212 research outputs found
Neural ODEs with stochastic vector field mixtures
It was recently shown that neural ordinary differential equation models
cannot solve fundamental and seemingly straightforward tasks even with
high-capacity vector field representations. This paper introduces two other
fundamental tasks to the set that baseline methods cannot solve, and proposes
mixtures of stochastic vector fields as a model class that is capable of
solving these essential problems. Dynamic vector field selection is of critical
importance for our model, and our approach is to propagate component
uncertainty over the integration interval with a technique based on forward
filtering. We also formalise several loss functions that encourage desirable
properties on the trajectory paths, and of particular interest are those that
directly encourage fewer expected function evaluations. Experimentally, we
demonstrate that our model class is capable of capturing the natural dynamics
of human behaviour; a notoriously volatile application area. Baseline
approaches cannot adequately model this problem
Kernel bandwidth estimation for moving object detection in non-stabilized cameras
The evolution of the television market is led by 3DTV technology, and this tendency can accelerate during the next years according to expert forecasts. However, 3DTV delivery by broadcast networks is not currently developed enough, and acts as a bottleneck for the complete deployment of the technology. Thus, increasing interest is dedicated to ste-reo 3DTV formats compatible with current HDTV video equipment and infrastructure, as they may greatly encourage 3D acceptance. In this paper, different subsampling schemes for HDTV compatible transmission of both progressive and interlaced stereo 3DTV are studied and compared. The frequency characteristics and preserved frequency content of each scheme are analyzed, and a simple interpolation filter is specially designed. Finally, the advantages and disadvantages of the different schemes and filters are evaluated through quality testing on several progressive and interlaced video sequences
Conditional t-SNE: Complementary t-SNE embeddings through factoring out prior information
Dimensionality reduction and manifold learning methods such as t-Distributed
Stochastic Neighbor Embedding (t-SNE) are routinely used to map
high-dimensional data into a 2-dimensional space to visualize and explore the
data. However, two dimensions are typically insufficient to capture all
structure in the data, the salient structure is often already known, and it is
not obvious how to extract the remaining information in a similarly effective
manner. To fill this gap, we introduce \emph{conditional t-SNE} (ct-SNE), a
generalization of t-SNE that discounts prior information from the embedding in
the form of labels. To achieve this, we propose a conditioned version of the
t-SNE objective, obtaining a single, integrated, and elegant method. ct-SNE has
one extra parameter over t-SNE; we investigate its effects and show how to
efficiently optimize the objective. Factoring out prior knowledge allows
complementary structure to be captured in the embedding, providing new
insights. Qualitative and quantitative empirical results on synthetic and
(large) real data show ct-SNE is effective and achieves its goal
Cost-sensitive classification based on Bregman divergences
The main object of this PhD. Thesis is the identification, characterization and
study of new loss functions to address the so-called cost-sensitive classification. Many
decision problems are intrinsically cost-sensitive. However, the dominating preference
for cost-insensitive methods in the machine learning literature is a natural consequence
of the fact that true costs in real applications are di fficult to evaluate.
Since, in general, uncovering the correct class of the data is less costly than any
decision error, designing low error decision systems is a reasonable (but suboptimal)
approach. For instance, consider the classification of credit applicants as either being good customers (will pay back the credit) or bad customers (will fail to pay o part of the credit). The cost of classifying one risky borrower as good could be much higher than the cost of classifying a potentially good customer as bad.
Our proposal relies on Bayes decision theory where the goal is to assign instances
to the class with minimum expected cost. The decision is made involving both costs and posterior probabilities of the classes. Obtaining calibrated probability
estimates at the classifier output requires a suitable learning machine, a large enough
representative data set as well as an adequate loss function to be minimized during
learning. The design of the loss function can be aided by the costs: classical decision
theory shows that cost matrices de ne class boundaries determined by posterior class
probability estimates. Strictly speaking, in order to make optimal decisions, accurate
probability estimates are only required near the decision boundaries. It is key to
point out that the election of the loss function becomes especially relevant when
the prior knowledge about the problem is limited or the available training examples
are somehow unsuitable. In those cases, different loss functions lead to dramatically
different posterior probabilities estimates. We focus our study on the set of Bregman
divergences. These divergences offer a rich family of proper losses that has recently
become very popular in the machine learning community [Nock and Nielsen, 2009,
Reid and Williamson, 2009a].
The first part of the Thesis deals with the development of a novel parametric family of multiclass Bregman divergences which captures the information in the cost
matrix, so that the loss function is adapted to each specific problem. Multiclass costsensitive learning is one of the main challenges in cost-sensitive learning and, through this parametric family, we provide a natural framework to successfully overcome
binary tasks. Following this idea, two lines are explored:
Cost-sensitive supervised classification: We derive several asymptotic results.
The first analysis guarantees that the proposed Bregman divergence has maximum sensitivity to changes at probability vectors near the decision regions. Further analysis shows that the optimization of this Bregman divergence becomes equivalent to minimizing the overall cost regret in non-separable problems, and to maximizing a margin in separable problems.
