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Found 6 papers, 3 papers with code
Interpretable Mixture of Experts for Structured Data
We introduce a novel framework, Interpretable Mixture of Experts (IME), that provides interpretability for structured data while preserving accuracy.
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.
Improving Multimodal Accuracy Through Modality Pre-training and Attention
Training a multimodal network is challenging and it requires complex architectures to achieve reasonable performance.
Benchmarking Deep Learning Interpretability in Time Series Predictions
Saliency methods are used extensively to highlight the importance of input features in model predictions.
Input-Cell Attention Reduces Vanishing Saliency of Recurrent Neural Networks
In this work we analyze saliency-based methods for RNNs, both classical and gated cell architectures.
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.
