{"pk":28135,"title":"Example Generation Under Constraints Using Cascade Correlation Neural Nets","subtitle":null,"abstract":"Humans not only can effortlessly imagine a wide range ofnovel instances and scenarios when prompted (e.g., a newshirt), but more remarkably, they can adequately generate ex-amples which satisfy a given set of constraints (e.g., a new,dotted, pink shirt). Recently, Nobandegani and Shultz (2017)proposed a framework which permits converting deterministic,discriminative neural nets into probabilistic generative models.In this work, we formally show that an extension of this frame-work allows for generating examples under a wide range ofconstraints. Furthermore, we show that this framework is con-sistent with developmental findings on children’s generativeabilities, and can account for a developmental shift in infants’probabilistic learning and reasoning. We discuss the impor-tance of integrating Bayesian and connectionist approaches tocomputational developmental psychology, and how our workcontributes to that research.","language":"eng","license":{"name":"","short_name":"","text":null,"url":""},"keywords":[{"word":"Cascade correlation neural networks; Determin-istic discriminative models; Probabilistic generative models;Bayesian vs. connectionist modeling of development"}],"section":"Publication-based-Talks","is_remote":true,"remote_url":"https://escholarship.org/uc/item/9f71755r","frozenauthors":[{"first_name":"Ardavan","middle_name":"S","last_name":"Nobandegani","name_suffix":"","institution":"McGill","department":""},{"first_name":"Thomas","middle_name":"R","last_name":"Schultz","name_suffix":"","institution":"McGill","department":""}],"date_submitted":null,"date_accepted":null,"date_published":"2018-01-01T18:00:00Z","render_galley":null,"galleys":[{"label":"PDF","type":"pdf","path":"https://journalpub.escholarship.org/cognitivesciencesociety/article/28135/galley/17794/download/"}]}