Patrick van der Smagt

research

∙ 04/20/2023

Filter-Aware Model-Predictive Control

Partially-observable problems pose a trade-off between reducing costs an...

0 Baris Kayalibay, et al. ∙

research

∙ 12/06/2022

PRISM: Probabilistic Real-Time Inference in Spatial World Models

We introduce PRISM, a method for real-time filtering in a probabilistic ...

0 Atanas Mirchev, et al. ∙

research

∙ 11/28/2022

CLAS: Coordinating Multi-Robot Manipulation with Central Latent Action Spaces

Multi-robot manipulation tasks involve various control entities that can...

0 Elie Aljalbout, et al. ∙

research

∙ 09/20/2022

Probabilistic Dalek – Emulator framework with probabilistic prediction for supernova tomography

Supernova spectral time series can be used to reconstruct a spatially re...

0 Wolfgang Kerzendorf, et al. ∙

research

∙ 06/13/2022

Local distance preserving auto-encoders using Continuous k-Nearest Neighbours graphs

Auto-encoder models that preserve similarities in the data are a popular...

0 Nutan Chen, et al. ∙

research

∙ 02/23/2022

Flat latent manifolds for music improvisation between human and machine

The use of machine learning in artistic music generation leads to contro...

0 Nutan Chen, et al. ∙

research

∙ 01/25/2022

Tracking and Planning with Spatial World Models

We introduce a method for real-time navigation and tracking with differe...

4 Baris Kayalibay, et al. ∙

research

∙ 01/29/2021

Constrained Probabilistic Movement Primitives for Robot Trajectory Adaptation

Versatile movement representations allow robots to learn new tasks and r...

0 Felix Frank, et al. ∙

research

∙ 01/18/2021

Mind the Gap when Conditioning Amortised Inference in Sequential Latent-Variable Models

Amortised inference enables scalable learning of sequential latent-varia...

0 Justin Bayer, et al. ∙

research

∙ 07/03/2020

Dalek – a deep-learning emulator for TARDIS

Supernova spectral time series contain a wealth of information about the...

0 Wolfgang E. Kerzendorf, et al. ∙

research

∙ 06/26/2020

Layerwise learning for quantum neural networks

With the increased focus on quantum circuit learning for near-term appli...

0 Andrea Skolik, et al. ∙

research

∙ 06/17/2020

Variational State-Space Models for Localisation and Dense 3D Mapping in 6 DoF

We solve the problem of 6-DoF localisation and 3D dense reconstruction i...

8 Atanas Mirchev, et al. ∙

research

∙ 03/19/2020

Learning to Fly via Deep Model-Based Reinforcement Learning

Learning to control robots without requiring models has been a long-term...

7 Philip Becker-Ehmck, et al. ∙

research

∙ 02/12/2020

Learning Flat Latent Manifolds with VAEs

Measuring the similarity between data points often requires domain knowl...

16 Nutan Chen, et al. ∙

research

∙ 11/02/2019

Beta DVBF: Learning State-Space Models for Control from High Dimensional Observations

Learning a model of dynamics from high-dimensional images can be a core ...

8 Neha Das, et al. ∙

research

∙ 10/14/2019

Variational Tracking and Prediction with Generative Disentangled State-Space Models

We address tracking and prediction of multiple moving objects in visual ...

8 Adnan Akhundov, et al. ∙

research

∙ 09/09/2019

Estimating Fingertip Forces, Torques, and Local Curvatures from Fingernail Images

The study of dexterous manipulation has provided important insights in h...

0 Nutan Chen, et al. ∙

research

∙ 08/23/2019

Increasing the Generalisaton Capacity of Conditional VAEs

We address the problem of one-to-many mappings in supervised learning, w...

21 Alexej Klushyn, et al. ∙

research

∙ 05/29/2019

Switching Linear Dynamics for Variational Bayes Filtering

System identification of complex and nonlinear systems is a central prob...

0 Philip Becker-Ehmck, et al. ∙

research

∙ 05/13/2019

Learning Hierarchical Priors in VAEs

We propose to learn a hierarchical prior in the context of variational a...

0 Alexej Klushyn, et al. ∙

research

∙ 03/18/2019

On Deep Set Learning and the Choice of Aggregations

Recently, it has been shown that many functions on sets can be represent...

4 Maximilian Soelch, et al. ∙

research

∙ 01/14/2019

Bayesian Learning of Neural Network Architectures

In this paper we propose a Bayesian method for estimating architectural ...

32 Georgi Dikov, et al. ∙

research

∙ 12/19/2018

Fast Approximate Geodesics for Deep Generative Models

The length of the geodesic between two data points along the Riemannian ...

