Research authors: Jonathan Mamou (Intel Labs), Hang Le (MIT), Miguel A. Del Rio (MIT), Cory Stephenson (Intel Labs), Hanlin Tang (Intel Labs), Yoon Kim (Harvard University), SueYeon Chung (MIT and Columbia University)
Abstract: Deep neural networks (DNNs) have shown much empirical success in solving perceptual tasks across various cognitive modalities. Researchers utilize mean-field theoretic manifold analysis, a recent technique from computational neuroscience that connects geometry of feature representations with linear separability of classes, to analyze language representations from large-scale contextual embedding models. They explore representations from different model families and find evidence for emergence of linguistic manifolds across layer depth, especially in ambiguous data. In addition, they find that the emergence of linear separability in these manifolds is driven by a combined reduction of manifolds’ radius, dimensionality, and inter-manifold correlations.