Representational Capacity

Representational capacity refers to the ability of a model or system to capture and effectively utilize information from data. Current research focuses on improving this capacity in various architectures, including recurrent and transformer neural networks, implicit neural representations (INRs), and graph neural networks (GNNs), often by addressing limitations like spectral bias, codebook collapse, and the trade-off between stability and expressiveness. These advancements aim to enhance the performance of machine learning models across diverse applications, from natural language processing and image reconstruction to multi-agent reinforcement learning and biomedical image analysis. A key theme is understanding how different model designs and training methods impact the ability to represent complex data structures and relationships.