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
Triple-Encoders: Representations That Fire Together, Wire Together
Justus-Jonas Erker, Florian Mai, Nils Reimers, Gerasimos Spanakis, Iryna Gurevych
Interpretable Brain-Inspired Representations Improve RL Performance on Visual Navigation Tasks
Moritz Lange, Raphael C. Engelhardt, Wolfgang Konen, Laurenz Wiskott
DGNN: Decoupled Graph Neural Networks with Structural Consistency between Attribute and Graph Embedding Representations
Jinlu Wang, Jipeng Guo, Yanfeng Sun, Junbin Gao, Shaofan Wang, Yachao Yang, Baocai Yin
Intriguing Equivalence Structures of the Embedding Space of Vision Transformers
Shaeke Salman, Md Montasir Bin Shams, Xiuwen Liu