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Uncertainty Estimation with Deep Learning for Rainfall-Runoff Modelling
Daniel Klotz, Frederik Kratzert, Martin Gauch +4
Deep Learning is becoming an increasingly important way to produce accurate hydrological predictions across a wide range of spatial and temporal scales. Uncertainty estimations are…
Convergence Proof for Actor-Critic Methods Applied to PPO and RUDDER
Markus Holzleitner, Lukas Gruber, José Arjona-Medina +2
We prove under commonly used assumptions the convergence of actor-critic reinforcement learning algorithms, which simultaneously learn a policy function, the actor, and a value fun…
Rainfall-Runoff Prediction at Multiple Timescales with a Single Long Short-Term Memory Network
Martin Gauch, Frederik Kratzert, Daniel Klotz +3
Long Short-Term Memory Networks (LSTMs) have been applied to daily discharge prediction with remarkable success. Many practical scenarios, however, require predictions at more gran…
Cross-Domain Few-Shot Learning by Representation Fusion
Thomas Adler, Johannes Brandstetter, Michael Widrich +5
In order to quickly adapt to new data, few-shot learning aims at learning from few examples, often by using already acquired knowledge. The new data often differs from the previous…
Align-RUDDER: Learning From Few Demonstrations by Reward Redistribution
Vihang P. Patil, Markus Hofmarcher, Marius-Constantin Dinu +5
Reinforcement learning algorithms require many samples when solving complex hierarchical tasks with sparse and delayed rewards. For such complex tasks, the recently proposed RUDDER…
A physical protocol for observers near the boundary to obtain bulk information in quantum gravity
Chandramouli Chowdhury, Olga Papadoulaki, Suvrat Raju
We consider a set of observers who live near the boundary of global AdS, and are allowed to act only with simple low-energy unitaries and make measurements in a small interval of t…
Modern Hopfield Networks and Attention for Immune Repertoire Classification
Michael Widrich, Bernhard Schäfl, Hubert Ramsauer +8
A central mechanism in machine learning is to identify, store, and recognize patterns. How to learn, access, and retrieve such patterns is crucial in Hopfield networks and the more…
Large-scale ligand-based virtual screening for SARS-CoV-2 inhibitors using deep neural networks
Markus Hofmarcher, Andreas Mayr, Elisabeth Rumetshofer +8
Due to the current severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, there is an urgent need for novel therapies and drugs. We conducted a large-scale virtual…