Abstract
The article discusses the use of meta-learning for automatic design of optimal neural network architectures. An approach based on two-level optimization is proposed, where meta-learning is used to search for universal parameters and strategies that accelerate the process of generating effective architectures. It is shown that meta-learning reduces computational costs and provides high adaptability to new problems, which makes it preferable to traditional methods such as manual design and network architecture search (NAS). Experimental results confirming the advantage of the approach are presented, and the main limitations are identified, including the requirements for computational resources and challenges in generalizing to new problems. Promising directions of development are considered, including integration with transformers, hybrid methods, and adaptation for specialized areas such as medicine and finance. The proposed approach contributes to the acceleration of the development of neural networks, improving their performance and availability for application in a wide range of problems.
Published Version
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