Scalable Neural

Scalable neural networks aim to overcome limitations of traditional deep learning models by enabling efficient training and inference on increasingly large datasets and complex architectures. Current research focuses on optimizing model architectures (e.g., Transformers, ConvNeXts, graph neural networks) and training algorithms (e.g., distributed optimization methods, knowledge distillation) to improve performance and scalability across diverse hardware platforms, including specialized AI chips and CPU clusters. These advancements are crucial for addressing challenges in various fields, such as natural language processing, medical image analysis, and high-energy physics, where massive datasets and computationally intensive models are essential for achieving state-of-the-art results. The development of efficient and scalable neural networks is driving progress in both fundamental research and practical applications.