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Zero-shot learning for language translation – Complete Phd and Masters Thesis

[ad_1] Introduction Zero-shot learning is a popular topic in the field of machine learning and natural language processing that aims to enable machines to perform tasks without the need for labeled training data. In the…

Transfer learning for cross-modal retrieval – Complete Phd and Masters Thesis

[ad_1] Introduction Transfer learning has emerged as a powerful technique in machine learning, where knowledge learned from one domain is transferred to another related domain to improve performance. In the context of cross-modal retrieval, transfer…

Quantum key distribution for secure cloud storage – Complete Phd and Masters Thesis

[ad_1] Introduction Quantum key distribution (QKD) has emerged as a promising technology for achieving secure communication in the era of cloud storage. With the increasing reliance on cloud services for storing sensitive information, the need…

Homomorphic encryption for privacy-preserving deep learning – Complete Phd and Masters Thesis

[ad_1] Introduction With the increasing use of deep learning in various industries, there is a growing concern about the privacy of sensitive data that is used to train these models. Homomorphic encryption has emerged as…

Zero-knowledge proofs for privacy-preserving data aggregation – Complete Phd and Masters Thesis

[ad_1] Introduction Zero-knowledge proofs have emerged as a powerful tool in cryptography for ensuring the privacy and security of data during aggregation processes. In the era of big data, preserving the privacy of individuals’ sensitive…

Reinforcement learning for robotic manipulation – Complete Phd and Masters Thesis

[ad_1] Introduction In recent years, robotic manipulation has gained significant attention due to its potential applications in various industries such as manufacturing, healthcare, and agriculture. Reinforcement learning, a type of machine learning algorithm, has shown…

Zero-knowledge succinct non-interactive arguments of knowledge (zk-SNARKs) for verifiable federated learning – Complete Phd and Masters Thesis

[ad_1] Introduction Zero-knowledge succinct non-interactive arguments of knowledge (zk-SNARKs) have emerged as a powerful cryptographic tool for ensuring privacy and security in various applications. In the context of federated learning, where multiple parties collaborate to…

Federated transfer learning for cross-device federated learning – Complete Phd and Masters Thesis

[ad_1] Introduction In the era of big data and connected devices, federated learning has emerged as a promising approach to collaborative machine learning without compromising data privacy. Federated transfer learning extends the concept of federated…

Differential privacy for privacy-preserving machine learning – Complete Phd and Masters Thesis

[ad_1] Introduction Privacy-preserving machine learning has become a critical research area in the era of big data and artificial intelligence. As organizations collect and analyze vast amounts of sensitive data for various purposes, the need…

Quantum error correction for quantum sensing – Complete Phd and Masters Thesis

[ad_1] Introduction Quantum error correction (QEC) is a vital aspect of quantum computing and quantum sensing technologies. As quantum systems are extremely susceptible to errors caused by environmental noise and decoherence, the development of efficient…

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