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Found 30 papers, 5 papers with code
Filter-Aware Model-Predictive Control
Partially-observable problems pose a trade-off between reducing costs and gathering information.
PRISM: Probabilistic Real-Time Inference in Spatial World Models
We introduce PRISM, a method for real-time filtering in a probabilistic generative model of agent motion and visual perception.
Tracking and Planning with Spatial World Models
We introduce a method for real-time navigation and tracking with differentiably rendered world models.
Less Suboptimal Learning and Control in Variational POMDPs
We examine the effect of the conditioning gap on model-based reinforcement learning with variational world models.
Model-based Reinforcement Learning
reinforcement-learning
+2
Mind the Gap when Conditioning Amortised Inference in Sequential Latent-Variable Models
Amortised inference enables scalable learning of sequential latent-variable models (LVMs) with the evidence lower bound (ELBO).
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 in spatial environments as approximate Bayesian inference in a deep state-space model.
Learning Flat Latent Manifolds with VAEs
Prevalent is the use of the Euclidean metric, which has the drawback of ignoring information about similarity of data stored in the decoder, as captured by the framework of Riemannian geometry.
Variational Tracking and Prediction with Generative Disentangled State-Space Models
We address tracking and prediction of multiple moving objects in visual data streams as inference and sampling in a disentangled latent state-space model.
FLAT MANIFOLD VAES
Latent-variable models represent observed data by mapping a prior distribution over some latent space to an observed space.
Increasing the Generalisation Capacity of Conditional VAEs
We address the problem of one-to-many mappings in supervised learning, where a single instance has many different solutions of possibly equal cost.
On Deep Set Learning and the Choice of Aggregations
Recently, it has been shown that many functions on sets can be represented by sum decompositions.
Bayesian Learning of Neural Network Architectures
In this paper we propose a Bayesian method for estimating architectural parameters of neural networks, namely layer size and network depth.
Fast Approximate Geodesics for Deep Generative Models
The length of the geodesic between two data points along a Riemannian manifold, induced by a deep generative model, yields a principled measure of similarity.
Approximate Bayesian inference in spatial environments
Model-based approaches bear great promise for decision making of agents interacting with the physical world.
Metrics for Deep Generative Models
Neural samplers such as variational autoencoders (VAEs) or generative adversarial networks (GANs) approximate distributions by transforming samples from a simple random source---the latent space---to samples from a more complex distribution represented by a dataset.
Unsupervised Real-Time Control through Variational Empowerment
We introduce a methodology for efficiently computing a lower bound to empowerment, allowing it to be used as an unsupervised cost function for policy learning in real-time control.
Unsupervised preprocessing for Tactile Data
Tactile information is important for gripping, stable grasp, and in-hand manipulation, yet the complexity of tactile data prevents widespread use of such sensors.
ML-based tactile sensor calibration: A universal approach
We study the responses of two tactile sensors, the fingertip sensor from the iCub and the BioTac under different external stimuli.
Deep Variational Bayes Filters: Unsupervised Learning of State Space Models from Raw Data
We introduce Deep Variational Bayes Filters (DVBF), a new method for unsupervised learning and identification of latent Markovian state space models.
Theano: A Python framework for fast computation of mathematical expressions
Since its introduction, it has been one of the most used CPU and GPU mathematical compilers - especially in the machine learning community - and has shown steady performance improvements.
Variational Inference for On-line Anomaly Detection in High-Dimensional Time Series
Approximate variational inference has shown to be a powerful tool for modeling unknown complex probability distributions.
Efficient Empowerment
This is a natural candidate for an intrinsic reward signal in the context of reinforcement learning: the agent will place itself in a situation where its action have maximum stability and maximum influence on the future.
Fast Adaptive Weight Noise
Marginalising out uncertain quantities within the internal representations or parameters of neural networks is of central importance for a wide range of learning techniques, such as empirical, variational or full Bayesian methods.
Learning Stochastic Recurrent Networks
Leveraging advances in variational inference, we propose to enhance recurrent neural networks with latent variables, resulting in Stochastic Recurrent Networks (STORNs).
Regularizing Recurrent Networks - On Injected Noise and Norm-based Methods
Advancements in parallel processing have lead to a surge in multilayer perceptrons' (MLP) applications and deep learning in the past decades.
Variational inference of latent state sequences using Recurrent Networks
Recent advances in the estimation of deep directed graphical models and recurrent networks let us contribute to the removal of a blind spot in the area of probabilistc modelling of time series.
On Fast Dropout and its Applicability to Recurrent Networks
Recurrent Neural Networks (RNNs) are rich models for the processing of sequential data.
Convolutional Neural Networks learn compact local image descriptors
A standard deep convolutional neural network paired with a suitable loss function learns compact local image descriptors that perform comparably to state-of-the art approaches.
Unsupervised Feature Learning for low-level Local Image Descriptors
Unsupervised feature learning has shown impressive results for a wide range of input modalities, in particular for object classification tasks in computer vision.
Learning Sequence Neighbourhood Metrics
Recurrent neural networks (RNNs) in combination with a pooling operator and the neighbourhood components analysis (NCA) objective function are able to detect the characterizing dynamics of sequences and embed them into a fixed-length vector space of arbitrary dimensionality.
