83 research outputs found
Learned SVD: solving inverse problems via hybrid autoencoding
Our world is full of physics-driven data where effective mappings between
data manifolds are desired. There is an increasing demand for understanding
combined model-based and data-driven methods. We propose a nonlinear, learned
singular value decomposition (L-SVD), which combines autoencoders that
simultaneously learn and connect latent codes for desired signals and given
measurements. We provide a convergence analysis for a specifically structured
L-SVD that acts as a regularisation method. In a more general setting, we
investigate the topic of model reduction via data dimensionality reduction to
obtain a regularised inversion. We present a promising direction for solving
inverse problems in cases where the underlying physics are not fully understood
or have very complex behaviour. We show that the building blocks of learned
inversion maps can be obtained automatically, with improved performance upon
classical methods and better interpretability than black-box methods
Deep learning cardiac motion analysis for human survival prediction
Motion analysis is used in computer vision to understand the behaviour of
moving objects in sequences of images. Optimising the interpretation of dynamic
biological systems requires accurate and precise motion tracking as well as
efficient representations of high-dimensional motion trajectories so that these
can be used for prediction tasks. Here we use image sequences of the heart,
acquired using cardiac magnetic resonance imaging, to create time-resolved
three-dimensional segmentations using a fully convolutional network trained on
anatomical shape priors. This dense motion model formed the input to a
supervised denoising autoencoder (4Dsurvival), which is a hybrid network
consisting of an autoencoder that learns a task-specific latent code
representation trained on observed outcome data, yielding a latent
representation optimised for survival prediction. To handle right-censored
survival outcomes, our network used a Cox partial likelihood loss function. In
a study of 302 patients the predictive accuracy (quantified by Harrell's
C-index) was significantly higher (p < .0001) for our model C=0.73 (95 CI:
0.68 - 0.78) than the human benchmark of C=0.59 (95 CI: 0.53 - 0.65). This
work demonstrates how a complex computer vision task using high-dimensional
medical image data can efficiently predict human survival
Generalised Gaussian Process Latent Variable Models (GPLVM) with Stochastic Variational Inference
Gaussian process latent variable models (GPLVM) are a flexible and non-linear
approach to dimensionality reduction, extending classical Gaussian processes to
an unsupervised learning context. The Bayesian incarnation of the GPLVM Titsias
and Lawrence, 2010] uses a variational framework, where the posterior over
latent variables is approximated by a well-behaved variational family, a
factorized Gaussian yielding a tractable lower bound. However, the
non-factories ability of the lower bound prevents truly scalable inference. In
this work, we study the doubly stochastic formulation of the Bayesian GPLVM
model amenable with minibatch training. We show how this framework is
compatible with different latent variable formulations and perform experiments
to compare a suite of models. Further, we demonstrate how we can train in the
presence of massively missing data and obtain high-fidelity reconstructions. We
demonstrate the model's performance by benchmarking against the canonical
sparse GPLVM for high-dimensional data examples.Comment: AISTATS 202
ICAM: Interpretable Classification via Disentangled Representations and Feature Attribution Mapping
Feature attribution (FA), or the assignment of class-relevance to different
locations in an image, is important for many classification problems but is
particularly crucial within the neuroscience domain, where accurate mechanistic
models of behaviours, or disease, require knowledge of all features
discriminative of a trait. At the same time, predicting class relevance from
brain images is challenging as phenotypes are typically heterogeneous, and
changes occur against a background of significant natural variation. Here, we
present a novel framework for creating class specific FA maps through
image-to-image translation. We propose the use of a VAE-GAN to explicitly
disentangle class relevance from background features for improved
interpretability properties, which results in meaningful FA maps. We validate
our method on 2D and 3D brain image datasets of dementia (ADNI dataset), ageing
(UK Biobank), and (simulated) lesion detection. We show that FA maps generated
by our method outperform baseline FA methods when validated against ground
truth. More significantly, our approach is the first to use latent space
sampling to support exploration of phenotype variation. Our code will be
available online at https://github.com/CherBass/ICAM.Comment: Submitted to NeurIPS 2020: Neural Information Processing Systems.
Keywords: interpretable, classification, feature attribution, domain
translation, variational autoencoder, generative adversarial network,
neuroimagin
Deep Learning Methods for Human Activity Recognition using Wearables
Wearable sensors provide an infrastructure-less multi-modal sensing method. Current
trends point to a pervasive integration of wearables into our lives with these devices
providing the basis for wellness and healthcare applications across rehabilitation,
caring for a growing older population, and improving human performance.
