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Regressing Relative Fine-Grained Change for Sub-Groups in Unreliable Heterogeneous Data Through Deep Multi-Task Metric Learning
Fine-Grained Change Detection and Regression Analysis are essential in many applications of ArtificialIntelligence.
Less Is More: Improved RNN-T Decoding Using Limited Label Context and Path Merging
End-to-end models that condition the output label sequence on all previously predicted labels have emerged as popular alternatives to conventional systems for automatic speech recognition (ASR).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Understanding and Exploiting Dependent Variables with Deep Metric Learning
Deep Metric Learning (DML) approaches learn to represent inputs to a lower-dimensional latent space such that the distance between representations in this space corresponds with a predefined notion of similarity.
Deep Learning vs. Traditional Computer Vision
However, that is not to say that the traditional computer vision techniques which had been undergoing progressive development in years prior to the rise of DL have become obsolete.
