1,720,961 research outputs found
Improving the Effectiveness of Graph Neural Networks in Practical Scenarios
No full textIn the past decade, deep learning has given new life to the field of artificial intelligence, providing many breakthroughs in areas like computer vision, natural language processing, audio, game-playing, and biology. The past few years have seen a particular interest in developing and applying deep learning models that can operate on graph-structured data, also called graph neural networks (GNNs). This is not surprising, as graphs are core data structures that are used to model relationships between different entities, which is a common scenario that appears in molecules, social networks, and physical interactions, just to cite a few.
GNNs have achieved many successes, and have been studied from both a theoretical, and a practical point of view. However, several questions remain unanswered. In this thesis, we focus on three open questions regarding practical aspects of GNNs, and we propose effective solutions for tackling them.
The first question concerns global structural information, i.e., information that depends on the global structure of the graph. This kind of information is particularly difficult to capture for GNNs, which are targeted towards local interactions. While global structural information has been previously overlooked, we show that it has an important impact on practical applications, and we propose a regularization strategy to provide this information to GNNs during training.
The second question concerns size-generalization, which is the ability of GNNs to generalize from small to large graphs. While GNNs are designed to operate on graphs of any size, it is observed that when trained on small graphs, they struggle at generalizing to large graphs. This is particularly problematic, as in certain domains, obtaining labels for large graphs is prohibitive. Furthermore, training on large graphs may require expensive computational resources. We propose a novel regularization strategy, that can be applied on any GNN, and that can improve size-generalization capabilities of up to 30%.
The third question focuses on multi-task settings. GNNs work by exchanging messages between nodes, and using learnable functions to produce node embeddings that encode structural and feature-related information. During training, GNNs tend to optimize the produced embeddings to the training loss, making it hard to reuse them effectively for different tasks. This requires the training of multiple models, and the use of different embeddings for different tasks. We propose a training strategy based on meta-learning that provides a single set of embeddings that can be used to perform multiple tasks while achieving performance comparable to those of single-task end-to-end trained models.In the past decade, deep learning has given new life to the field of artificial intelligence, providing many breakthroughs in areas like computer vision, natural language processing, audio, game-playing, and biology. The past few years have seen a particular interest in developing and applying deep learning models that can operate on graph-structured data, also called graph neural networks (GNNs). This is not surprising, as graphs are core data structures that are used to model relationships between different entities, which is a common scenario that appears in molecules, social networks, and physical interactions, just to cite a few.
GNNs have achieved many successes, and have been studied from both a theoretical, and a practical point of view. However, several questions remain unanswered. In this thesis, we focus on three open questions regarding practical aspects of GNNs, and we propose effective solutions for tackling them.
The first question concerns global structural information, i.e., information that depends on the global structure of the graph. This kind of information is particularly difficult to capture for GNNs, which are targeted towards local interactions. While global structural information has been previously overlooked, we show that it has an important impact on practical applications, and we propose a regularization strategy to provide this information to GNNs during training.
The second question concerns size-generalization, which is the ability of GNNs to generalize from small to large graphs. While GNNs are designed to operate on graphs of any size, it is observed that when trained on small graphs, they struggle at generalizing to large graphs. This is particularly problematic, as in certain domains, obtaining labels for large graphs is prohibitive. Furthermore, training on large graphs may require expensive computational resources. We propose a novel regularization strategy, that can be applied on any GNN, and that can improve size-generalization capabilities of up to 30%.
The third question focuses on multi-task settings. GNNs work by exchanging messages between nodes, and using learnable functions to produce node embeddings that encode structural and feature-related information. During training, GNNs tend to optimize the produced embeddings to the training loss, making it hard to reuse them effectively for different tasks. This requires the training of multiple models, and the use of different embeddings for different tasks. We propose a training strategy based on meta-learning that provides a single set of embeddings that can be used to perform multiple tasks while achieving performance comparable to those of single-task end-to-end trained models
Scalable Theory-Driven Regularization of Scene Graph Generation Models
Full text linkSeveral techniques have recently aimed to improve the performance of deep learning models for Scene Graph Generation (SGG) by incorporating background knowledge. State-of-the-art techniques can be divided into two families: one where the background knowledge is incorporated into the model in a subsymbolic fashion, and another in which the background knowledge is maintained in symbolic form. Despite promising results, both families of techniques face several shortcomings: the first one requires ad-hoc, more complex neural architectures increasing the training or inference cost; the second one suffers from limited scalability w.r.t. the size of the background knowledge. Our work introduces a regularization technique for injecting symbolic background knowledge into neural SGG models that overcomes the limitations of prior art. Our technique is model-agnostic, does not incur any cost at inference time, and scales to previously unmanageable background knowledge sizes. We demonstrate that our technique can improve the accuracy of state-of-the-art SGG models, by up to 33%
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
A Deep Learning Model for Personalized Human Activity Recognition
Full text linkHuman Activity Recognition (HAR) is a time series classification task that involves predicting the movement or action of a person based on sensor data. In the past the problem has been tackled by hand crafting features, which is time consuming and doesn’t generalize well. In this thesis we firstly analyze a Deep Learning model created for Time Series data that has set the state of the art in HAR. We then propose a customized version of the framework that is capable of adapting to a user
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
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