Search Results for author: Daniel Klotz

Found 10 papers, 6 papers with code

AI Increases Global Access to Reliable Flood Forecasts

1 code implementation30 Jul 2023 Grey Nearing, Deborah Cohen, Vusumuzi Dube, Martin Gauch, Oren Gilon, Shaun Harrigan, Avinatan Hassidim, Daniel Klotz, Frederik Kratzert, Asher Metzger, Sella Nevo, Florian Pappenberger, Christel Prudhomme, Guy Shalev, Shlomo Shenzis, Tadele Tekalign, Dana Weitzner, Yoss Matias

Using AI, we achieve reliability in predicting extreme riverine events in ungauged watersheds at up to a 5-day lead time that is similar to or better than the reliability of nowcasts (0-day lead time) from a current state of the art global modeling system (the Copernicus Emergency Management Service Global Flood Awareness System).

Management

Conformal Prediction for Time Series with Modern Hopfield Networks

1 code implementation NeurIPS 2023 Andreas Auer, Martin Gauch, Daniel Klotz, Sepp Hochreiter

To quantify uncertainty, conformal prediction methods are gaining continuously more interest and have already been successfully applied to various domains.

Conformal Prediction Time Series

Few-Shot Learning by Dimensionality Reduction in Gradient Space

1 code implementation7 Jun 2022 Martin Gauch, Maximilian Beck, Thomas Adler, Dmytro Kotsur, Stefan Fiel, Hamid Eghbal-zadeh, Johannes Brandstetter, Johannes Kofler, Markus Holzleitner, Werner Zellinger, Daniel Klotz, Sepp Hochreiter, Sebastian Lehner

We introduce SubGD, a novel few-shot learning method which is based on the recent finding that stochastic gradient descent updates tend to live in a low-dimensional parameter subspace.

Dimensionality Reduction Few-Shot Learning

MC-LSTM: Mass-Conserving LSTM

1 code implementation13 Jan 2021 Pieter-Jan Hoedt, Frederik Kratzert, Daniel Klotz, Christina Halmich, Markus Holzleitner, Grey Nearing, Sepp Hochreiter, Günter Klambauer

MC-LSTMs set a new state-of-the-art for neural arithmetic units at learning arithmetic operations, such as addition tasks, which have a strong conservation law, as the sum is constant over time.

Inductive Bias

Uncertainty Estimation with Deep Learning for Rainfall-Runoff Modelling

no code implementations15 Dec 2020 Daniel Klotz, Frederik Kratzert, Martin Gauch, Alden Keefe Sampson, Günter Klambauer, Sepp Hochreiter, Grey Nearing

Deep Learning is becoming an increasingly important way to produce accurate hydrological predictions across a wide range of spatial and temporal scales.

Benchmarking

Rainfall-Runoff Prediction at Multiple Timescales with a Single Long Short-Term Memory Network

1 code implementation15 Oct 2020 Martin Gauch, Frederik Kratzert, Daniel Klotz, Grey Nearing, Jimmy Lin, Sepp Hochreiter

Compared to naive prediction with a distinct LSTM per timescale, the multi-timescale architectures are computationally more efficient with no loss in accuracy.

Accurate Hydrologic Modeling Using Less Information

no code implementations21 Nov 2019 Guy Shalev, Ran El-Yaniv, Daniel Klotz, Frederik Kratzert, Asher Metzger, Sella Nevo

Joint models are a common and important tool in the intersection of machine learning and the physical sciences, particularly in contexts where real-world measurements are scarce.

Towards Learning Universal, Regional, and Local Hydrological Behaviors via Machine-Learning Applied to Large-Sample Datasets

1 code implementation19 Jul 2019 Frederik Kratzert, Daniel Klotz, Guy Shalev, Günter Klambauer, Sepp Hochreiter, Grey Nearing

The problem currently is that traditional hydrological models degrade significantly in performance when calibrated for multiple basins together instead of for a single basin alone.

Benchmarking BIG-bench Machine Learning +2

NeuralHydrology -- Interpreting LSTMs in Hydrology

no code implementations19 Mar 2019 Frederik Kratzert, Mathew Herrnegger, Daniel Klotz, Sepp Hochreiter, Günter Klambauer

LSTMs are particularly well-suited for this problem since memory cells can represent dynamic reservoirs and storages, which are essential components in state-space modelling approaches of the hydrological system.

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