Fundamental to these applications is our ability to automatically and accurately
recognise human activities from often tiny sensors embedded in wearables. In this
dissertation, we consider the problem of human activity recognition (HAR) using
multi-channel time-series data captured by wearable sensors.
Our collective know-how regarding the solution of HAR problems with wearables has
progressed immensely through the use of deep learning paradigms. Nevertheless, this
field still faces unique methodological challenges. As such, this dissertation focuses on
developing end-to-end deep learning frameworks to promote HAR application opportunities
using wearable sensor technologies and to mitigate specific associated challenges. In our
efforts, the investigated problems cover a diverse range of HAR challenges and spans
from fully supervised to unsupervised problem domains.
In order to enhance automatic feature extraction from multi-channel time-series
data for HAR, the problem of learning enriched and highly discriminative activity
feature representations with deep neural networks is considered. Accordingly, novel
end-to-end network elements are designed which: (a) exploit the latent relationships
between multi-channel sensor modalities and specific activities, (b) employ effective
regularisation through data-agnostic augmentation for multi-modal sensor data
streams, and (c) incorporate optimization objectives to encourage minimal intra-class
representation differences, while maximising inter-class differences to achieve more
discriminative features.
In order to promote new opportunities in HAR with emerging battery-less sensing
platforms, the problem of learning from irregularly sampled and temporally sparse readings
captured by passive sensing modalities is considered. For the first time, an efficient
set-based deep learning framework is developed to address the problem. This
framework is able to learn directly from the generated data, bypassing the need for
the conventional interpolation pre-processing stage. In order to address the multi-class window problem and create potential solutions
for the challenging task of concurrent human activity recognition, the problem of
enabling simultaneous prediction of multiple activities for sensory segments is considered.
As such, the flexibility provided by the emerging set learning concepts is further
leveraged to introduce a novel formulation of HAR. This formulation treats HAR
as a set prediction problem and elegantly caters for segments carrying sensor data
from multiple activities. To address this set prediction problem, a unified deep HAR
architecture is designed that: (a) incorporates a set objective to learn mappings from
raw input sensory segments to target activity sets, and (b) precedes the supervised
learning phase with unsupervised parameter pre-training to exploit unlabelled data
for better generalisation performance.
In order to leverage the easily accessible unlabelled activity data-streams to serve
downstream classification tasks, the problem of unsupervised representation learning from
multi-channel time-series data is considered. For the first time, a novel recurrent
generative adversarial (GAN) framework is developed that explores the GAN’s latent
feature space to extract highly discriminating activity features in an unsupervised
fashion. The superiority of the learned representations is substantiated by their
ability to outperform the de facto unsupervised approaches based on autoencoder
frameworks. At the same time, they rival the recognition performance of fully
supervised trained models on downstream classification benchmarks.
In recognition of the scarcity of large-scale annotated sensor datasets and the
tediousness of collecting additional labelled data in this domain, the hitherto unexplored
problem of end-to-end clustering of human activities from unlabelled wearable data is
considered. To address this problem, a first study is presented for the purpose of
developing a stand-alone deep learning paradigm to discover semantically meaningful
clusters of human actions. In particular, the paradigm is intended to: (a) leverage
the inherently sequential nature of sensory data, (b) exploit self-supervision from
reconstruction and future prediction tasks, and (c) incorporate clustering-oriented
objectives to promote the formation of highly discriminative activity clusters. The
systematic investigations in this study create new opportunities for HAR to learn
human activities using unlabelled data that can be conveniently and cheaply collected
from wearables.Thesis (Ph.D.) -- University of Adelaide, School of Computer Science, 202
ICAM-reg: Interpretable Classification and Regression with Feature Attribution for Mapping Neurological Phenotypes in Individual Scans
Feature attribution (FA), or the assignment of class-relevance to different locations in an image, is important for many classification and regression problems but is particularly crucial within the neuroscience domain, where accurate mechanistic models of behaviours, or disease, require knowledge of all features discriminative of a trait. At the same time, predicting class relevance from brain images is challenging as phenotypes are typically heterogeneous, and changes occur against a background of significant natural variation. Here, we present an extension of the ICAM framework for creating prediction specific FA maps through image-to-image translation
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