73 citations
- Transylvania University of BrașovRO6 papers
- Centre National de la Recherche ScientifiqueFR3 papers
- Nagoya City UniversityJP2 papers
- National Astronomical Observatory of JapanJP2 papers
- Siemens (Romania)RO2 papers
- Université Paris CitéFR2 papers
- Arcetri Astrophysical ObservatoryIT1 paper
- Astrophysique, Instrumentation et ModélisationFR1 paper
- California Polytechnic State UniversityUS1 paper
- CEA Paris-SaclayFR1 paper
- Center for Astrophysics Harvard & SmithsonianUS1 paper
- Chinese Academy of SciencesCN1 paper
8 papers · 1 filter
Boosting of Classification Models with Human-in-the-Loop Computational Visual Knowledge Discovery
Alice Williams, Boris Kovalerchuk
High-risk artificial intelligence and machine learning classification tasks, such as healthcare diagnosis, require accurate and interpretable prediction models. However, classifier…
Synthetic Data Generation and Automated Multidimensional Data Labeling for AI/ML in General and Circular Coordinates
Alice Williams, Boris Kovalerchuk
Insufficient amounts of available training data is a critical challenge for both development and deployment of artificial intelligence and machine learning (AI/ML) models. This pap…
Learning in Convolutional Neural Networks Accelerated by Transfer Entropy
Adrian Moldovan, Angel Caţaron, Răzvan Andonie
Recently, there is a growing interest in applying Transfer Entropy (TE) in quantifying the effective connectivity between artificial neurons. In a feedforward network, the TE can b…
Learning in Feedforward Neural Networks Accelerated by Transfer Entropy
Adrian Moldovan, Angel Caţaron, Răzvan Andonie
Current neural networks architectures are many times harder to train because of the increasing size and complexity of the used datasets. Our objective is to design more efficient t…
Semiotic Aggregation in Deep Learning
Bogdan Musat, Razvan Andonie
Convolutional neural networks utilize a hierarchy of neural network layers. The statistical aspects of information concentration in successive layers can bring an insight into the…
Survey of explainable machine learning with visual and granular methods beyond quasi-explanations
Boris Kovalerchuk, Muhammad Aurangzeb Ahmad, Ankur Teredesai
This paper surveys visual methods of explainability of Machine Learning (ML) with focus on moving from quasi-explanations that dominate in ML to domain-specific explanation support…