Cost-sensitive semi-supervised classification: When labeled data is
scarce but unlabeled data is widely available, semi-supervised learning is an
useful tool to make the most of the unlabeled data. We discuss an optimization
problem relying on the minimization of our parametric family of Bregman divergences, using both labeled and unlabeled data, based on what is called the Entropy Minimization principle. We propose the rst multiclass cost-sensitive semi-supervised algorithm, under the assumption that inter-class separation is stronger than intra-class separation.
The second part of the Thesis deals with the transformation of this parametric family of Bregman divergences into a sequence of Bregman divergences. Work along this line can be further divided into two additional areas:
Foundations of sequences of Bregman divergences: We generalize some
previous results about the design and characterization of Bregman divergences
that are suitable for learning and their relationship with convexity. In addition,
we aim to broaden the subset of Bregman divergences that are interesting for
cost-sensitive learning. Under very general conditions, we nd sequences of (cost-sensitive) Bregman divergences, whose minimization provides minimum (cost-sensitive) risk for non-separable problems and some type of maximum margin classifiers in separable cases.
Learning with example-dependent costs: A strong assumption is widespread through most cost-sensitive learning algorithms: misclassification costs are the same for all examples. In many cases this statement is not true.
We claim that using the example-dependent costs directly is more natural and will lead to the production of more accurate classifiers. For these reasons, we consider the extension of cost-sensitive sequences of Bregman losses to example-dependent cost scenarios to generate finely tuned posterior probability estimates
Online Feature Selection for Activity Recognition using Reinforcement Learning with Multiple Feedback
Recent advances in both machine learning and Internet-of-Things have
attracted attention to automatic Activity Recognition, where users wear a
device with sensors and their outputs are mapped to a predefined set of
activities. However, few studies have considered the balance between wearable
power consumption and activity recognition accuracy. This is particularly
important when part of the computational load happens on the wearable device.
In this paper, we present a new methodology to perform feature selection on the
device based on Reinforcement Learning (RL) to find the optimum balance between
power consumption and accuracy. To accelerate the learning speed, we extend the
RL algorithm to address multiple sources of feedback, and use them to tailor
the policy in conjunction with estimating the feedback accuracy. We evaluated
our system on the SPHERE challenge dataset, a publicly available research
dataset. The results show that our proposed method achieves a good trade-off
between wearable power consumption and activity recognition accuracy
Statistical moving object detection for mobile devices with camera
A novel and high-quality system for moving object detection in sequences recorded with moving cameras is proposed. This system is based on the collaboration between an automatic homography estimation module for image alignment, and a robust moving object detection using an efficient spatiotemporal nonparametric background modeling
Versatile Bayesian classifier for moving object detection by non-parametric background-foreground modeling
Along the recent years, several moving object detection strategies by non-parametric background-foreground modeling have been proposed. To combine both models and to obtain the probability of a pixel to belong to the foreground, these strategies make use of Bayesian classifiers. However, these classifiers do not allow to take advantage of additional prior information at different pixels. So, we propose a novel and efficient alternative Bayesian classifier that is suitable for this kind of strategies and that allows the use of whatever prior information. Additionally, we present an effective method to dynamically estimate prior probability from the result of a particle filter-based tracking strategy
Adaptable Bayesian classifier for spatiotemporal nonparametric moving object detection strategies
Electronic devices endowed with camera platforms require new and powerful machine vision applications, which commonly include moving object detection strategies. To obtain high-quality results, the most recent strategies estimate nonparametrically background and foreground models and combine them by means of a Bayesian classifier. However, typical classifiers are limited by the use of constant prior values and they do not allow the inclusion of additional spatiodependent prior information. In this Letter, we propose an alternative Bayesian classifier that, unlike those reported before, allows the use of additional prior information obtained from any source and depending on the spatial position of each pixel
Sampling Based On Natural Image Statistics Improves Local Surrogate Explainers
Many problems in computer vision have recently been tackled using models
whose predictions cannot be easily interpreted, most commonly deep neural
networks. Surrogate explainers are a popular post-hoc interpretability method
to further understand how a model arrives at a particular prediction. By
training a simple, more interpretable model to locally approximate the decision
boundary of a non-interpretable system, we can estimate the relative importance
of the input features on the prediction. Focusing on images, surrogate
explainers, e.g., LIME, generate a local neighbourhood around a query image by
sampling in an interpretable domain. However, these interpretable domains have
traditionally been derived exclusively from the intrinsic features of the query
image, not taking into consideration the manifold of the data the
non-interpretable model has been exposed to in training (or more generally, the
manifold of real images). This leads to suboptimal surrogates trained on
potentially low probability images. We address this limitation by aligning the
local neighbourhood on which the surrogate is trained with the original
training data distribution, even when this distribution is not accessible. We
propose two approaches to do so, namely (1) altering the method for sampling
the local neighbourhood and (2) using perceptual metrics to convey some of the
properties of the distribution of natural images.Comment: 12 pages, 7 figure
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