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{ "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/" } ] }