1,720,972 research outputs found
Advancing adversarial robustness with feature desensitization and synthesized data
Get PDFCette thèse porte sur la question critique de la vulnérabilité des modèles d’apprentissage profond face aux attaques adversariales. Susceptibles à de légères perturbations invisibles à l'œil humain, ces modèles peuvent produire des prédictions erronées. Les attaques adversariales représentent une menace importante quant à l’utilisation de ces modèles dans des systèmes de sécurité critique. Pour atténuer ces risques, l’entraînement adversarial s’impose comme une approche prometteuse, consistant à entraîner les modèles sur des exemples adversariaux pour renforcer leur robustesse.
Dans le Chapitre 1, nous offrons un aperçu détaillé de la vulnérabilité adversariale, en décrivant la création d’échantillons adversariaux ainsi que leurs répercussions dans le monde réel. Nous expliquons le processus de conception de ces exemples et présentons divers scénarios illustrant leurs conséquences potentiellement catastrophiques. En outre, nous examinons les défis associés à l'entraînement adversarial, en mettant l’emphase sur des défis tels que le manque de robustesse face à une large gamme d’attaques et le compromis entre robustesse et généralisation, qui sont au cœur de cette étude.
Le Chapitre 2 présente la Désensibilisation des Caractéristiques Adversariales (AFD), une méthode innovante utilisant des techniques d’adaptation de domaine pour renforcer la robustesse adversariale. L’AFD vise à apprendre des caractéristiques invariantes aux perturbations adversariales, augmentant ainsi la résilience face à divers types et intensités d’attaques. Cette approche consiste à entraîner simultanément un discriminateur de domaine et un classificateur afin de réduire la divergence entre les représentations de données naturelles et adversariales. En alignant les caractéristiques des deux domaines, l'AFD garantit que les caractéristiques apprises sont à la fois prédictives et robustes, atténuant ainsi le surapprentissage à des schémas d'attaque spécifiques et favorisant une défense plus globale.
Le Chapitre 3 présente l’Entraînement Adversarial avec Données Synthétisées, une méthode visant à combler l’écart entre la robustesse et la généralisation des réseaux de neurones. En utilisant des données synthétisées générées par des techniques avancées, ce chapitre explore comment l'incorporation de telles données peut atténuer le surapprentissage et améliorer la performance globale des modèles entraînés adversarialement. Les résultats montrent que, bien que l’entraînement adversarial soit souvent confronté à un compromis entre robustesse et généralisation, l’utilisation de données synthétisées permet de maintenir une haute précision des données corrompues et hors distribution sans compromettre la robustesse. Cette approche offre une voie prometteuse pour développer des réseaux de neurones à la fois résilients aux attaques adversariales et capables de bien généraliser à de nombreux scénarios.
Le Chapitre 4 conclut la thèse en résumant les principales découvertes et contributions de cette recherche. De plus, il propose plusieurs pistes pour des recherches futures visant à améliorer davantage la sécurité et la fiabilité des modèles d’apprentissage profond. Ces pistes incluent l’exploration de l’effet des données synthétisées sur une gamme plus large de tâches de généralisation, le développement d’approches alternatives moins coûteuses en termes de calcul d’entraînement, et l’adaptation de nouvelles techniques guidées par l’information en retour pour synthétiser des données qui favorise l’efficacité d’échantillonnage. En suivant ces directions, les recherches futures pourront s’appuyer sur les bases présentées dans cette thèse et continuer à faire progresser le domaine de la robustesse adversariale, menant à des systèmes d’apprentissage automatique plus sécuritaires et plus fiables.
À travers ces contributions, cette thèse avance la compréhension de la robustesse adversariale et propose des solutions pratiques pour améliorer la sécurité et la fiabilité des systèmes d'apprentissage automatique. En abordant les limites des méthodes actuelles d'entraînement adversarial et en introduisant des approches innovatrices comme l'AFD et l'incorporation de données synthétisées, cette recherche ouvre le chemin à des modèles d'apprentissage automatique plus robustes et généralisables.This thesis addresses the critical issue of adversarial vulnerability in deep learning models, which are susceptible to slight, human-imperceptible perturbations that can lead to incorrect predictions. Adversarial attacks pose significant threats to the deployment of these models in safety-critical systems. To mitigate these threats, adversarial training has emerged as a prominent approach, where models are trained on adversarial examples to enhance their robustness.
