Sebastian Otte

Orcid: 0000-0002-0305-0463

Affiliations:
  • Eberhard Karls University of Tübingen, Department of Computer Science, Germany (PhD 2017)


According to our database1, Sebastian Otte authored at least 76 papers between 2011 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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PhD thesis 
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Online presence:

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Bibliography

2024
WARP-LCA: Efficient Convolutional Sparse Coding with Locally Competitive Algorithm.
CoRR, 2024

Inferring Underwater Topography with FINN.
CoRR, 2024

Understanding the Convergence in Balanced Resonate-and-Fire Neurons.
CoRR, 2024

Flexible and Efficient Surrogate Gradient Modeling with Forward Gradient Injection.
CoRR, 2024

Representation Learning of Multivariate Time Series using Attention and Adversarial Training.
CoRR, 2024

Balanced Resonate-and-Fire Neurons.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

Resonator-Gated RNNs.
Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2024, 2024

3D Lattice Deformation Prediction with Hierarchical Graph Attention Networks.
Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2024, 2024

2023
Physical Domain Reconstruction with Finite Volume Neural Networks.
Appl. Artif. Intell., December, 2023

The deep arbitrary polynomial chaos neural network or how Deep Artificial Neural Networks could benefit from data-driven homogeneous chaos theory.
Neural Networks, September, 2023

Extending the Omniglot Challenge: Imitating Handwriting Styles on a New Sequential Data Set.
IEEE Trans. Cogn. Dev. Syst., 2023

Loci-Segmented: Improving Scene Segmentation Learning.
CoRR, 2023

Looping LOCI: Developing Object Permanence from Videos.
CoRR, 2023

Inductive biases in deep learning models for weather prediction.
CoRR, 2023

Learning What and Where: Disentangling Location and Identity Tracking Without Supervision.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

Generating Sparse Counterfactual Explanations for Multivariate Time Series.
Proceedings of the Artificial Neural Networks and Machine Learning, 2023

2022
Replication Data for: Learning Groundwater Contaminant Diffusion-Sorption Processes with a Finite Volume Neural Network.
Dataset, November, 2022


Inference of affordances and active motor control in simulated agents.
Frontiers Neurorobotics, September, 2022

Inference of time series components by online co-evolution.
Genet. Program. Evolvable Mach., 2022

Learning What and Where - Unsupervised Disentangling Location and Identity Tracking.
CoRR, 2022

Composing Partial Differential Equations with Physics-Aware Neural Networks.
Proceedings of the International Conference on Machine Learning, 2022

Binding Dancers Into Attractors.
Proceedings of the IEEE International Conference on Development and Learning, 2022

A Taxonomy of Recurrent Learning Rules.
Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2022, 2022

Efficient LSTM Training with Eligibility Traces.
Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2022, 2022

Infering Boundary Conditions in Finite Volume Neural Networks.
Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2022, 2022

2021
Finite Volume Neural Network: Modeling Subsurface Contaminant Transport.
CoRR, 2021

Many-Joint Robot Arm Control with Recurrent Spiking Neural Networks.
Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2021

Gestalt Perception of Biological Motion: A Generative Artificial Neural Network Model.
Proceedings of the IEEE International Conference on Development and Learning, 2021

Dynamic Action Inference with Recurrent Spiking Neural Networks.
Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2021, 2021

Binding and Perspective Taking as Inference in a Generative Neural Network Model.
Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2021, 2021

Early Recognition of Ball Catching Success in Clinical Trials with RNN-Based Predictive Classification.
Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2021, 2021

Latent State Inference in a Spatiotemporal Generative Model.
Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2021, 2021

Fostering Compositionality in Latent, Generative Encodings to Solve the Omniglot Challenge.
Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2021, 2021

Signal Denoising with Recurrent Spiking Neural Networks and Active Tuning.
Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2021, 2021

Latent Event-Predictive Encodings through Counterfactual Regularization.
Proceedings of the 43rd Annual Meeting of the Cognitive Science Society, 2021

Demand Forecasting Using Ensemble Learning for Effective Scheduling of Logistic Orders.
Proceedings of the Advances in Artificial Intelligence, Software and Systems Engineering, 2021

2020
Active Tuning.
CoRR, 2020

Hidden Latent State Inference in a Spatio-Temporal Generative Model.
CoRR, 2020

Learning Precise Spike Timings with Eligibility Traces.
Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2020, 2020

Inferring, Predicting, and Denoising Causal Wave Dynamics.
Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2020, 2020

Fostering Event Compression Using Gated Surprise.
Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2020, 2020

Investigating Efficient Learning and Compositionality in Generative LSTM Networks.
Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2020, 2020

