Representation Bottleneck

Representation bottlenecks in machine learning refer to limitations in a model's ability to effectively capture and utilize the relevant information from input data, often due to insufficient representational capacity or inefficient information encoding. Current research focuses on mitigating these bottlenecks through architectural innovations like compute-in-memory architectures and bottleneck-enhanced autoencoders, as well as algorithmic improvements such as knowledge-priors and multi-task learning strategies. Addressing representation bottlenecks is crucial for improving the efficiency, accuracy, and generalizability of machine learning models across diverse applications, from natural language processing and image analysis to network optimization and quantum computing.