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A semi-supervised algorithm for improving the consistency of crowdsourced datasets: The COVID-19 case study on respiratory disorder classification

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A semi-supervised algorithm for improving the consistency of crowdsourced datasets: The COVID-19 case study on respiratory disorder classification

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Estimating The Degree of Sleepiness by Integrating Articulatory Feature Knowledge In Raw Waveform Based CNNs

Learning stereo reconstruction with deep neural networks

The Devil is in the Detail: A Framework for Macroscopic Prediction via Microscopic Models

Exploiting semi-supervised training through a dropout regularization in end-to-end speech recognition

Classification of degenerative parkinsonism subtypes by support-vector-machine analysis and striatal I-123-FP-CIT indices

Unsupervised Learning of Phase-Change-Based Neuromorphic Systems

Nanoscale topography determines the capillary assembly of nanoparticles

Semi-supervised Learning with Semantic Knowledge Extraction for Improved Speech Recognition in Air Traffic Control

Semi-supervised Learning with Semantic Knowledge Extraction for Improved Speech Recognition in Air Traffic Control

iSCHRUNK – In Silico Approach to Characterization and Reduction of Uncertainty in the Kinetic Models of Genome-scale Metabolic Networks

Learning stereo reconstruction with deep neural networks

Nanoscale topography determines the capillary assembly of nanoparticles

Estimating The Degree of Sleepiness by Integrating Articulatory Feature Knowledge In Raw Waveform Based CNNs

Classification of degenerative parkinsonism subtypes by support-vector-machine analysis and striatal I-123-FP-CIT indices

Unsupervised Learning of Phase-Change-Based Neuromorphic Systems

Semi-supervised Learning with Semantic Knowledge Extraction for Improved Speech Recognition in Air Traffic Control

Exploiting semi-supervised training through a dropout regularization in end-to-end speech recognition

Semi-supervised Learning with Semantic Knowledge Extraction for Improved Speech Recognition in Air Traffic Control

iSCHRUNK – In Silico Approach to Characterization and Reduction of Uncertainty in the Kinetic Models of Genome-scale Metabolic Networks

The Devil is in the Detail: A Framework for Macroscopic Prediction via Microscopic Models