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ORCIDNoah D. Goodman
Person information
- affiliation: Stanford University, Department of Psychology, USA
- affiliation: Massachusetts Institute of Technology, Department of Brain and Cognitive Sciences, USA
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2020 – today
- 2023
[c129]Rose E. Wang, Pawan Wirawarn, Noah D. Goodman, Dorottya Demszky:
SIGHT: A Large Annotated Dataset on Student Insights Gathered from Higher Education Transcripts. BEA@ACL 2023: 315-351- [c128]Jasmine Bayrooti, Noah D. Goodman, Alex Tamkin:
Multispectral Contrastive Learning with Viewmaker Networks. CVPR Workshops 2023: 440-448 - [c127]Alex Tamkin, Kunal Handa, Avash Shrestha, Noah D. Goodman:
Task Ambiguity in Humans and Language Models. ICLR 2023 - [c126]Megha Srivastava, Noah D. Goodman, Dorsa Sadigh:
Generating Language Corrections for Teaching Physical Control Tasks. ICML 2023: 32561-32574 - [i108]Jasmine Bayrooti, Noah D. Goodman, Alex Tamkin:
Multispectral Self-Supervised Learning with Viewmaker Networks. CoRR abs/2302.05757 (2023) - [i107]Atticus Geiger, Zhengxuan Wu, Christopher Potts, Thomas Icard, Noah D. Goodman:
Finding Alignments Between Interpretable Causal Variables and Distributed Neural Representations. CoRR abs/2303.02536 (2023) - [i106]Ben Prystawski, Noah D. Goodman:
Why think step-by-step? Reasoning emerges from the locality of experience. CoRR abs/2304.03843 (2023) - [i105]Jesse Mu, Xiang Lisa Li, Noah D. Goodman:
Learning to Compress Prompts with Gist Tokens. CoRR abs/2304.08467 (2023) - [i104]Joy He-Yueya, Gabriel Poesia, Rose E. Wang, Noah D. Goodman:
Solving Math Word Problems by Combining Language Models With Symbolic Solvers. CoRR abs/2304.09102 (2023) - [i103]Dilip Arumugam, Mark K. Ho, Noah D. Goodman, Benjamin Van Roy:
Bayesian Reinforcement Learning with Limited Cognitive Load. CoRR abs/2305.03263 (2023) - [i102]Polina Tsvilodub, Michael Franke, Robert D. Hawkins, Noah D. Goodman:
Overinformative Question Answering by Humans and Machines. CoRR abs/2305.07151 (2023) - [i101]Zhengxuan Wu, Atticus Geiger, Christopher Potts, Noah D. Goodman:
Interpretability at Scale: Identifying Causal Mechanisms in Alpaca. CoRR abs/2305.08809 (2023) - [i100]Dhara Yu, Noah D. Goodman, Jesse Mu:
Characterizing tradeoffs between teaching via language and demonstrations in multi-agent systems. CoRR abs/2305.11374 (2023) - [i99]Kanishk Gandhi, Dorsa Sadigh, Noah D. Goodman:
Strategic Reasoning with Language Models. CoRR abs/2305.19165 (2023) - [i98]Gabriel Poesia, Kanishk Gandhi, Eric Zelikman, Noah D. Goodman:
Certified Reasoning with Language Models. CoRR abs/2306.04031 (2023) - [i97]Megha Srivastava, Noah D. Goodman, Dorsa Sadigh:
Generating Language Corrections for Teaching Physical Control Tasks. CoRR abs/2306.07012 (2023) - [i96]Rose E. Wang, Pawan Wirawarn, Noah D. Goodman, Dorottya Demszky
:
SIGHT: A Large Annotated Dataset on Student Insights Gathered from Higher Education Transcripts. CoRR abs/2306.09343 (2023) - [i95]Eric Zelikman, Qian Huang, Percy Liang, Nick Haber, Noah D. Goodman:
Just One Byte (per gradient): A Note on Low-Bandwidth Decentralized Language Model Finetuning Using Shared Randomness. CoRR abs/2306.10015 (2023) - [i94]Lionel Wong, Gabriel Grand, Alexander K. Lew, Noah D. Goodman, Vikash K. Mansinghka, Jacob Andreas, Joshua B. Tenenbaum:
