Patrick van der Smagt

Author Info

Name	Affiliation	Papers
PATRICK VAN DER SMAGT	Institute of Robotics and Mechatronics, German Aerospace Center, DLR Oberpfaffenhofen, PO Box 1116, 82230 Wessling, Germany	42
Collaborators	Citations	PageRank
95	188	24.23
Referers	Referees	References
623	840	327

Search Limit

100840

Publications (42 rows)

Collaborators (95 rows)

Referers (100 rows)

Referees (100 rows)

Title	Citations	PageRank	Year
Constrained Probabilistic Movement Primitives for Robot Trajectory Adaptation	0	0.34	2022
Mind the Gap when Conditioning Amortised Inference in Sequential Latent-Variable Models	0	0.34	2021
Variational State-Space Models for Localisation and Dense 3D Mapping in 6 DoF	0	0.34	2021
Estimating Fingertip Forces, Torques, and Local Curvatures from Fingernail Images	2	0.39	2020
Learning Flat Latent Manifolds with VAEs	0	0.34	2020
Continual Learning with Bayesian Neural Networks for Non-Stationary Data	0	0.34	2020
Learning Hierarchical Priors in VAEs.	0	0.34	2019
Switching Linear Dynamics for Variational Bayes Filtering.	1	0.35	2019
ORC—A Lightweight, Lightning-Fast Middleware	0	0.34	2019
Bayesian Learning of Neural Network Architectures.	0	0.34	2019
Approximate Bayesian inference in spatial environments.	1	0.35	2018
Multi-Source Neural Variational Inference.	0	0.34	2018
Active Learning Based On Data Uncertainty And Model Sensitivity	0	0.34	2018
Connecting Artificial Brains to Robots in a Comprehensive Simulation Framework: The Neurorobotics Platform.	12	0.75	2017
Automatic Differentiation for Tensor Algebras.	1	0.36	2017
Deep Variational Bayes Filters: Unsupervised Learning of State Space Models from Raw Data.	6	0.45	2017
CNN-based Segmentation of Medical Imaging Data.	14	0.79	2017
Hitting the sweet spot: Automatic optimization of energy transfer during tool-held hits.	0	0.34	2017
Key Insights into Hand Biomechanics: Human Grip Stiffness Can Be Decoupled from Force by Cocontraction and Predicted from Electromyography.	3	0.47	2017
Gaussian Process Neurons Learn Stochastic Activation Functions.	0	0.34	2017
ML-based tactile sensor calibration: A universal approach.	1	0.41	2016
Variational Inference for On-line Anomaly Detection in High-Dimensional Time Series.	10	0.64	2016
Unsupervised preprocessing for Tactile Data.	0	0.34	2016
A Differentiable Transition Between Additive and Multiplicative Neurons.	0	0.34	2016
Musculoskeletal Robots: Scalability in Neural Control.	4	0.39	2016
Boost online virtual network embedding: Using neural networks for admission control	5	0.44	2016
Efficient Empowerment.	0	0.34	2015
Robust Detection of Anomalies via Sparse Methods.	2	0.37	2015
Fast Adaptive Weight Noise	1	0.49	2015
A new biarticular joint mechanism to extend stiffness ranges	0	0.34	2014
On Fast Dropout and its Applicability to Recurrent Networks	0	0.34	2014
Estimating finger grip force from an image of the hand using Convolutional Neural Networks and Gaussian processes	1	0.36	2014
On Fast Dropout and its Applicability to Recurrent Networks.	15	2.04	2013
Minimizing data consumption with sequential online feature selection.	7	0.44	2013
Computing Grip Force And Torque From Finger Nail Images Using Gaussian Processes	0	0.34	2013
Continuous robot control using surface electromyography of atrophic muscles	3	0.47	2013
Learning sequence neighbourhood metrics	2	0.45	2012
Sequential feature selection for classification	7	0.67	2011
Surface EMG in advanced hand prosthetics.	84	4.89	2009
Searching a Scalable Approach to Cerebellar Based Control	1	0.43	2002
Benchmarking cerebellar control	3	0.56	2000
Approximation With Neural Networks: Between Local And Global Approximation	2	1.20	1995