Noah D. Goodman

See: Stochastic Lambda Calculus, Bayesian Cognitive Model, Pragmatic Language Understanding, Probabilistic Programming Language, Causal Inference, Theory of Mind.

References

(Bingham et al., 2019) ⇒ Eli Bingham, Jonathan P. Chen, Martin Jankowiak, Fritz Obermeyer, Neeraj Pradhan, and others. (2019). "Pyro: Deep Universal Probabilistic Programming." In: Journal of Machine Learning Research, Volume 20, Pages 1-6. [1](http://jmlr.org/papers/v20/18-403.html)
- NOTE: It describes Pyro's capabilities in probabilistic programming, emphasizing its flexibility and scalability in developing complex models.

(Frank & Goodman, 2012) ⇒ Michael C. Frank and Noah D. Goodman. (2012). "Predicting Pragmatic Reasoning in Language Games." In: Science, Volume 336, Page 998. [doi:10.1126/science.1220374](https://doi.org/10.1126/science.1220374)
- NOTE: It highlights the role of pragmatic reasoning in understanding and generating language within interactive contexts.
(Goodman, 2012). => Noah D. Goodman. (2012). http://www.nasslli2012.com/courses/stochastic-lambda-calculus

(Tenenbaum et al., 2011) ⇒ Joshua B. Tenenbaum, Charles Kemp, Thomas L. Griffiths, and Noah D. Goodman. (2011). “How to Grow a Mind: Statistics, Structure, and Abstraction.” In: Science, 331(6022). doi:10.1126/science.1192788
- NOTE: It provides a framework for understanding cognitive development through statistical learning and abstract reasoning.
(Bonawitz et al., 2011) ⇒ Elizabeth Bonawitz, Patrick Shafto, Hyowon Gweon, Noah D. Goodman, Elizabeth Spelke, and Laura Schulz. (2011). "The Double-Edged Sword of Pedagogy: Instruction Limits Spontaneous Exploration and Discovery." In: Cognition, Volume 120, Issue 3, Pages 322-330. [doi:10.1016/j.cognition.2010.10.001](https://doi.org/10.1016/j.cognition.2010.10.001)
- NOTE: It discusses the implications of pedagogical approaches on the balance between guided learning and spontaneous exploration.

(Goodman et al., 2008) ⇒ Noah D. Goodman, Vikash Mansinghka, Daniel Roy, Kevin Bonawitz, and Joshua B. Tenenbaum. (2008). "Church: A Language for Generative Models." In: Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence (UAI). Pages Not Provided. [2](https://dl.acm.org/doi/10.5555/3023476.3023496)
- NOTE: It introduces the Church programming language, which facilitates the specification and inference of generative models in a probabilistic framework.