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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…

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…

Explainable deep learning for financial forecasting – Complete Phd and Masters Thesis

[ad_1] Introduction Deep learning has gained significant attention in recent years due to its ability to effectively model complex relationships in data and make accurate predictions. In the field of finance, deep learning has shown…

Quantum-resistant homomorphic encryption – Complete Phd and Masters Thesis

[ad_1] Introduction With the rise of quantum computing technology, current encryption methods are at risk of being compromised, leading to potential security breaches. In response to this threat, quantum-resistant cryptography has emerged as a promising…

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…

Quantum machine learning for quantum error correction – Complete Phd and Masters Thesis

[ad_1] Introduction Quantum computing is a rapidly evolving field that has the potential to revolutionize the way we process and analyze data. Quantum machine learning is an emerging area of research that combines principles of…

Multi-modal learning for emotion recognition – Complete Phd and Masters Thesis

[ad_1] Introduction Emotion recognition is a crucial aspect of human interaction and communication. Being able to accurately identify and understand the emotions of others can lead to better relationships and more effective communication. In recent…

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…

Federated reinforcement learning for intelligent transportation systems – Complete Phd and Masters Thesis

[ad_1] Introduction With the rapid advancement of technology, intelligent transportation systems (ITS) have become a crucial component in optimizing traffic flow, reducing congestion, and improving overall transportation efficiency. Traditional reinforcement learning algorithms have been used…

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