15 citations · 77 across the 20 of their papers we have counts for
15 papers · 1 filter
Efficient LSTM Training with Eligibility Traces
Michael Hoyer, Shahram Eivazi, Sebastian Otte
Training recurrent neural networks is predominantly achieved via backpropagation through time (BPTT). However, this algorithm is not an optimal solution from both a biological and…
Generating Sparse Counterfactual Explanations For Multivariate Time Series
Jana Lang, Martin Giese, Winfried Ilg +1
Since neural networks play an increasingly important role in critical sectors, explaining network predictions has become a key research topic. Counterfactual explanations can help…
A Taxonomy of Recurrent Learning Rules
Guillermo Martín-Sánchez, Sander Bohté, Sebastian Otte
Backpropagation through time (BPTT) is the de facto standard for training recurrent neural networks (RNNs), but it is non-causal and non-local. Real-time recurrent learning is a ca…
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…
Early Recognition of Ball Catching Success in Clinical Trials with RNN-Based Predictive Classification
Jana Lang, Martin A. Giese, Matthis Synofzik +2
Motor disturbances can affect the interaction with dynamic objects, such as catching a ball. A classification of clinical catching trials might give insight into the existence of p…
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…
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…
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…
Learning, Planning, and Control in a Monolithic Neural Event Inference Architecture
Martin V. Butz, David Bilkey, Dania Humaidan +2
We introduce REPRISE, a REtrospective and PRospective Inference SchEme, which learns temporal event-predictive models of dynamical systems. REPRISE infers the unobservable contextu…