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- Istituto Nazionale di Fisica Nucleare, Sezione di TriesteIT79 papers
- Trieste Astronomical ObservatoryIT79 papers
- Institute for Fundamental Physics of the UniverseIT78 papers
- Scuola Internazionale Superiore di Studi AvanzatiIT78 papers
- Ludwig-Maximilians-Universität MünchenDE75 papers
- Max Planck Institute for AstronomyDE75 papers
- University of BolognaIT75 papers
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- Astronomical Observatory of CapodimonteIT73 papers
- Brera Astronomical ObservatoryIT73 papers
- California Institute of TechnologyUS73 papers
5 papers · 1 filter
IMPACTX: improving model performance by appropriately constraining the training with teacher explanations
Andrea Apicella, Salvatore Giugliano, Francesco Isgrò +2
The eXplainable Artificial Intelligence (XAI) research predominantly concentrates to provide explainations about AI model decisions, especially Deep Learning (DL) models. However,…
The GECo algorithm for Graph Neural Networks Explanation
Salvatore Calderaro, Domenico Amato, Giosuè Lo Bosco +2
Graph Neural Networks (GNNs) are powerful models that can manage complex data sources and their interconnection links. One of GNNs' main drawbacks is their lack of interpretability…
SDCOR: Scalable Density-based Clustering for Local Outlier Detection in Massive-Scale Datasets
Sayyed Ahmad Naghavi Nozad, Maryam Amir Haeri, Gianluigi Folino
This paper presents a batch-wise density-based clustering approach for local outlier detection in massive-scale datasets. Unlike the well-known traditional algorithms, which assume…
Sequential Variational Autoencoders for Collaborative Filtering
Noveen Sachdeva, Giuseppe Manco, Ettore Ritacco +1
Variational autoencoders were proven successful in domains such as computer vision and speech processing. Their adoption for modeling user preferences is still unexplored, although…
Outlying Property Detection with Numerical Attributes
Fabrizio Angiulli, Fabio Fassetti, Luigi Palopoli +1
The outlying property detection problem is the problem of discovering the properties distinguishing a given object, known in advance to be an outlier in a database, from the other…