Are deep MLP and convolution networks specialized for processing a sequential grid of values?

Deep Multi-Layer Perceptrons (MLPs) and convolutional neural networks (CNNs) are both powerful models used in machine learning, but they are designed for different types of data processing. While CNNs are especially adept at processing grid-like data structures such as images, MLPs do not have inherent specializations for sequential or grid data. MLPs treat input features independently and are better suited for tabular data or structured data rather than sequential information.

Convolutional networks utilize local connectivity and weight sharing, making them highly effective at capturing spatial hierarchies in images, where data resides in a two-dimensional grid format. They apply filters that slide across the input to capture patterns and reduce complexities for image analysis.

However, as a general rule, neither MLPs nor convolutional networks are specialized for sequential data or grid structures in a traditional sense because recurrent neural networks (RNNs) are more specifically designed to handle data sequences effectively, such as time series or natural language processing tasks. Hence, stating that they are not specialized accurately reflects their capabilities relative to the processing of sequential grid values.