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Found 6 papers, 0 papers with code
DANLIP: Deep Autoregressive Networks for Locally Interpretable Probabilistic Forecasting
Despite the high performance of neural network-based time series forecasting methods, the inherent challenge in explaining their predictions has limited their applicability in certain application areas.
Interpretable Time Series Clustering Using Local Explanations
The explanations are used to obtain insights into the clustering models.
Text Classification for Predicting Multi-level Product Categories
In our experiments, we investigate the generalizability of the trained models to the products of other online retailers, the dynamic masking of infeasible subcategories for pretrained language models, and the benefits of incorporating product titles in multiple languages.
Word-level Text Highlighting of Medical Texts for Telehealth Services
The first method uses TF-IDF scores directly to highlight important parts of the text.
Evaluation of Local Explanation Methods for Multivariate Time Series Forecasting
In this study, we propose two novel evaluation metrics for time series forecasting: Area Over the Perturbation Curve for Regression and Ablation Percentage Threshold.
BIG-bench Machine Learning
Multivariate Time Series Forecasting
+2
An empirical study on using CNNs for fast radio signal prediction
We compare deep learning-based prediction models including RadioUNET and four different variations of the UNET model for the power prediction task.
