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Multi-modal learning for cross-modal fusion – Complete Phd and Masters Thesis

[ad_1] Introduction Multi-modal learning, a subfield of machine learning, has gained significant attention in recent years due to its ability to integrate information from multiple modalities such as text, images, and audio. Cross-modal fusion, on…

Inverse reinforcement learning for reward estimation – Complete Phd and Masters Thesis

[ad_1] Introduction Inverse reinforcement learning (IRL) is a subfield of machine learning that is concerned with inferring a reward function based on observed behavior. Unlike traditional reinforcement learning, where an agent learns a policy by…

Imitation learning for behavior cloning – Complete Phd and Masters Thesis

[ad_1] Introduction Imitation learning, also known as behavioral cloning, is a machine learning technique that involves learning a policy from demonstrations provided by an expert. This approach is particularly useful in settings where designing a…

Active learning for efficient data labeling – Complete Phd and Masters Thesis

[ad_1] Introduction In recent years, the volume of data generated in various fields such as healthcare, finance, and social media has exponentially increased. This massive amount of data requires efficient labeling to make it usable…

Self-supervised learning for unlabeled data – Complete Phd and Masters Thesis

[ad_1] Introduction Self-supervised learning has emerged as a promising technique in the field of machine learning, especially for tasks where labeled data is scarce or expensive to obtain. This approach aims to leverage the inherent…

Zero-shot learning for unseen classes – Complete Phd and Masters Thesis

[ad_1] Introduction Zero-shot learning is a promising technique in machine learning, where the model is trained on a set of classes but is able to generalize to unseen classes at test time. This approach is…

Reinforcement learning for adaptive decision-making – Complete Phd and Masters Thesis

[ad_1] Introduction Reinforcement learning is a subfield of machine learning that focuses on enabling agents to make sequential decisions in order to maximize rewards. It has gained significant attention in recent years due to its…

Probabilistic graphical models for inference – Complete Phd and Masters Thesis

[ad_1] Introduction: Probabilistic graphical models are powerful tools for representing and reasoning about uncertainty in complex systems. These models combine principles from probability theory and graph theory to capture the dependencies between variables in a…

Neural networks for pattern recognition – Complete Phd and Masters Thesis

[ad_1] Introduction Neural networks have emerged as a powerful tool for pattern recognition in recent years. This technology has been widely applied in various fields such as image recognition, speech recognition, and natural language processing.…

Evolutionary computation for optimization – Complete Phd and Masters Thesis

[ad_1] Introduction Evolutionary computation is a powerful optimization technique inspired by the process of natural selection. This method involves generating potential solutions to a problem and then using genetic operators such as mutation, crossover, and…

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