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Found 4 papers, 1 papers with code
From Extreme Multi-label to Multi-class: A Hierarchical Approach for Automated ICD-10 Coding Using Phrase-level Attention
With our proposed approach, interpretability is achieved not through implicitly learned attention scores but by attributing each prediction to a particular sentence and words selected by human coders.
Human Attention Maps for Text Classification: Do Humans and Neural Networks Focus on the Same Words?
Motivated by human attention, computational attention mechanisms have been designed to help neural networks adjust their focus on specific parts of the input data.
Reducing Computation in Recurrent Networks by Selectively Updating State Neurons
As a result, even for high-dimensional hidden states, all dimensions are updated at each timestep regardless of the recurrent memory cell.
Adaptive-Halting Policy Network for Early Classification
Early classification of time series is the prediction of the class label of a time series before it is observed in its entirety.
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