93 citations · 150 across the 20 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…
Binding Dancers Into Attractors
Franziska Kaltenberger, Sebastian Otte, Martin V. Butz
To effectively perceive and process observations in our environment, feature binding and perspective taking are crucial cognitive abilities. Feature binding combines observed featu…
Developing hierarchical anticipations via neural network-based event segmentation
Christian Gumbsch, Maurits Adam, Birgit Elsner +2
Humans can make predictions on various time scales and hierarchical levels. Thereby, the learning of event encodings seems to play a crucial role. In this work we model the develop…
Learning What and Where: Disentangling Location and Identity Tracking Without Supervision
Manuel Traub, Sebastian Otte, Tobias Menge +3
Our brain can almost effortlessly decompose visual data streams into background and salient objects. Moreover, it can anticipate object motion and interactions, which are crucial a…
Inference of Affordances and Active Motor Control in Simulated Agents
Fedor Scholz, Christian Gumbsch, Sebastian Otte +1
Flexible, goal-directed behavior is a fundamental aspect of human life. Based on the free energy minimization principle, the theory of active inference formalizes the generation of…
Composing Partial Differential Equations with Physics-Aware Neural Networks
Matthias Karlbauer, Timothy Praditia, Sebastian Otte +3
We introduce a compositional physics-aware FInite volume Neural Network (FINN) for learning spatiotemporal advection-diffusion processes. FINN implements a new way of combining the…
Sparsely Changing Latent States for Prediction and Planning in Partially Observable Domains
Christian Gumbsch, Martin V. Butz, Georg Martius
A common approach to prediction and planning in partially observable domains is to use recurrent neural networks (RNNs), which ideally develop and maintain a latent memory about hi…
Latent Event-Predictive Encodings through Counterfactual Regularization
Dania Humaidan, Sebastian Otte, Christian Gumbsch +2
A critical challenge for any intelligent system is to infer structure from continuous data streams. Theories of event-predictive cognition suggest that the brain segments sensorimo…
Finite Volume Neural Network: Modeling Subsurface Contaminant Transport
Timothy Praditia, Matthias Karlbauer, Sebastian Otte +3
Data-driven modeling of spatiotemporal physical processes with general deep learning methods is a highly challenging task. It is further exacerbated by the limited availability of…
Binding and Perspective Taking as Inference in a Generative Neural Network Model
Mahdi Sadeghi, Fabian Schrodt, Sebastian Otte +1
The ability to flexibly bind features into coherent wholes from different perspectives is a hallmark of cognition and intelligence. Importantly, the binding problem is not only rel…
Hierarchical principles of embodied reinforcement learning: A review
Manfred Eppe, Christian Gumbsch, Matthias Kerzel +3
Cognitive Psychology and related disciplines have identified several critical mechanisms that enable intelligent biological agents to learn to solve complex problems. There exists…
Active Tuning
Sebastian Otte, Matthias Karlbauer, Martin V. Butz
We introduce Active Tuning, a novel paradigm for optimizing the internal dynamics of recurrent neural networks (RNNs) on the fly. In contrast to the conventional sequence-to-sequen…
Inferring, Predicting, and Denoising Causal Wave Dynamics
Matthias Karlbauer, Sebastian Otte, Hendrik P. A. Lensch +3
The novel DISTributed Artificial neural Network Architecture (DISTANA) is a generative, recurrent graph convolution neural network. It implements a grid or mesh of locally paramete…
Latent State Inference in a Spatiotemporal Generative Model
Matthias Karlbauer, Tobias Menge, Sebastian Otte +4
Knowledge about the hidden factors that determine particular system dynamics is crucial for both explaining them and pursuing goal-directed interventions. Inferring these factors f…
Fostering Event Compression using Gated Surprise
Dania Humaidan, Sebastian Otte, Martin V. Butz
Our brain receives a dynamically changing stream of sensorimotor data. Yet, we perceive a rather organized world, which we segment into and perceive as events. Computational theori…
Learning Precise Spike Timings with Eligibility Traces
Manuel Traub, Martin V. Butz, R. Harald Baayen +1
Recent research in the field of spiking neural networks (SNNs) has shown that recurrent variants of SNNs, namely long short-term SNNs (LSNNs), can be trained via error gradients ju…
Investigating Efficient Learning and Compositionality in Generative LSTM Networks
Sarah Fabi, Sebastian Otte, Jonas Gregor Wiese +1
When comparing human with artificial intelligence, one major difference is apparent: Humans can generalize very broadly from sparse data sets because they are able to recombine and…
A Distributed Neural Network Architecture for Robust Non-Linear Spatio-Temporal Prediction
Matthias Karlbauer, Sebastian Otte, Hendrik P. A. Lensch +3
We introduce a distributed spatio-temporal artificial neural network architecture (DISTANA). It encodes mesh nodes using recurrent, neural prediction kernels (PKs), while neural tr…
Autonomous Identification and Goal-Directed Invocation of Event-Predictive Behavioral Primitives
Christian Gumbsch, Martin V. Butz, Georg Martius
Voluntary behavior of humans appears to be composed of small, elementary building blocks or behavioral primitives. While this modular organization seems crucial for the learning of…