Mutual Information Loss

Mutual information loss (MIL) quantifies the information lost when compressing data, a crucial consideration in machine learning models that learn compressed representations (e.g., encoder-decoder architectures). Current research focuses on improving MIL estimation within contrastive learning frameworks, particularly for recommendation systems and unsupervised representation learning, and exploring its application in diverse areas like spike sorting and zero-shot learning. Understanding and minimizing MIL is vital for enhancing the efficiency and performance of these models, leading to improved accuracy and reduced computational costs in various applications.

Papers