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Found 4 papers, 0 papers with code
Decoupling Feature Extraction and Classification Layers for Calibrated Neural Networks
Deep Neural Networks (DNN) have shown great promise in many classification applications, yet are widely known to have poorly calibrated predictions when they are over-parametrized.
Neural machine translation for automated feedback on children's early-stage writing
In this work, we address the problem of assessing and constructing feedback for early-stage writing automatically using machine learning.
On the role of Model Uncertainties in Bayesian Optimization
In this work, we provide an extensive study of the relationship between the BO performance (regret) and uncertainty calibration for popular surrogate models and compare them across both synthetic and real-world experiments.
Topic Model Robustness to Automatic Speech Recognition Errors in Podcast Transcripts
We then observe how the cosine similarities decrease as transcription noise increases and conclude that even when automatic speech recognition transcripts are erroneous, it is still possible to obtain high-quality topic embeddings from the transcriptions.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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