NeuralHydrology -- Interpreting LSTMs in Hydrology

NeuralHydrology -- Interpreting LSTMs in Hydrology

19 March 2019

Frederik Kratzert

Sepp Hochreiter

Günter Klambauer

Papers citing "NeuralHydrology -- Interpreting LSTMs in Hydrology"

8 / 8 papers shown

Title
Explainable AI for Time Series via Virtual Inspection Layers Johanna Vielhaben Sebastian Lapuschkin G. Montavon Wojciech Samek XAI AI4TS 34 26 0 11 Mar 2023
Disentangled Explanations of Neural Network Predictions by Finding Relevant Subspaces Pattarawat Chormai J. Herrmann Klaus-Robert Muller G. Montavon FAtt 52 18 0 30 Dec 2022
Few-Shot Learning by Dimensionality Reduction in Gradient Space M. Gauch M. Beck Thomas Adler D. Kotsur Stefan Fiel ... Markus Holzleitner Werner Zellinger D. Klotz Sepp Hochreiter Sebastian Lehner 48 9 0 07 Jun 2022
Toward Explainable AI for Regression Models S. Letzgus Patrick Wagner Jonas Lederer Wojciech Samek Klaus-Robert Muller G. Montavon XAI 36 63 0 21 Dec 2021
Capabilities of Deep Learning Models on Learning Physical Relationships: Case of Rainfall-Runoff Modeling with LSTM Kazuki Yokoo K. Ishida A. Ercan T. Tu T. Nagasato M. Kiyama Motoki Amagasaki 18 33 0 15 Jun 2021
Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications Wojciech Samek G. Montavon Sebastian Lapuschkin Christopher J. Anders K. Müller XAI 51 82 0 17 Mar 2020
Enhancing streamflow forecast and extracting insights using long-short term memory networks with data integration at continental scales D. Feng K. Fang Chaopeng Shen AI4TS 28 274 0 18 Dec 2019
Towards Learning Universal, Regional, and Local Hydrological Behaviors via Machine-Learning Applied to Large-Sample Datasets Frederik Kratzert D. Klotz Guy Shalev Günter Klambauer Sepp Hochreiter G. Nearing 22 547 0 19 Jul 2019