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Found 5 papers, 4 papers with code
Time Interpret: a Unified Model Interpretability Library for Time Series
As such, this library implements several feature attribution methods that can be used to explain predictions made by any Pytorch model.
Learning Perturbations to Explain Time Series Predictions
Explaining predictions based on multivariate time series data carries the additional difficulty of handling not only multiple features, but also time dependencies.
Sequential Integrated Gradients: a simple but effective method for explaining language models
In order to keep the meaning of these sentences as close as possible to the original one, we propose Sequential Integrated Gradients (SIG), which computes the importance of each word in a sentence by keeping fixed every other words, only creating interpolations between the baseline and the word of interest.
Neural Temporal Point Processes For Modelling Electronic Health Records
The modelling of Electronic Health Records (EHRs) has the potential to drive more efficient allocation of healthcare resources, enabling early intervention strategies and advancing personalised healthcare.
Neural Language Priors
The choice of sentence encoder architecture reflects assumptions about how a sentence's meaning is composed from its constituent words.
