Sam Gershman (continuation of previous talk), & Josh Tenenbaum: Bayesian Inference

Sam Gershman (continuation of previous talk), & Josh Tenenbaum: Bayesian Inference

Topics: (Sam Gershman) Application of Bayesian learning to motion perception; automatic structure learning
(Joshua Tenenbaum) Learning to learn: hierarchical Bayes; empirical studies of word learning and the relevant object features; transfer of concepts to real-world vocabulary learning; inductive biases; learning about feature variability; hierarchical Bayesian model that accounts for empirical observations (Kemp, Perfors, Tenenbaum, Developmental Science 2007; Salakhutdinov, Tenenbaum, Torralba, ICML 2010); learning structural forms with hierarchical Bayes (Kemp, Tenenbaum, PNAS 2008); learning the form of matrix decompositions (Grosse, Salakhutdinov, Freeman, Tenenbaum, UAI 2012))