10 Nutan Chen, et al. ∙

research

∙ 11/11/2018

Multi-Source Neural Variational Inference

Learning from multiple sources of information is an important problem in...

6 Richard Kurle, et al. ∙

research

∙ 08/06/2018

Active Learning based on Data Uncertainty and Model Sensitivity

Robots can rapidly acquire new skills from demonstrations. However, duri...

8 Nutan Chen, et al. ∙

research

∙ 05/18/2018

Approximate Bayesian inference in spatial environments

We propose to learn a stochastic recurrent model to solve the problem of...

0 Atanas Mirchev, et al. ∙

research

∙ 11/29/2017

Gaussian Process Neurons Learn Stochastic Activation Functions

We propose stochastic, non-parametric activation functions that are full...

0 Sebastian Urban, et al. ∙

research

∙ 11/03/2017

Automatic Differentiation for Tensor Algebras

Kjolstad et. al. proposed a tensor algebra compiler. It takes expression...

0 Sebastian Urban, et al. ∙

research

∙ 11/03/2017

Metrics for Deep Generative Models

Neural samplers such as variational autoencoders (VAEs) or generative ad...

0 Nutan Chen, et al. ∙

research

∙ 10/13/2017

Unsupervised Real-Time Control through Variational Empowerment

We introduce a methodology for efficiently computing a lower bound to em...

0 Maximilian Karl, et al. ∙

research

∙ 03/22/2017

Two-Stream RNN/CNN for Action Recognition in 3D Videos

The recognition of actions from video sequences has many applications in...

0 Rui Zhao, et al. ∙

research

∙ 01/11/2017

CNN-based Segmentation of Medical Imaging Data

Convolutional neural networks have been applied to a wide variety of com...

0 Baris Kayalibay, et al. ∙

research

∙ 09/26/2016

Variational Inference with Hamiltonian Monte Carlo

Variational inference lies at the core of many state-of-the-art algorith...

0 Christopher Wolf, et al. ∙

research

∙ 06/23/2016

Unsupervised preprocessing for Tactile Data

Tactile information is important for gripping, stable grasp, and in-hand...

0 Maximilian Karl, et al. ∙

research

∙ 05/20/2016

Deep Variational Bayes Filters: Unsupervised Learning of State Space Models from Raw Data

We introduce Deep Variational Bayes Filters (DVBF), a new method for uns...

0 Maximilian Karl, et al. ∙

research

∙ 04/13/2016

A Differentiable Transition Between Additive and Multiplicative Neurons

Existing approaches to combine both additive and multiplicative neural u...

0 Wiebke Köpp, et al. ∙

research

∙ 02/23/2016

Variational Inference for On-line Anomaly Detection in High-Dimensional Time Series

Approximate variational inference has shown to be a powerful tool for mo...

0 Maximilian Soelch, et al. ∙

research

∙ 01/19/2016

Scalability in Neural Control of Musculoskeletal Robots

Anthropomimetic robots are robots that sense, behave, interact and feel ...

0 Christoph Richter, et al. ∙

research

∙ 09/28/2015

Efficient Empowerment

Empowerment quantifies the influence an agent has on its environment. Th...

0 Maximilian Karl, et al. ∙

research

∙ 04/26/2015

FlowNet: Learning Optical Flow with Convolutional Networks

Convolutional neural networks (CNNs) have recently been very successful ...

0 Philipp Fischer, et al. ∙

research

∙ 03/19/2015

A Neural Transfer Function for a Smooth and Differentiable Transition Between Additive and Multiplicative Interactions

Existing approaches to combine both additive and multiplicative neural u...

0 Sebastian Urban, et al. ∙

research

∙ 11/04/2013

On Fast Dropout and its Applicability to Recurrent Networks

Recurrent Neural Networks (RNNs) are rich models for the processing of s...

0 Justin Bayer, et al. ∙

research

∙ 04/30/2013

Convolutional Neural Networks learn compact local image descriptors

A standard deep convolutional neural network paired with a suitable loss...

0 Christian Osendorfer, et al. ∙

research

∙ 01/14/2013

Unsupervised Feature Learning for low-level Local Image Descriptors

Unsupervised feature learning has shown impressive results for a wide ra...

0 Christian Osendorfer, et al. ∙

research

∙ 09/09/2011

Learning Sequence Neighbourhood Metrics

Recurrent neural networks (RNNs) in combination with a pooling operator ...

0 Justin Bayer, et al. ∙

Patrick van der Smagt

Featured Co-authors