[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…
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[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…
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[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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[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…
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[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…
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[ad_1] Introduction Convex optimization is a powerful mathematical tool that has been widely used in various fields such as machine learning, signal processing, control systems, and operations research. It involves the optimization of convex objective…
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[ad_1] Introduction Tensor factorization is a powerful technique used in multi-way data analysis to decompose high-dimensional tensors into a set of lower-dimensional factors. It has gained popularity in various fields such as signal processing, image…
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[ad_1] Introduction Dictionary learning is a powerful technique used in machine learning and signal processing for basis discovery. It involves the process of learning a dictionary that can effectively represent a set of data samples…
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[ad_1] Introduction Manifold learning is a powerful technique used in machine learning and data analysis for dimensionality reduction. It aims to uncover the underlying structure of high-dimensional data by representing it in a lower-dimensional space…
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[ad_1] Introduction: In the field of machine learning and pattern recognition, the measurement of similarity between data points is a crucial task with implications in various applications such as image retrieval, recommendation systems, and text…
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