17,689 research outputs found
One-Shot Learning using Mixture of Variational Autoencoders: a Generalization Learning approach
Deep learning, even if it is very successful nowadays, traditionally needs
very large amounts of labeled data to perform excellent on the classification
task. In an attempt to solve this problem, the one-shot learning paradigm,
which makes use of just one labeled sample per class and prior knowledge,
becomes increasingly important. In this paper, we propose a new one-shot
learning method, dubbed MoVAE (Mixture of Variational AutoEncoders), to perform
classification. Complementary to prior studies, MoVAE represents a shift of
paradigm in comparison with the usual one-shot learning methods, as it does not
use any prior knowledge. Instead, it starts from zero knowledge and one labeled
sample per class. Afterward, by using unlabeled data and the generalization
learning concept (in a way, more as humans do), it is capable to gradually
improve by itself its performance. Even more, if there are no unlabeled data
available MoVAE can still perform well in one-shot learning classification. We
demonstrate empirically the efficiency of our proposed approach on three
datasets, i.e. the handwritten digits (MNIST), fashion products
(Fashion-MNIST), and handwritten characters (Omniglot), showing that MoVAE
outperforms state-of-the-art one-shot learning algorithms
Event-Based Modeling with High-Dimensional Imaging Biomarkers for Estimating Spatial Progression of Dementia
Event-based models (EBM) are a class of disease progression models that can
be used to estimate temporal ordering of neuropathological changes from
cross-sectional data. Current EBMs only handle scalar biomarkers, such as
regional volumes, as inputs. However, regional aggregates are a crude summary
of the underlying high-resolution images, potentially limiting the accuracy of
EBM. Therefore, we propose a novel method that exploits high-dimensional
voxel-wise imaging biomarkers: n-dimensional discriminative EBM (nDEBM). nDEBM
is based on an insight that mixture modeling, which is a key element of
conventional EBMs, can be replaced by a more scalable semi-supervised support
vector machine (SVM) approach. This SVM is used to estimate the degree of
abnormality of each region which is then used to obtain subject-specific
disease progression patterns. These patterns are in turn used for estimating
the mean ordering by fitting a generalized Mallows model. In order to validate
the biomarker ordering obtained using nDEBM, we also present a framework for
Simulation of Imaging Biomarkers' Temporal Evolution (SImBioTE) that mimics
neurodegeneration in brain regions. SImBioTE trains variational auto-encoders
(VAE) in different brain regions independently to simulate images at varying
stages of disease progression. We also validate nDEBM clinically using data
from the Alzheimer's Disease Neuroimaging Initiative (ADNI). In both
experiments, nDEBM using high-dimensional features gave better performance than
state-of-the-art EBM methods using regional volume biomarkers. This suggests
that nDEBM is a promising approach for disease progression modeling.Comment: IPMI 201
Distributed Gaussian Processes
To scale Gaussian processes (GPs) to large data sets we introduce the robust
Bayesian Committee Machine (rBCM), a practical and scalable product-of-experts
model for large-scale distributed GP regression. Unlike state-of-the-art sparse
GP approximations, the rBCM is conceptually simple and does not rely on
inducing or variational parameters. The key idea is to recursively distribute
computations to independent computational units and, subsequently, recombine
them to form an overall result. Efficient closed-form inference allows for
straightforward parallelisation and distributed computations with a small
memory footprint. The rBCM is independent of the computational graph and can be
used on heterogeneous computing infrastructures, ranging from laptops to
clusters. With sufficient computing resources our distributed GP model can
handle arbitrarily large data sets.Comment: 10 pages, 5 figures. Appears in Proceedings of ICML 201
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