40 research outputs found
Manifold Relevance Determination
In this paper we present a fully Bayesian latent variable model which
exploits conditional nonlinear(in)-dependence structures to learn an efficient
latent representation. The latent space is factorized to represent shared and
private information from multiple views of the data. In contrast to previous
approaches, we introduce a relaxation to the discrete segmentation and allow
for a "softly" shared latent space. Further, Bayesian techniques allow us to
automatically estimate the dimensionality of the latent spaces. The model is
capable of capturing structure underlying extremely high dimensional spaces.
This is illustrated by modelling unprocessed images with tenths of thousands of
pixels. This also allows us to directly generate novel images from the trained
model by sampling from the discovered latent spaces. We also demonstrate the
model by prediction of human pose in an ambiguous setting. Our Bayesian
framework allows us to perform disambiguation in a principled manner by
including latent space priors which incorporate the dynamic nature of the data.Comment: ICML201
Tracking known and unknown human activites
Tracking known and unknown human activite
Deep Gaussian Processes
In this paper we introduce deep Gaussian process (GP) models. Deep GPs are a
deep belief network based on Gaussian process mappings. The data is modeled as
the output of a multivariate GP. The inputs to that Gaussian process are then
governed by another GP. A single layer model is equivalent to a standard GP or
the GP latent variable model (GP-LVM). We perform inference in the model by
approximate variational marginalization. This results in a strict lower bound
on the marginal likelihood of the model which we use for model selection
(number of layers and nodes per layer). Deep belief networks are typically
applied to relatively large data sets using stochastic gradient descent for
optimization. Our fully Bayesian treatment allows for the application of deep
models even when data is scarce. Model selection by our variational bound shows
that a five layer hierarchy is justified even when modelling a digit data set
containing only 150 examples.Comment: 9 pages, 8 figures. Appearing in Proceedings of the 16th
International Conference on Artificial Intelligence and Statistics (AISTATS)
201
Doubly Stochastic Variational Inference for Deep Gaussian Processes
Gaussian processes (GPs) are a good choice for function approximation as they
are flexible, robust to over-fitting, and provide well-calibrated predictive
uncertainty. Deep Gaussian processes (DGPs) are multi-layer generalisations of
GPs, but inference in these models has proved challenging. Existing approaches
to inference in DGP models assume approximate posteriors that force
independence between the layers, and do not work well in practice. We present a
doubly stochastic variational inference algorithm, which does not force
independence between layers. With our method of inference we demonstrate that a
DGP model can be used effectively on data ranging in size from hundreds to a
billion points. We provide strong empirical evidence that our inference scheme
for DGPs works well in practice in both classification and regression.Comment: NIPS 201
Affective Facial Expression Processing via Simulation: A Probabilistic Model
Understanding the mental state of other people is an important skill for
intelligent agents and robots to operate within social environments. However,
the mental processes involved in `mind-reading' are complex. One explanation of
such processes is Simulation Theory - it is supported by a large body of
neuropsychological research. Yet, determining the best computational model or
theory to use in simulation-style emotion detection, is far from being
understood.
In this work, we use Simulation Theory and neuroscience findings on
Mirror-Neuron Systems as the basis for a novel computational model, as a way to
handle affective facial expressions. The model is based on a probabilistic
mapping of observations from multiple identities onto a single fixed identity
(`internal transcoding of external stimuli'), and then onto a latent space
(`phenomenological response'). Together with the proposed architecture we
present some promising preliminary resultsComment: Annual International Conference on Biologically Inspired Cognitive
Architectures - BICA 201
Transfering Nonlinear Representations using Gaussian Processes with a Shared Latent Space
When a series of problems are related, representations derived fromlearning earlier tasks may be useful in solving later problems. Inthis paper we propose a novel approach to transfer learning withlow-dimensional, non-linear latent spaces. We show how suchrepresentations can be jointly learned across multiple tasks in adiscriminative probabilistic regression framework. When transferred tonew tasks with relatively few training examples, learning can befaster and/or more accurate. Experiments on a digit recognition taskshow significantly improved performance when compared to baselineperformance with the original feature representation or with arepresentation derived from a semi-supervised learning approach
Variational auto-encoded deep Gaussian processes
We develop a scalable deep non-parametric generative model by augmenting deep
Gaussian processes with a recognition model. Inference is performed in a novel
scalable variational framework where the variational posterior distributions
are reparametrized through a multilayer perceptron. The key aspect of this
reformulation is that it prevents the proliferation of variational parameters
which otherwise grow linearly in proportion to the sample size. We derive a new
formulation of the variational lower bound that allows us to distribute most of
the computation in a way that enables to handle datasets of the size of
mainstream deep learning tasks. We show the efficacy of the method on a variety
of challenges including deep unsupervised learning and deep Bayesian
optimization