is not naturally met in applications that are based on...Show More
Domain Knowledge Alleviates Adversarial Attacks in Multi-Label Classifiers
Stefano Melacci;Gabriele Ciravegna;Angelo Sotgiu;Ambra Demontis;Battista Biggio;Marco Gori;Fabio Roli
Adversarial attacks on machine learning-based classifiers, along with defense mechanisms, have been widely studied in the context of single-label classification problems. In this paper, we shift the attention to multi-label classification, where the availability of domain knowledge on the relationships among the considered classes may offer a natural way to spot incoherent predictions, i.e., predi...Show More
Messing Up 3D Virtual Environments: Transferable Adversarial 3D Objects
Enrico Meloni;Matteo Tiezzi;Luca Pasqualini;Marco Gori;Stefano Melacci
2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA)
Year: 2021 | Conference Paper |
Cited by: Papers (2)
In the last few years, the scientific community showed a remarkable and increasing interest towards 3D Virtual Environments, training and testing Machine Learning-based models in realistic virtual worlds. On one hand, these environments could also become a mean to study the weaknesses of Machine Learning algorithms, or to simulate training settings that allow Machine Learning models to gain robust...Show More
HeMoG: A White-Box Model to Unveil the Connection between Saliency Information and Human Head Motion in Virtual Reality
Miguel Fabian Romero Rondon;Dario Zanca;Stefano Melacci;Marco Gori;Lucile Sassatelli
2021 IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR)
Year: 2021 | Conference Paper |
Cited by: Papers (1)
Immersive environments such as Virtual Reality (VR) are now a main area of interactive digital entertainment. The challenge to design personalized interactive VR systems is specifically to guide and adapt to the user's attention. Understanding the connection between the visual content and the human attentional process is therefore key. In this article, we investigate this connection by first propo...Show More
Friendly Training: Neural Networks Can Adapt Data To Make Learning Easier
2021 International Joint Conference on Neural Networks (IJCNN)
Year: 2021 | Conference Paper |
Cited by: Papers (2)
In the last decade, motivated by the success of Deep Learning, the scientific community proposed several approaches to make the learning procedure of Neural Networks more effective. When focussing on the way in which the training data are provided to the learning machine, we can distinguish between the classic random selection of stochastic gradient-based optimization and more involved techniques ...Show More
SAILenv: Learning in Virtual Visual Environments Made Simple
Enrico Meloni;Luca Pasqualini;Matteo Tiezzi;Marco Gori;Stefano Melacci
2020 25th International Conference on Pattern Recognition (ICPR)
Year: 2021 | Conference Paper |
Cited by: Papers (1)
Recently, researchers in Machine Learning algorithms, Computer Vision scientists, engineers and others, showed a growing interest in 3D simulators as a mean to artificially create experimental settings that are very close to those in the real world. However, most of the existing platforms to interface algorithms with 3D environments are often designed to setup navigation-related experiments, to st...Show More
Local Propagation in Constraint-based Neural Networks
Giuseppe Marra;Matteo Tiezzi;Stefano Melacci;Alessandro Betti;Marco Maggini;Marco Gori
2020 International Joint Conference on Neural Networks (IJCNN)
Year: 2020 | Conference Paper |
Cited by: Papers (3)
In this paper we study a constraint-based representation of neural network architectures. We cast the learning problem in the Lagrangian framework and we investigate a simple optimization procedure that is well suited to fulfil the so-called architectural constraints, learning from the available supervisions. The computational structure of the proposed Local Propagation (LP) algorithm is based on ...Show More
Developing Constrained Neural Units Over Time
2020 International Joint Conference on Neural Networks (IJCNN)
Year: 2020 | Conference Paper |
Cited by: Papers (1)
In this paper we present a foundational study on a constrained method that defines learning problems with Neural Networks in the context of the principle of least cognitive action, which very much resembles the principle of least action in mechanics. Starting from a general approach to enforce constraints into the dynamical laws of learning, this work focuses on an alternative way of defining Neur...Show More
Toward Improving the Evaluation of Visual Attention Models: a Crowdsourcing Approach
2020 International Joint Conference on Neural Networks (IJCNN)
Year: 2020 | Conference Paper |
Cited by: Papers (2)
Human visual attention is a complex phenomenon. A computational modeling of this phenomenon must take into account where people look in order to evaluate which are the salient locations (spatial distribution of the fixations), when they look in those locations to understand the temporal development of the exploration (temporal order of the fixations), and how they move from one location to another...Show More
Gravitational Laws of Focus of Attention
The understanding of the mechanisms behind focus of attention in a visual scene is a problem of great interest in visual perception and computer vision. In this paper, we describe a model of scanpath as a dynamic process which can be interpreted as a variational law somehow related to mechanics, where the focus of attention is subject to a gravitational field. The distributed virtual mass that dri...Show More
Cognitive Action Laws: The Case of Visual Features
This paper proposes a theory for understanding perceptual learning processes within the general framework of laws of nature. Artificial neural networks are regarded as systems whose connections are Lagrangian variables, namely, functions depending on time. They are used to minimize the cognitive action, an appropriate functional index that measures the agent interactions with the environment. The ...Show More
