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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,…

Triplet networks for comparative learning – Complete Phd and Masters Thesis

[ad_1] Introduction Triplet networks have gained popularity in recent years for their ability to learn effective representations for various tasks, particularly in the field of comparative learning. In a triplet network, the model is trained…

Recurrent neural networks for temporal data – Complete Phd and Masters Thesis

[ad_1] Introduction: Recurrent neural networks (RNNs) have gained significant attention in recent years due to their ability to effectively model sequential data. In particular, RNNs are well-suited for analyzing temporal data, where there is a…

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…

Decentralized AI for distributed intelligence – Complete Phd and Masters Thesis

[ad_1] Introduction: In recent years, artificial intelligence (AI) has played a significant role in revolutionizing various industries by enabling machines to perform tasks that traditionally required human intelligence. However, the centralized nature of traditional AI…

Secure aggregation for distributed learning – Complete Phd and Masters Thesis

[ad_1] Introduction Secure aggregation for distributed learning is a critical component in the field of machine learning and data privacy. With the increasing amount of data being collected and processed in various applications, the need…

Federated analytics for decentralized learning – Complete Phd and Masters Thesis

[ad_1] Introduction Federated analytics is a novel approach to decentralized learning that involves training machine learning models across multiple edge devices or servers while keeping the data on these devices rather than centralizing it. This…

Secure multi-party computation for collaborative learning – Complete Phd and Masters Thesis

[ad_1] Introduction The increasing demand for data privacy and security in collaborative learning environments has led to the development of secure multi-party computation (MPC) techniques. These techniques allow multiple parties to jointly compute a function…

Privacy-preserving machine learning for secure computation – Complete Phd and Masters Thesis

[ad_1] Introduction Privacy-preserving machine learning for secure computation is a rapidly growing field in computer science and data privacy. With the increasing amount of sensitive data being collected and analyzed, ensuring the privacy and security…

Stochastic optimization for noisy objectives – Complete Phd and Masters Thesis

[ad_1] Introduction: Stochastic optimization is a powerful tool used in various fields such as machine learning, operations research, and engineering to find optimal solutions in the presence of uncertainty. In many real-world scenarios, the objectives…

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