[ad_1] Introduction: Deep learning has shown significant promise in the field of medical image segmentation, a critical task in medical image analysis for disease diagnosis and treatment planning. Medical image segmentation involves partitioning an image…
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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. In recent years, research has focused on improving collaborative filtering for sequential recommendation,…
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[ad_1] Introduction: Bayesian Deep Learning has emerged as a powerful tool for uncertainty quantification in machine learning models. By incorporating Bayesian principles into deep learning algorithms, researchers can not only make more accurate predictions but…
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[ad_1] Introduction: Distributed representation learning for multimodal data is a cutting-edge research area that aims to develop efficient and effective algorithms for extracting meaningful representations from data that combine information from multiple modalities, such as…
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[ad_1] Introduction: Multi-Agent Reinforcement Learning (MARL) has gained significant attention in recent years due to its ability to enable autonomous agents to learn and adapt in dynamic and complex environments. In the field of swarm…
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[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: With the advancements in genomic research, there is a growing need for sharing genomic data among researchers and institutions. However, ensuring the privacy and confidentiality of this sensitive data poses a significant challenge.…
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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: 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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