[ad_1] Introduction Autoregressive models have gained significant attention in recent years due to their ability to generate realistic sequences of data, such as text, audio, and images. These models have been successful in various applications,…
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[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…
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[ad_1] **Introduction** Swarm intelligence is a field of study that focuses on the collective behavior of decentralized, self-organizing systems. It draws inspiration from the behavior of social insects such as ants, bees, and termites, which…
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[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…
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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 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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[ad_1] Introduction Non-convex optimization is a challenging field in mathematics and computer science that deals with finding the optimal solutions for problems that do not have convex objective functions. Local minima are a common issue…
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[ad_1] Introduction Collaborative filtering is a popular technique used in recommendation systems to provide personalized suggestions to users based on their preferences and behaviors. With the increasing amount of information available online, the need for…
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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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