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Bayesian neural networks for uncertainty estimation – Complete Phd and Masters Thesis

[ad_1] Introduction Bayesian neural networks (BNNs) have gained significant attention in recent years due to their ability to provide uncertainty estimates in neural network models. This is crucial in various applications such as medical diagnosis,…

Siamese networks for similarity learning – Complete Phd and Masters Thesis

[ad_1] **Introduction** Siamese networks have gained significant attention in recent years for their ability to learn similarity between pairs of inputs in a variety of domains such as image recognition, natural language processing, and recommendation…

Inception networks for multi-scale feature extraction – Complete Phd and Masters Thesis

[ad_1] Introduction: In recent years, deep learning has revolutionized the field of computer vision by achieving unprecedented levels of accuracy in various tasks, such as image classification, object detection, and segmentation. One key component of…

Convolutional neural networks for grid-like data – Complete Phd and Masters Thesis

[ad_1] Introduction Convolutional neural networks (CNNs) have gained significant importance in recent years for their ability to effectively extract and learn features from grid-like data such as images, videos, and sensor data. This thesis focuses…

Memory networks for long-term dependencies – Complete Phd and Masters Thesis

[ad_1] Introduction Memory networks have become a popular research topic in the field of machine learning and artificial intelligence due to their ability to capture and store long-term dependencies in sequential data. Traditional neural networks…

Normalizing flows for flexible density estimation – Complete Phd and Masters Thesis

[ad_1] Introduction Normalizing flows have emerged as a powerful tool for flexible density estimation in recent years. These methods allow for the modeling of complex, multi-modal distributions by transforming a simple base distribution into the…

Generative adversarial networks for realistic synthesis – Complete Phd and Masters Thesis

[ad_1] Introduction: Generative adversarial networks (GANs) have emerged as powerful tools for generating realistic synthetic data in recent years. By pitting two neural networks against each other in a zero-sum game setting, GANs are able…

Game-theoretic learning for strategic interaction – Complete Phd and Masters Thesis

[ad_1] Introduction Game theory is a branch of applied mathematics that examines strategic interactions between rational decision-makers. Through the use of mathematical models, game theory seeks to understand the strategies that players use to maximize…

Smart contracts for automated machine learning – Complete Phd and Masters Thesis

[ad_1] Introduction Smart contracts have gained popularity in recent years due to their ability to automate and facilitate secure transactions on blockchain platforms. In the field of machine learning, smart contracts can be used to…

Secure enclaves for trusted execution – Complete Phd and Masters Thesis

[ad_1] Introduction: Secure enclaves for trusted execution have gained significant attention in recent years due to the growing concerns about the security and privacy of sensitive data in cloud computing environments. Secure enclaves, also known…

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