In Chapter 1, we provide a comprehensive background on adversarial vulnerability, detailing the creation of adversarial examples and their real-world implications. We illustrate how adversarial examples are crafted and present various scenarios demonstrating their potential catastrophic outcomes. Furthermore, we explore the challenges associated with adversarial training, focusing on issues like the lack of robustness against a broad range of attack strengths and a trade-off between robustness and generalization, which are the subjects of our study.
Chapter 2 introduces Adversarial Feature Desensitization (AFD), a novel method that leverages domain adaptation techniques to enhance adversarial robustness. AFD aims to learn features that are invariant to adversarial perturbations, thereby improving resilience across various attack types and strengths. This approach involves training a domain discriminator alongside the classifier to reduce the divergence between natural and adversarial data representations. By aligning the features from both domains, AFD ensures that the learned features are both predictive and robust, mitigating overfitting to specific attack patterns and promoting broader defensive capability.
Chapter 3 presents Adversarial Training with Synthesized Data, a method aimed at bridging the gap between robustness and generalization in neural networks. By leveraging synthesized data generated through advanced techniques, this chapter explores how incorporating such data can mitigate robust overfitting and enhance the overall performance of adversarially trained models. The findings indicate that while adversarial training traditionally faces a trade-off between robustness and generalization, the use of synthesized data helps maintain high accuracy on corrupted and out-of-distribution data without compromising robustness. This approach provides a promising pathway to develop neural networks that are both resilient to adversarial attacks and capable of generalizing well to a wide range of scenarios.
Chapter 4 concludes the thesis by summarizing the key findings and contributions of this thesis. Additionally, it outlines several avenues for future research to further enhance the security and reliability of deep learning models. Future research could explore the effect of synthesized data on a broader range of generalization tasks, develop alternative approaches to adversarial training that are less computationally expensive, and adapt new feedback-guided techniques for synthesizing data to enhance sample efficiency. By pursuing these directions, future research can build on the foundations laid by this thesis and continue to advance the field of adversarial robustness, ultimately leading to safer and more reliable machine learning systems.
Through these contributions, this thesis advances the understanding of adversarial robustness and proposes practical solutions to enhance the security and reliability of machine learning systems. By addressing the limitations of current adversarial training methods and introducing innovative approaches like AFD and the incorporation of synthesized data, this research paves the way for more robust and generalizable machine learning models capable of withstanding a diverse array of adversarial attacks
Commonality and Singularity in Working Memory Network Predicting Performance and Individual Differences
Get PDFWorking memory (WM) is considered a core element of cognition, acting as a shared resource among many different mental processes. It plays a key role in determining individual cognitive capacity and performance limits. Despite remarkable progress, prior works fall short in identifying which network structure in the brain limits our working memory capacity. We investigated this issue by analyzing electrical activity of human brain recorded from the scalp (Electroencephalogram or EEG). In this dissertation, first we describe a novel method for constructing a graph model of human brain activities. The nodes and edges of the graph represent cortices and interconnecting links, respectively. Next, we present a multivariate machine learning approach for identifying cognitive states and estimating network structure explaining individual differences. Finally, we describe our proposed approach that preserves the spatial-spectral-temporal information and capable of learning representation from EEG recording eliminating the need of feature engineering and reliance on domain knowledge.Main contributions of this dissertation are (but are not limited to): 1) study and characterization of common oscillatory neural response to varying levels of memory demand (load); 2) Identification of singular cortical structure explaining differences in WM capacity across individuals; and 3) Development of a robust representation learning approach for multi-dimensional time-series data. Our findings on identifying commonality and singularity in cortical structures are critical for designing cognitively informed brain-computer interfaces and long-term goal of designing neural prosthetics. On the other hand, our proposed representation learning method opens up new possibilities for data driven decoding of brain activities which is particularly capable in generalizing across individuals
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe 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
Get PDF“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
Get PDFWe 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
Get PDFWe 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
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