Pubblicazioni scientifiche
SOMEnv: An R package for mining environmental monitoring datasets by Self‐Organizing Map and k‐means algorithms with a graphical user interface
Abstract: The Self-Organizing Map (SOM) algorithm belongs to the family of artificial neural networks. It is an unsupervised method that requires no a priori knowledge regarding experimental data classification. Further, it can deal with large datasets and non-linear problems, providing powerful visualization features for outcome exploration on 2D maps. For environmental pollution assessments other unsupervised techniques are widely used, such as principal component and hierarchical cluster analyses, but their application for mining large datasets and properly visualizing the results is limited, making them difficult to use for handling of large datasets obtained by high frequency environmental monitoring. This study presents an R package (SOMEnv) that allows non-expert users to elaborate by SOM algorithm environmental variables (pollutants and/or chemical physical properties) recorded with high frequency for a long monitoring period. Additionally, SOMEnv can also be used for elaborating small datasets derived from uneven sampling. All the calculations and outcome visualizations can be done using a graphical user interface (GUI), meaning that experience in R software coding is not necessary, and only a basic knowledge regarding the employed algorithm is needed to interpret the results. The benefits of the SOMEnv package are that (i) both the software environment and tool are freely available; (ii) it is able to handle large datasets; (iii) it provides heuristic rules for SOM initialization; (iv) it has a built-in GUI for performing calculations and visualizing the results. Moreover, it comes with a wide range of visualizations, several of which are dedicated to high frequency data monitoring. An example of application is presented. The package is freely available on the Comprehensive R Archive Network (CRAN) repository. Authors Sabina Licen, Marco Franzon, Tommaso Rodani, Pierluigi Barbieri Journal Microchemical Journal Publication date 31/12/2020 Consult the pubblication
Investigating Similarity Metrics for Convolutional Neural Networks in the Case of Unstructured Pruning
Abstract: Deep Neural Networks (DNNs) are essential tools of modern science and technology. The current lack of explainability of their inner workings and of principled ways to tame their architectural complexity triggered a lot of research in recent years. There is hope that, by making sense of representations in their hidden layers, we could collect insights on how to reduce model complexity—without performance degradation—by pruning useless connections. It is natural then to ask the following question: how similar are representations in pruned and unpruned models? Even small insights could help in finding principled ways to design good lightweight models, enabling significant savings of computation, memory, time and energy. In this work, we investigate empirically this problem on a wide spectrum of similarity measures, network architectures and datasets. We find that the results depend critically on the similarity measure used and we discuss briefly the origin of these differences, concluding that further investigations are required in order to make substantial advances. Autori: Alessio Ansuini, Eric Medvet, Felice Andrea Pellegrino, Marco Zullich Rivista: International Conference on Pattern Recognition Applications and Methods (ICPRAM) Data di pubblicazione: 23/12/2020 Consulta la pubblicazione
Hierarchical nucleation in deep neural networks
Abstract Deep convolutional networks (DCNs) learn meaningful representations where data that share the same abstract characteristics are positioned closer and closer. Understanding these representations and how they are generated is of unquestioned practical and theoretical interest. In this work we study the evolution of the probability density of the ImageNet dataset across the hidden layers in some stateof-the-art DCNs. We find that the initial layers generate a unimodal probability density getting rid of any structure irrelevant for classification. In subsequent layers density peaks arise in a hierarchical fashion that mirrors the semantic hierarchy of the concepts. Density peaks corresponding to single categories appear only close to the output and via a very sharp transition which resembles the nucleation process of a heterogeneous liquid. This process leaves a footprint in the probability density of the output layer where the topography of the peaks allows reconstructing the semantic relationships of the categories. Autori Diego Doimo, Aldo Glielmo, Alessio Ansuini, Alessandro Laio Rivista NIPS’20: Proceedings of the 34th International Conference on Neural Information Processing Systems Data di pubblicazione 06/12/2020 Consulta la pubblicazione