Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 26 for “"Adversarial learning."”.
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Generative modelling and adversarial learning
A main goal of statistics and machine learning is to represent and manipulate high-dimensional probability distributions of real-world data, such as natural images. Generative adversarial networks (GAN), which are based on the adversarial learning paradigm, are one of the main types of methods for …
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MDEA : malware detection with evolutionary adversarial learning
Many applications have used machine learning as a tool to detect malware. These applications take in raw or processed binary data to feed neural network models to classify benign or malicious files. Even though this approach has proved effective against dynamic changes, such as encrypting, …
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Adversarial Learning through Red Teaming: From Data to Behaviour
… characteristics to military planning, such as adversarial learning, risk assessment, and behavioural decision making in a competitive environment. Computational red teaming is a recent approach that extends red teaming concept in the cyber space and benefits from replacing the physical red and …
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Detecting Irregular Network Activity with Adversarial Learning and Expert Feedback
… thesis proposes a novel self-supervised deep learning framework CAAD for anomaly detection in wireless communication systems. Specifically, CAAD employs powerful adversarial learning and contrastive learning techniques to learn effective representations of normal and anomalous behavior in …
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Mathematical Optimization Algorithms for Model Compression and Adversarial Learning in Deep Neural Networks
… issue of model size, DNNs are also sensitive to adversarial attacks, a small invisible noise on the input data can fully mislead a DNN. Research on the robustness of DNNs follows two directions in general. The first is to enhance the robustness of DNNs, which increases the degree of difficulty …
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Adversarial Learning based framework for Anomaly Detection in the context of Unmanned Aerial Systems
… ambiguous, unsupervised and semi-supervised deep learning (DL) algorithms that primarily use unlabeled datasets to model normal (regular) behaviors, are popularly studied in this context. The unmanned aerial system (UAS) can use contextual anomaly detection algorithms to identify interesting …
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Towards robust malware detection
… challenge of malware detection using machine learning methods is the presence of adversarial variants, small changes to detectable malware that allow it to evade a model (i.e. be classified as benign). We take inspiration from adversarial variant generation methods in the continuous-valued …
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Towards Better Representations with Deep/Bayesian Learning
<p>Deep learning and Bayesian Learning are two popular research topics in machine learning. They provide the flexible representations in the complementary manner. Therefore, it is desirable to take the best from both fields. This thesis focuses on the intersection of the two topics— enriching one …
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Privileged Machine Learning for Prediction
Machine learning for prediction suffers from asymmetric distribution, such as posterior information, future information and hidden information. With some additional information only available in training, how to learn a machine learning model with them remains a key challenge. Despite recent …
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Multi-Domain Text Classification with Adversarial Training
… mainstream MDTC approaches resort to transfer learning techniques to reduce domain divergence across different domains. In particular, these methods adopt adversarial training and shared-private paradigm to implement domain alignment, yielding state-of-the-art performance. Adversarial learning …
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Efficient Edge Intelligence in the Era of Big Data
… intelligent data compression. We leverage Deep Learning (DL), more specifically, Convolutional Autoencoder (CAE), to learn a sparse representation of the vital big data. The minimized energy need, even taking into consideration the CAE-induced overhead, is tremendously lower than the original …
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Towards Energy-Efficient Cloud Datacentres: A Unified Framework for Generative and Multi-Scale Time Series Forecasting
… a Transformer-Rectification-based Generative Adversarial Network that combines adversarial learning with attention-based sequence modelling to enhance long-horizon prediction accuracy. To further improve robustness and generalisability under volatile conditions, DAA-T-GAN is proposed, …
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Towards Robust Deep Neural Networks
… state-of-the-art performance for most machine learning tasks. Unfortunately, they are vulnerable to attacks, such as Trojans during training and Adversarial Examples at test time. Adversarial Examples are inputs with carefully crafted perturbations added to benign samples. In the Computer …
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Multi-agent Coordination Algorithms for Pursuit-Evasion
… and (5) how to get the arms race from the adversarial co-evolution. Accordingly, a safety-constrained multi-agent pursuit-evasion platform: MatrixWorld is proposed, and three coordination algorithms are designed, i.e., the cooperative coevolutionary particle swarm optimization (CCPSO-R) …
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Unsupervised Domain Adaptation per la rilevazione di oggetti e riconoscimento di azioni
… delle azioni. UDA è una tecnica di machine learning che mira a ridurre le differenze di distribuzione tra un dominio di origine (con dati etichettati) e un dominio di destinazione (con dati non etichettati). L'obiettivo principale è sviluppare un modello in grado di adattarsi a scenari …
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Discriminative and adaptive training for robust speech recognition and understanding
… problem even with the advances of deep learning. To achieve robust ASU, two discriminative training objectives are proposed for keyword spotting and topic classification: (1) To accurately recognize the semantically important keywords, the non-uniform error cost minimum classification …
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Efficient Numerical Algorithms for Structured Nonsmooth Min–Max and Adjustable Robust Optimization problems with Applications
… arises in important applications such as adversarial learning and robust optimization modeling for optimal radiotherapy. However, the challenge of solving them efficiently emerge from their inherent problem structure. Many practical problems in this area often exist in large scale …
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Deep neural networks for medical image super-resolution
… for natural images, I explore progressive learning, adversarial learning and meta-learning in end-to-end frameworks based on convolution neural networks, generative adversarial networks and vision transformers for robust medical image super-resolution. In addition to general image quality …
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Unsupervised Learning : Model-guided and Model-agnostic Approaches
Unsupervised learning is the branch of machine learning that is aimed at learning patterns from data without labels. Supervised learning with millions of labels for image classification had driven the modern deep learning revolution in the past few years. Deep neural networks have exceeded human …
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Generative models meet similarity search: efficient, heuristic-free and robust retrieval
… (ANN) search methods, especially the Learning-to-hash or Hashing methods, provide principled approaches that balance the trade-offs between the quality of the guesses and the computational cost for web-scale databases. In this era of data explosion, it is crucial for the hashing …
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