Probabilistic Modeling
probabilistic modeling
Dr. Tatsuo Okubo
We are a new group at the Chinese Institute for Brain Research (CIBR), Beijing, which focuses on using modern data science and machine learning tools on neuroscience data. We collaborate with various labs within CIBR to develop models and analysis pipelines to accelerate neuroscience research. We are looking for enthusiastic and talented machine learning engineers and data scientists to join this effort. Example projects include (but not limited to) extracting hidden states from population neural activity, automating behavioral classification from videos, and segmenting neurons from confocal images using deep learning.
Theory-driven probabilistic modeling of language use: a case study on quantifiers, logic and typicality
Theoretical linguistics postulates abstract structures that successfully explain key aspects of language. However, the precise relation between abstract theoretical ideas and empirical data from language use is not always apparent. Here, we propose to empirically test abstract semantic theories through the lens of probabilistic pragmatic modelling. We consider the historically important case of quantity words (e.g., `some', `all'). Data from a large-scale production study seem to suggest that quantity words are understood via prototypes. But based on statistical and empirical model comparison, we show that a probabilistic pragmatic model that embeds a strict truth-conditional notion of meaning explains the data just as well as a model that encodes prototypes into the meaning of quantity words.