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Long short-term memory networks for sequence modeling – Complete Phd and Masters Thesis

[ad_1] Introduction Long short-term memory (LSTM) networks have gained significant attention in the field of neural networks due to their ability to model complex sequences and learn long-term dependencies. These networks have been successfully applied…

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…

Variational autoencoders for latent representation – Complete Phd and Masters Thesis

[ad_1] Introduction In recent years, deep learning models have shown impressive performance in various tasks such as image recognition, natural language processing, and speech recognition. Variational autoencoders (VAEs) are a type of generative model that…

Adversarial learning for robust models – Complete Phd and Masters Thesis

[ad_1] Introduction: In recent years, there has been a growing interest in developing robust machine learning models that can perform well in the presence of adversarial attacks. These attacks are designed to exploit vulnerabilities in…

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…

Multi-agent reinforcement learning for coordination – Complete Phd and Masters Thesis

[ad_1] Introduction Multi-agent reinforcement learning (MARL) is a subfield of artificial intelligence that focuses on developing algorithms and techniques for coordinating multiple autonomous agents to achieve a common goal. The coordination of multiple agents presents…

Blockchain for decentralized machine learning – Complete Phd and Masters Thesis

[ad_1] Introduction Blockchain technology has gained significant attention in recent years due to its potential to revolutionize various industries by providing decentralized, secure, and transparent systems. One area that stands to benefit greatly from the…

Encrypted computation for confidential processing – Complete Phd and Masters Thesis

[ad_1] Introduction In recent times, with the rapid advancements in technology and the widespread use of cloud computing, the need for secure and confidential data processing has become more important than ever. Encrypted computation is…

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…

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