On a Convex Logic Fragment for Learning and Reasoning
Francesco Giannini;Michelangelo Diligenti;Marco Gori;Marco Maggini
IEEE Transactions on Fuzzy Systems
Cited by: Papers (8)
In this paper, we introduce the convex fragment of Łukasiewicz logic and discuss its possible applications in different learning schemes. The provided theoretical results are highly general because they can be exploited in any learning framework involving logical constraints. The method is of particular interest since the fragment guarantees to deal with convex constraints, which are shown to be e...Show More
A Constrained-Based Approach to Machine Learning
2018 14th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)
Year: 2018 | Conference Paper |
Cited by: Papers (1)
This paper promotes a constrained-based approach to machine learning as a natural evolution to classic distinction between supervised, unsupervised and semi-supervised learning. In particular, in addition to the unification of symbolic and sub-symbolic processing, it is shown that the notion of constraint opens the doors to a truly new way of discovering the solution that relies on the Lagrangian ...Show More
Learning in Variable-Dimensional Spaces
This paper proposes a unified approach to learning in environments in which patterns can be represented in variable-dimension domains, which nicely includes the case in which there are missing features. The proposal is based on the representation of the environment by pointwise constraints that are shown to model naturally pattern relationships that come out in problems of information retrieval, c...Show More
Online learning as an LQG optimal control problem with random matrices
Giorgio Gnecco;Alberto Bemporad;Marco Gori;Rita Morisi;Marcello Sanguineti
In this paper, we combine optimal control theory and machine learning techniques to propose and solve an optimal control formulation of online learning from supervised examples, which are used to learn an unknown vector parameter modeling the relationship between the input examples and their outputs. We show some connections of the problem investigated with the classical LQG optimal control proble...Show More
Learning With Mixed Hard/Soft Pointwise Constraints
Giorgio Gnecco;Marco Gori;Stefano Melacci;Marcello Sanguineti
A learning paradigm is proposed and investigated, in which the classical framework of learning from examples is enhanced by the introduction of hard pointwise constraints, i.e., constraints imposed on a finite set of examples that cannot be violated. Such constraints arise, e.g., when requiring coherent decisions of classifiers acting on different views of the same pattern. The classical examples ...Show More
On-Line Video Motion Estimation by Invariant Receptive Inputs
2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops
Year: 2014 | Conference Paper |
Cited by: Papers (1)
In this paper, we address the problem of estimating the optical flow in long-term video sequences. We devise a computational scheme that exploits the idea of receptive fields, in which the pixel flow does not only depends on the brightness level of the pixel itself, but also on neighborhood-related information. Our approach relies on the definition of receptive units that are invariant to affine t...Show More
Graph and Manifold Co-regularization
2013 12th International Conference on Machine Learning and Applications
Year: 2013 | Conference Paper |
Classical foundations of Statistical Learning Theory rely on the assumption that the input patterns are independently and identically distributed. However, in many applications, the inputs, represented as feature vectors, are also embedded into a network of pair wise relations. Transductive approaches like graph regularization rely on the network topology without considering the feature vectors. S...Show More
Collective Classification Using Semantic Based Regularization
2013 12th International Conference on Machine Learning and Applications
Year: 2013 | Conference Paper |
Cited by: Papers (1)
Semantic Based Regularization (SBR) is a framework for injecting prior knowledge expressed as FOL clauses into a semi-supervised learning problem. The prior knowledge is converted into a set of continuous constraints, which are enforced during training. SBR employs the prior knowledge only at training time, hoping that the learning process is able to encode the knowledge via the training data into...Show More
Learning with Box Kernels
Supervised examples and prior knowledge on regions of the input space have been profitably integrated in kernel machines to improve the performance of classifiers in different real-world contexts. The proposed solutions, which rely on the unified supervision of points and sets, have been mostly based on specific optimization schemes in which, as usual, the kernel function operates on points only. ...Show More
Constraint Verification With Kernel Machines
Based on a recently proposed framework of learning from constraints using kernel-based representations, in this brief, we naturally extend its application to the case of inferences on new constraints. We give examples for polynomials and first-order logic by showing how new constraints can be checked on the basis of given premises and data samples. Interestingly, this gives rise to a perceptual lo...Show More
Unsupervised Learning by Minimal Entropy Encoding
Following basic principles of information-theoretic learning, in this paper, we propose a novel approach to data clustering, referred to as minimal entropy encoding (MEE), which is based on a set of functions (features) projecting each input onto a minimum entropy configuration (code). Inspired by traditional parsimony principles, we seek solutions in reproducing kernel Hilbert spaces and then we ...Show More
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