A Distributed Neural Network Architecture for Robust Non-Linear Spatio-Temporal Prediction.
Proceedings of the 28th European Symposium on Artificial Neural Networks, 2020

Sequence Classification using Ensembles of Recurrent Generative Expert Modules.
Proceedings of the 28th European Symposium on Artificial Neural Networks, 2020

2019
Learning, planning, and control in a monolithic neural event inference architecture.
Neural Networks, 2019

Incorporating Adaptive RNN-Based Action Inference and Sensory Perception.
Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2019: Text and Time Series, 2019

Gradient-Based Learning of Compositional Dynamics with Modular RNNs.
Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2019: Theoretical Neural Computation, 2019

Inferring Event-Predictive Goal-Directed Object Manipulations in REPRISE.
Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2019: Theoretical Neural Computation, 2019

2018
Robust Real-Time 3D Person Detection for Indoor and Outdoor Applications.
Proceedings of the 2018 IEEE International Conference on Robotics and Automation, 2018

Integrative Collision Avoidance Within RNN-Driven Many-Joint Robot Arms.
Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2018, 2018

Online Carry Mode Detection for Mobile Devices with Compact RNNs.
Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2018, 2018

Configuration Depending Crosstalk Torque Calibration for Robotic Manipulators with Deep Neural Regression Models.
Proceedings of the Intelligent Autonomous Systems 15, 2018

REPRISE: A Retrospective and Prospective Inference Scheme.
Proceedings of the 40th Annual Meeting of the Cognitive Science Society, 2018

2017
Recurrent Neural Networks for Sequential Pattern Recognition Applications.
PhD thesis, 2017

Inherently Constraint-Aware Control of Many-Joint Robot Arms with Inverse Recurrent Models.
Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2017, 2017

Inferring Adaptive Goal-Directed Behavior Within Recurrent Neural Networks.
Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2017, 2017

Anticipatory Active Inference from Learned Recurrent Neural Forward Models.
Proceedings of the 39th Annual Meeting of the Cognitive Science Society, 2017

A Computational Model for the Dynamical Learning of Event Taxonomies.
Proceedings of the 39th Annual Meeting of the Cognitive Science Society, 2017

2016
Optimizing recurrent reservoirs with neuro-evolution.
Neurocomputing, 2016

Recurrent Neural Networks for fast and robust vibration-based ground classification on mobile robots.
Proceedings of the 2016 IEEE International Conference on Robotics and Automation, 2016

Inverse Recurrent Models - An Application Scenario for Many-Joint Robot Arm Control.
Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2016, 2016

Revisiting Deep Convolutional Neural Networks for RGB-D Based Object Recognition.
Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2016, 2016

Investigating Recurrent Neural Networks for Feature-Less Computational Drug Design.
Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2016, 2016

Vector-AMCL: Vector Based Adaptive Monte Carlo Localization for Indoor Maps.
Proceedings of the Intelligent Autonomous Systems 14, 2016

2015
An analysis of Dynamic Cortex Memory networks.
Proceedings of the 2015 International Joint Conference on Neural Networks, 2015

Robust Visual Terrain Classification with Recurrent Neural Networks.
Proceedings of the 23rd European Symposium on Artificial Neural Networks, 2015

Learning Recurrent Dynamics using Differential Evolution.
Proceedings of the 23rd European Symposium on Artificial Neural Networks, 2015

2014
Investigating Long Short-Term Memory Networks for Various Pattern Recognition Problems.
Proceedings of the Machine Learning and Data Mining in Pattern Recognition, 2014

ANTSAC: A Generic RANSAC Variant Using Principles of Ant Colony Algorithms.
Proceedings of the 22nd International Conference on Pattern Recognition, 2014

Dynamic Cortex Memory: Enhancing Recurrent Neural Networks for Gradient-Based Sequence Learning.
Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2014, 2014

2013
OCT A-Scan based lung tumor tissue classification with Bidirectional Long Short Term Memory networks.
Proceedings of the IEEE International Workshop on Machine Learning for Signal Processing, 2013

Investigating Long Short-Term Memory Networks for various Pattern Recognition Prolems.
Proceedings of the Machine Learning and Data Mining in Pattern Recognition, 2013

JANNLab Neural Network Framework for Java.
Proceedings of the Machine Learning and Data Mining in Pattern Recognition, 2013

2012
Local Feature Based Online Mode Detection with Recurrent Neural Networks.
Proceedings of the 2012 International Conference on Frontiers in Handwriting Recognition, 2012

2011
Distributed Evolutionary Optimization of Neural Network Topologies.
Proceedings of the Machine Learning and Data Mining in Pattern Recognition, 2011


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