1,721,010 research outputs found
Towards robust machine learning with graph neural networks
Full text linkIn order to apply Neural Networks in safety-critical settings, such as healthcare or autonomous driving, we need to be able to analyse their robustness against adversarial attacks. These attacks perturb natural images by adding small, carefully chosen perturbations to them that are imperceptible to the human eye. Trained neural networks with high training and validation accuracy often misclassify a large number of these perturbed images. In this thesis we propose several new methods aimed at analysing the robustness of trained neural networks to adversarial attacks.
In the first part, we improve upon existing methods to generate adversarial examples more efficiently. We note that past work in this field has relied on optimization methods that ignore the inherent structure of the problem and data, or generative methods that rely purely on learning and often fail to generate adversarial examples where they are hard to find. To alleviate these deficiencies, we propose a novel stand-alone attack based on a GNN that takes advantage of the strengths of both approaches. Our GNN computes descent directions to guide an iterative procedure towards adversarial examples.
Our next contribution is inspired by the observation that many state-of-the-art adversarial attacks require many random restarts to generate adversarial examples. Each time we perform a restart we ignore all previous unsuccessful runs. In order to alleviate this deficiency, we propose a method that learns from its mistakes. Specifically, our method uses GNNs as an attention, to greatly reduce the search space for future iterations of the attacks.
For our final contribution, we note that adversarial attacks may fail, even where adversarial examples exist. We thus focus on formal complete neural network verification which returns a sound and complete proof of robustness. Recent years have witnessed the deployment of branch-and-bound (BaB) frameworks for formal verification in deep learning. The main computational bottleneck of BaB is the estimation of lower bounds. Past work in this field has relied on traditional optimization algorithms whose inefficiencies have limited their scope. To alleviate this deficiency, we propose a novel graph neural network (GNN) based approach. Our GNN aims to compute a dual solution of the convex relaxation, thereby providing a valid lower bound, which, if positive, proves robustness
Task-oriented learning of structured probability distributions
Full text linkMachine learning models automatically learn from historical data to predict unseen
events. Such events are often represented as complex multi-dimensional structures.
In many cases there is high uncertainty in the prediction process. Research has
developed probabilistic models to capture distributions of complex objects, but
their learning objective is often agnostic of the evaluation loss. In this thesis,
we address the aforementioned defficiency by designing probabilistic methods for
structured object prediction that take into account the task at hand.
First, we consider that the task at hand is explicitly known, but there is ambiguity
in the prediction due to an unobserved (latent) variable. We develop a
framework for latent structured output prediction that unifies existing empirical
risk minimisation methods. We empirically demonstrate that for large and ambiguous
latent spaces, performing prediction by minimising the uncertainty in the latent
variable provides more accurate results.
Empirical risk minimisation methods predict only a pointwise estimate of the
output, however there can be uncertainty on the output value itself. To tackle this
deficiency, we introduce a novel type of model to perform probabilistic structured
output prediction. Our training objective minimises a dissimilarity coefficient between
the data distribution and the model's distribution. This coefficient is defined
according to a loss of choice, thereby our objective can be tailored to the task loss.
We empirically demonstrate the ability of our model to capture distributions over
complex objects.
Finally, we tackle a setting where the task loss is implicitly expressed. Specifically,
we consider the case of grouped observations. We propose a new model for
learning a representation of the data that decomposes according to the semantics
behind this grouping, while allowing efficient test-time inference. We experimentally
demonstrate that our model learns a disentangled and controllable representation,
leverages grouping information when available, and generalises to unseen observations.</p
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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