Neural Network Architecture

Neural network architecture research focuses on designing efficient and effective network structures for various machine learning tasks. Current efforts concentrate on automating architecture design through techniques like evolutionary algorithms and neural architecture search (NAS), exploring diverse architectures such as transformers, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and their hybrid combinations, often tailored for specific data types and hardware constraints. This field is crucial for advancing machine learning capabilities across diverse applications, from image recognition and natural language processing to scientific modeling and medical diagnosis, by improving model accuracy, efficiency, and fairness.