PMLR

It is known that sparsity can improve interpretability for deep neural networks. However, existing methods in the area either require networks that are pre-trained with sparsity constraints, or impose sparsity after the fact, altering the network’s general behavior. In this paper, we demonstrate, for the first time, that sparsity can instead be incorporated into the interpretation process itself, as a sample-specific preprocessing step. Unlike previous work, this approach, which we call SPADE, does not place constraints on the trained model and does not affect its behavior during inference on the sample. Given a trained model and a target sample, SPADE uses sample-targeted pruning to provide a "trace" of the network’s execution on the sample, reducing the network to the most important connections prior to computing an interpretation. We demonstrate that preprocessing with SPADE significantly increases the accuracy of image saliency maps across several interpretability methods. Additionally, SPADE improves the usefulness of neuron visualizations, aiding humans in reasoning about network behavior. Our code is available at https://github.com/IST-DASLab/SPADE

PMLR

It is known that sparsity can improve interpretability for deep neural networks. However, existing methods in the area either require networks that are pre-trained with sparsity constraints, or impose sparsity after the fact, altering the network’s general behavior. In this paper, we demonstrate, for the first time, that sparsity can instead be incorporated into the interpretation process itself, as a sample-specific preprocessing step. Unlike previous work, this approach, which we call SPADE, does not place constraints on the trained model and does not affect its behavior during inference on the sample. Given a trained model and a target sample, SPADE uses sample-targeted pruning to provide a "trace" of the network’s execution on the sample, reducing the network to the most important connections prior to computing an interpretation. We demonstrate that preprocessing with SPADE significantly increases the accuracy of image saliency maps across several interpretability methods. Additionally, SPADE improves the usefulness of neuron visualizations, aiding humans in reasoning about network behavior. Our code is available at https://github.com/IST-DASLab/SPADE

Model-based assessment of groundwater recharge in Slovenia

The implementation process of the EU water legislation (EU WFD, EU GWD) has put pressure on environmental managers to create, analyse and disseminate hydrological data in recent years. In this context, distributed hydrological model results at the macro scale (>10,000 km2) have gained importance for the Environment Agency of the Republic of Slovenia, too. Within a joint project the distributed water balance model GROWA, developed for Germany, has been adapted to Slovenia by re-calibrating the routine for determining the average annual groundwater recharge rate. This routine consists mainly of a base flow index approach (BFI). This BFI is based on 41 different site conditions in Slovenia, whereas lithology dominates the recharge process. This paper outlines the general GROWA approach, the required input data, and the calibration process. Validated model results for the period 1971–2000, especially total runoff and base flow, are presented and discussed. These results have been used already for practical water management issues in Slovenia on European, national and regional level. It is shown that Slovenian groundwater resources exhibit high regional and seasonal variability. Tendencies of more frequent and more pronounced droughts have been detected. As demonstrated by the results GROWA is a valuable tool for the spatially distributed assessment of groundwater recharge in Slovenia