530 citations · 565 across the 10 of their papers we have counts for
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Unreliable Uncertainty Estimates with Monte Carlo Dropout
Aslak Djupskås, Alexander Johannes Stasik, Signe Riemer-Sørensen
Reliable uncertainty estimation is crucial for machine learning models, especially in safety-critical domains. While exact Bayesian inference offers a principled approach, it is of…
DON-LSTM: Multi-Resolution Learning with DeepONets and Long Short-Term Memory Neural Networks
Katarzyna Michałowska, Somdatta Goswami, George Em Karniadakis +1
Deep operator networks (DeepONets, DONs) offer a distinct advantage over traditional neural networks in their ability to be trained on multi-resolution data. This property becomes…
Neural Operator Learning for Long-Time Integration in Dynamical Systems with Recurrent Neural Networks
Katarzyna Michałowska, Somdatta Goswami, George Em Karniadakis +1
Deep neural networks are an attractive alternative for simulating complex dynamical systems, as in comparison to traditional scientific computing methods, they offer reduced comput…
Deep Reinforcement Learning for Long Term Hydropower Production Scheduling
Signe Riemer-Sorensen, Gjert H. Rosenlund
We explore the use of deep reinforcement learning to provide strategies for long term scheduling of hydropower production. We consider a use-case where the aim is to optimise the y…