Meaningful Representation
Meaningful representation in machine learning aims to create data encodings that are both informative and computationally efficient, facilitating better model interpretability, controllability, and transferability across tasks and domains. Current research emphasizes disentangled representations, often achieved using techniques like variational autoencoders and contrastive learning, and focuses on developing robust metrics to evaluate the quality of these representations, particularly in complex data like multimodal healthcare data and 3D structures. These advancements are crucial for improving the performance and reliability of AI systems across diverse applications, from medical diagnosis to materials science and natural language processing.
Papers
Learning disentangled representations for explainable chest X-ray classification using Dirichlet VAEs
Rachael Harkness, Alejandro F Frangi, Kieran Zucker, Nishant Ravikumar
The SSL Interplay: Augmentations, Inductive Bias, and Generalization
Vivien Cabannes, Bobak T. Kiani, Randall Balestriero, Yann LeCun, Alberto Bietti