93 citations · 149 across the 17 of their papers we have counts for
25 papers
Minimal Convolutional RNNs Accelerate Spatiotemporal Learning
Coşku Can Horuz, Sebastian Otte, Martin V. Butz +1
We introduce MinConvLSTM and MinConvGRU, two novel spatiotemporal models that combine the spatial inductive biases of convolutional recurrent networks with the training efficiency…
Advancing Parsimonious Deep Learning Weather Prediction using the HEALPix Mesh
Matthias Karlbauer, Nathaniel Cresswell-Clay, Dale R. Durran +5
We present a parsimonious deep learning weather prediction model to forecast seven atmospheric variables with 3-h time resolution for up to one-year lead times on a 110-km global m…
Loci-Segmented: Improving Scene Segmentation Learning
Manuel Traub, Frederic Becker, Adrian Sauter +2
Current slot-oriented approaches for compositional scene segmentation from images and videos rely on provided background information or slot assignments. We present a segmented loc…
Learning Object Permanence from Videos via Latent Imaginations
Manuel Traub, Frederic Becker, Sebastian Otte +1
While human infants exhibit knowledge about object permanence from two months of age onwards, deep-learning approaches still largely fail to recognize objects' continued existence.…
Inductive biases in deep learning models for weather prediction
Jannik Thuemmel, Matthias Karlbauer, Sebastian Otte +8
Deep learning has gained immense popularity in the Earth sciences as it enables us to formulate purely data-driven models of complex Earth system processes. Deep learning-based wea…
Intelligent problem-solving as integrated hierarchical reinforcement learning
Manfred Eppe, Christian Gumbsch, Matthias Kerzel +3
According to cognitive psychology and related disciplines, the development of complex problem-solving behaviour in biological agents depends on hierarchical cognitive mechanisms. H…