N
Nicola Pezzotti
Researcher at Philips
Publications - 43
Citations - 1639
Nicola Pezzotti is an academic researcher from Philips. The author has contributed to research in topics: Visualization & Mass cytometry. The author has an hindex of 17, co-authored 39 publications receiving 1184 citations. Previous affiliations of Nicola Pezzotti include University of Brescia & Delft University of Technology.
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Approximated and User Steerable tSNE for Progressive Visual Analytics
Nicola Pezzotti,Boudewijn P. F. Lelieveldt,Laurens van der Maaten,Thomas Höllt,Elmar Eisemann,Anna Vilanova +5 more
TL;DR: In this article, a controllable t-Distributed Stochastic Neighbor Embedding (tSNE) is introduced to enable interactive data exploration, where the user can decide on local refinements and steer the approximation level during the analysis.
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Approximated and User Steerable tSNE for Progressive Visual Analytics
Nicola Pezzotti,Boudewijn P. F. Lelieveldt,Laurens van der Maaten,Thomas Höllt,Elmar Eisemann,Anna Vilanova +5 more
TL;DR: A controllable tSNE approximation (A-tSNE), which trades off speed and accuracy, to enable interactive data exploration and offers real-time visualization techniques, including a density-based solution and a Magic Lens to inspect the degree of approximation.
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Visual analysis of mass cytometry data by hierarchical stochastic neighbour embedding reveals rare cell types
Vincent van Unen,Thomas Höllt,Thomas Höllt,Nicola Pezzotti,Na Li,Marcel J. T. Reinders,Elmar Eisemann,Frits Koning,Anna Vilanova,Boudewijn P. F. Lelieveldt,Boudewijn P. F. Lelieveldt +10 more
TL;DR: Hierarchical Stochastic Neighbor Embedding (HSNE) is introduced, a method for analysis of mass cytometry data that can handle very large datasets and allows their intuitive and hierarchical exploration.
Journal ArticleDOI
DeepEyes: Progressive Visual Analytics for Designing Deep Neural Networks
Nicola Pezzotti,Thomas Höllt,Jan C. van Gemert,Boudewijn P. F. Lelieveldt,Elmar Eisemann,Anna Vilanova +5 more
TL;DR: This paper presents DeepEyes, a Progressive Visual Analytics system that supports the design of neural networks during training, and presents novel visualizations, supporting the identification of layers that learned a stable set of patterns and, therefore, are of interest for a detailed analysis.
Journal ArticleDOI
Hierarchical stochastic neighbor embedding
TL;DR: This work introduces Hierarchical Stochastic Neighbor Embedding (Hierarchical‐SNE), a hierarchical representation of the data that incorporates the well‐known mantra of Overview‐First, Details‐On‐Demand in non‐linear dimensionality reduction, and explains how it scales to the analysis of big datasets.