
Mind the Gap when Conditioning Amortised Inference in Sequential LatentVariable Models
Amortised inference enables scalable learning of sequential latentvaria...
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Variational StateSpace Models for Localisation and Dense 3D Mapping in 6 DoF
We solve the problem of 6DoF localisation and 3D dense reconstruction i...
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Learning Flat Latent Manifolds with VAEs
Measuring the similarity between data points often requires domain knowl...
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Variational Tracking and Prediction with Generative Disentangled StateSpace Models
We address tracking and prediction of multiple moving objects in visual ...
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Increasing the Generalisaton Capacity of Conditional VAEs
We address the problem of onetomany mappings in supervised learning, w...
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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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Bayesian Learning of Neural Network Architectures
In this paper we propose a Bayesian method for estimating architectural ...
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Fast Approximate Geodesics for Deep Generative Models
The length of the geodesic between two data points along the Riemannian ...
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Approximate Bayesian inference in spatial environments
We propose to learn a stochastic recurrent model to solve the problem of...
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Metrics for Deep Generative Models
Neural samplers such as variational autoencoders (VAEs) or generative ad...
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Unsupervised RealTime Control through Variational Empowerment
We introduce a methodology for efficiently computing a lower bound to em...
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Unsupervised preprocessing for Tactile Data
Tactile information is important for gripping, stable grasp, and inhand...
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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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Theano: A Python framework for fast computation of mathematical expressions
Theano is a Python library that allows to define, optimize, and evaluate...
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Variational Inference for Online Anomaly Detection in HighDimensional Time Series
Approximate variational inference has shown to be a powerful tool for mo...
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Efficient Empowerment
Empowerment quantifies the influence an agent has on its environment. Th...
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Learning Stochastic Recurrent Networks
Leveraging advances in variational inference, we propose to enhance recu...
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Regularizing Recurrent Networks  On Injected Noise and Normbased Methods
Advancements in parallel processing have lead to a surge in multilayer p...
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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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Convolutional Neural Networks learn compact local image descriptors
A standard deep convolutional neural network paired with a suitable loss...
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Unsupervised Feature Learning for lowlevel Local Image Descriptors
Unsupervised feature learning has shown impressive results for a wide ra...
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Learning Sequence Neighbourhood Metrics
Recurrent neural networks (RNNs) in combination with a pooling operator ...
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