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Found 3 papers, 2 papers with code
Improving Deep Learning Interpretability by Saliency Guided Training
In this paper, we tackle this issue and introduce a {\it saliency guided training}procedure for neural networks to reduce noisy gradients used in predictions while retaining the predictive performance of the model.
Benchmarking Deep Learning Interpretability in Time Series Predictions
Saliency methods are used extensively to highlight the importance of input features in model predictions.
Improving Long-Horizon Forecasts with Expectation-Biased LSTM Networks
State-of-the-art forecasting methods using Recurrent Neural Net- works (RNN) based on Long-Short Term Memory (LSTM) cells have shown exceptional performance targeting short-horizon forecasts, e. g given a set of predictor features, forecast a target value for the next few time steps in the future.
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