Hybrid Learning

Hybrid learning combines different learning paradigms, such as supervised and unsupervised methods, or model-based and model-free approaches, to leverage the strengths of each for improved performance and efficiency. Current research focuses on applications across diverse fields, including robotics, image processing, and education, employing techniques like neural networks (e.g., convolutional neural networks, LSTMs, Transformers), reinforcement learning (e.g., Soft Actor-Critic), and graph-based methods to enhance model accuracy and data efficiency. This interdisciplinary approach holds significant promise for advancing various scientific domains and practical applications by enabling more robust, adaptable, and interpretable systems.