[ad_1] Introduction: Numerical optimization plays a crucial role in large-scale machine learning, as it allows us to efficiently optimize complex models and algorithms used in data analysis and prediction. In this thesis, we will explore…
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[ad_1] Introduction: Graph embedding techniques have gained popularity in the field of molecular data analysis due to their ability to capture complex relationships and patterns within molecular structures. These techniques involve transforming molecular data into…
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[ad_1] Introduction: Domain generalization is a critical component of robust machine learning, allowing models to perform well on unseen data from different domains. In this thesis, we will explore the concept of domain generalization and…
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[ad_1] Introduction: Reinforcement learning is a type of machine learning that enables an agent to learn how to behave in an environment by performing actions and receiving rewards. It has gained significant attention in recent…
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[ad_1] Introduction: Spatio-temporal data analysis plays a crucial role in climate modeling as it allows researchers to understand the complex relationships between various environmental factors over both space and time. This type of analysis is…
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[ad_1] Introduction: Tensor decomposition is a powerful technique used in high-dimensional data analysis to extract meaningful patterns and relationships from complex datasets. By decomposing a tensor into a set of simpler components, researchers can gain…
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[ad_1] Introduction: Fairness and bias mitigation in machine learning is a critical topic in the field of artificial intelligence and data science. As machine learning models become more prevalent in decision-making processes across various industries,…
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[ad_1] Introduction: In recent years, artificial intelligence (AI) has become increasingly integrated into various aspects of our daily lives, from healthcare and finance to autonomous vehicles and social media. However, as AI systems become more…
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[ad_1] Introduction: Multimodal learning is a growing field in machine learning that focuses on integrating information from multiple modalities to improve the performance of learning systems. Multimodal data fusion refers to the process of combining…
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[ad_1] Introduction: Imbalanced data handling is a crucial aspect of data analysis, particularly in scenarios where rare events need to be detected. Rare event detection involves identifying events that occur infrequently in a dataset, but…
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