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04/20/2023
Filter-Aware Model-Predictive Control
Partially-observable problems pose a trade-off between reducing costs an...
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12/06/2022
PRISM: Probabilistic Real-Time Inference in Spatial World Models
We introduce PRISM, a method for real-time filtering in a probabilistic ...
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11/28/2022
CLAS: Coordinating Multi-Robot Manipulation with Central Latent Action Spaces
Multi-robot manipulation tasks involve various control entities that can...
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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...
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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...
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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...
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01/25/2022
Tracking and Planning with Spatial World Models
We introduce a method for real-time navigation and tracking with differe...
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01/29/2021
Constrained Probabilistic Movement Primitives for Robot Trajectory Adaptation
Versatile movement representations allow robots to learn new tasks and r...
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01/18/2021
Mind the Gap when Conditioning Amortised Inference in Sequential Latent-Variable Models
Amortised inference enables scalable learning of sequential latent-varia...
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07/03/2020
Dalek – a deep-learning emulator for TARDIS
Supernova spectral time series contain a wealth of information about the...
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06/26/2020
Layerwise learning for quantum neural networks
With the increased focus on quantum circuit learning for near-term appli...
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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...
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03/19/2020
Learning to Fly via Deep Model-Based Reinforcement Learning
Learning to control robots without requiring models has been a long-term...
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02/12/2020
Learning Flat Latent Manifolds with VAEs
Measuring the similarity between data points often requires domain knowl...
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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 ...
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10/14/2019
Variational Tracking and Prediction with Generative Disentangled State-Space Models
We address tracking and prediction of multiple moving objects in visual ...
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09/09/2019
Estimating Fingertip Forces, Torques, and Local Curvatures from Fingernail Images
The study of dexterous manipulation has provided important insights in h...
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08/23/2019
Increasing the Generalisaton Capacity of Conditional VAEs
We address the problem of one-to-many mappings in supervised learning, w...
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05/29/2019
Switching Linear Dynamics for Variational Bayes Filtering
System identification of complex and nonlinear systems is a central prob...
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05/13/2019
Learning Hierarchical Priors in VAEs
We propose to learn a hierarchical prior in the context of variational a...
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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...
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01/14/2019
Bayesian Learning of Neural Network Architectures
In this paper we propose a Bayesian method for estimating architectural ...
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12/19/2018
Fast Approximate Geodesics for Deep Generative Models
The length of the geodesic between two data points along the Riemannian ...
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11/11/2018
Multi-Source Neural Variational Inference
Learning from multiple sources of information is an important problem in...
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08/06/2018
Active Learning based on Data Uncertainty and Model Sensitivity
Robots can rapidly acquire new skills from demonstrations. However, duri...
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05/18/2018
Approximate Bayesian inference in spatial environments
We propose to learn a stochastic recurrent model to solve the problem of...
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11/29/2017
Gaussian Process Neurons Learn Stochastic Activation Functions
We propose stochastic, non-parametric activation functions that are full...
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11/03/2017
Automatic Differentiation for Tensor Algebras
Kjolstad et. al. proposed a tensor algebra compiler. It takes expression...
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11/03/2017
Metrics for Deep Generative Models
Neural samplers such as variational autoencoders (VAEs) or generative ad...
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10/13/2017
Unsupervised Real-Time Control through Variational Empowerment
We introduce a methodology for efficiently computing a lower bound to em...
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03/22/2017
Two-Stream RNN/CNN for Action Recognition in 3D Videos
The recognition of actions from video sequences has many applications in...
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01/11/2017
CNN-based Segmentation of Medical Imaging Data
Convolutional neural networks have been applied to a wide variety of com...
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09/26/2016
Variational Inference with Hamiltonian Monte Carlo
Variational inference lies at the core of many state-of-the-art algorith...
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06/23/2016
Unsupervised preprocessing for Tactile Data
Tactile information is important for gripping, stable grasp, and in-hand...
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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...
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04/13/2016
A Differentiable Transition Between Additive and Multiplicative Neurons
Existing approaches to combine both additive and multiplicative neural u...
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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...
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01/19/2016
Scalability in Neural Control of Musculoskeletal Robots
Anthropomimetic robots are robots that sense, behave, interact and feel ...
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09/28/2015
Efficient Empowerment
Empowerment quantifies the influence an agent has on its environment. Th...
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04/26/2015
FlowNet: Learning Optical Flow with Convolutional Networks
Convolutional neural networks (CNNs) have recently been very successful ...
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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...
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11/04/2013
On Fast Dropout and its Applicability to Recurrent Networks
Recurrent Neural Networks (RNNs) are rich models for the processing of s...
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04/30/2013
Convolutional Neural Networks learn compact local image descriptors
A standard deep convolutional neural network paired with a suitable loss...
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01/14/2013
Unsupervised Feature Learning for low-level Local Image Descriptors
Unsupervised feature learning has shown impressive results for a wide ra...
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09/09/2011