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Calibrate your listeners! Robust communication-based training for pragmatic speakers.

R. Wang, J. White, J. Mu, и N. Goodman. EMNLP (Findings), стр. 977-984. Association for Computational Linguistics, (2021)

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Hans Noah

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Learning to Learn Causal Models.C. Kemp, N. Goodman, и J. Tenenbaum. Cogn. Sci., 34 (7): 1185-1243 (2010)Applying Probabilistic Programming to Affective Computing.D. Ong, H. Soh, J. Zaki, и N. Goodman. CoRR, (2019)Pyro: Deep Universal Probabilistic Programming.E. Bingham, J. Chen, M. Jankowiak, F. Obermeyer, N. Pradhan, T. Karaletsos, R. Singh, P. Szerlip, P. Horsfall, и N. Goodman. CoRR, (2018)An Incremental Iterated Response Model of Pragmatics.R. Cohn-Gordon, N. Goodman, и C. Potts. CoRR, (2018)Pyro: Deep Universal Probabilistic Programming.E. Bingham, J. Chen, M. Jankowiak, F. Obermeyer, N. Pradhan, T. Karaletsos, R. Singh, P. Szerlip, P. Horsfall, и N. Goodman. J. Mach. Learn. Res., (2019)Bayesian Policy Search with Policy Priors.D. Wingate, N. Goodman, D. Roy, L. Kaelbling, и J. Tenenbaum. IJCAI, стр. 1565-1570. IJCAI/AAAI, (2011)Generating Efficient MCMC Kernels from Probabilistic Programs.L. Yang, P. Hanrahan, и N. Goodman. AISTATS, том 33 из JMLR Workshop and Conference Proceedings, стр. 1068-1076. JMLR.org, (2014)Variational Item Response Theory: Fast, Accurate, and Expressive.M. Wu, R. Davis, B. Domingue, C. Piech, и N. Goodman. EDM, International Educational Data Mining Society, (2020)The principles and practice of probabilistic programming.N. Goodman. POPL, стр. 399-402. ACM, (2013)Causal Distillation for Language Models.Z. Wu, A. Geiger, J. Rozner, E. Kreiss, H. Lu, T. Icard, C. Potts, и N. Goodman. NAACL-HLT, стр. 4288-4295. Association for Computational Linguistics, (2022)

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