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of 3
pro vyhledávání: '"Tuveri, Francesco"'
In this work we present a deep learning approach to conduct hypothesis-free, transcriptomics-based matching of drugs for diseases. Our proposed neural network architecture is trained on approved drug-disease indications, taking as input the relevant
Externí odkaz:
http://arxiv.org/abs/2303.11695
Autor:
Ferroni, Giacomo, Turpault, Nicolas, Azcarreta, Juan, Tuveri, Francesco, Serizel, Romain, Bilen, Çagdaş, Krstulović, Sacha
The ranking of sound event detection (SED) systems may be biased by assumptions inherent to evaluation criteria and to the choice of an operating point. This paper compares conventional event-based and segment-based criteria against the Polyphonic So
Externí odkaz:
http://arxiv.org/abs/2010.13648
This work defines a new framework for performance evaluation of polyphonic sound event detection (SED) systems, which overcomes the limitations of the conventional collar-based event decisions, event F-scores and event error rates. The proposed frame
Externí odkaz:
http://arxiv.org/abs/1910.08440