From Word Models to World Models: Translating from Natural Language to the Probabilistic Language of Thought. CoRR abs/2306.12672 (2023) - [i93]Kanishk Gandhi, Jan-Philipp Fränken, Tobias Gerstenberg, Noah D. Goodman:
Understanding Social Reasoning in Language Models with Language Models. CoRR abs/2306.15448 (2023) - [i92]Ruocheng Wang, Eric Zelikman, Gabriel Poesia, Yewen Pu, Nick Haber, Noah D. Goodman:
Hypothesis Search: Inductive Reasoning with Language Models. CoRR abs/2309.05660 (2023) - [i91]Jiayuan Mao, Xuelin Yang, Xikun Zhang, Noah D. Goodman, Jiajun Wu:
CLEVRER-Humans: Describing Physical and Causal Events the Human Way. CoRR abs/2310.03635 (2023) - [i90]Belinda Z. Li, Alex Tamkin, Noah D. Goodman, Jacob Andreas:
Eliciting Human Preferences with Language Models. CoRR abs/2310.11589 (2023) - [i89]Alex Tamkin, Mohammad Taufeeque, Noah D. Goodman:
Codebook Features: Sparse and Discrete Interpretability for Neural Networks. CoRR abs/2310.17230 (2023) - [i88]Jan-Philipp Fränken, Sam Kwok, Peixuan Ye, Kanishk Gandhi, Dilip Arumugam, Jared Moore, Alex Tamkin, Tobias Gerstenberg, Noah D. Goodman:
Social Contract AI: Aligning AI Assistants with Implicit Group Norms. CoRR abs/2310.17769 (2023) - 2022
- [j24]Michael Henry Tessler, Noah D. Goodman:
Warm (for Winter): Inferring Comparison Classes in Communication. Cogn. Sci. 46(3) (2022) - [j23]Michael Henry Tessler, Joshua B. Tenenbaum, Noah D. Goodman:
Logic, Probability, and Pragmatics in Syllogistic Reasoning. Top. Cogn. Sci. 14(3): 574-601 (2022) - [c125]Julia White, Noah D. Goodman, Robert X. D. Hawkins:
Mixed-effects transformers for hierarchical adaptation. EMNLP 2022: 3944-3954 - [c124]Elisa Kreiss, Fei Fang, Noah D. Goodman, Christopher Potts:
Concadia: Towards Image-Based Text Generation with a Purpose. EMNLP 2022: 4667-4684 - [c123]Rose E. Wang, Esin Durmus, Noah D. Goodman, Tatsunori Hashimoto:
Language modeling via stochastic processes. ICLR 2022 - [c122]Atticus Geiger, Zhengxuan Wu, Hanson Lu, Josh Rozner, Elisa Kreiss, Thomas Icard, Noah D. Goodman, Christopher Potts:
Inducing Causal Structure for Interpretable Neural Networks. ICML 2022: 7324-7338 - [c121]Zhengxuan Wu, Atticus Geiger, Joshua Rozner, Elisa Kreiss, Hanson Lu, Thomas Icard, Christopher Potts, Noah D. Goodman:
Causal Distillation for Language Models. NAACL-HLT 2022: 4288-4295 - [c120]Joy Hsu, Jiajun Wu, Noah D. Goodman:
Geoclidean: Few-Shot Generalization in Euclidean Geometry. NeurIPS 2022 - [c119]Jiayuan Mao, Xuelin Yang, Xikun Zhang, Noah D. Goodman, Jiajun Wu:
CLEVRER-Humans: Describing Physical and Causal Events the Human Way. NeurIPS 2022 - [c118]Jesse Mu, Victor Zhong, Roberta Raileanu, Minqi Jiang, Noah D. Goodman, Tim Rocktäschel, Edward Grefenstette:
Improving Intrinsic Exploration with Language Abstractions. NeurIPS 2022 - [c117]Megha Srivastava, Erdem Biyik, Suvir Mirchandani, Noah D. Goodman, Dorsa Sadigh:
Assistive Teaching of Motor Control Tasks to Humans. NeurIPS 2022 - [c116]Alex Tamkin, Gaurab Banerjee, Mohamed Owda, Vincent Liu, Shashank Rammoorthy, Noah D. Goodman:
DABS 2.0: Improved Datasets and Algorithms for Universal Self-Supervision. NeurIPS 2022 - [c115]Alex Tamkin, Dat Nguyen, Salil Deshpande, Jesse Mu, Noah D. Goodman:
Active Learning Helps Pretrained Models Learn the Intended Task. NeurIPS 2022 - [c114]Mike Wu, Noah D. Goodman:
Foundation Posteriors for Approximate Probabilistic Inference. NeurIPS 2022 - [c113]Eric Zelikman, Yuhuai Wu, Jesse Mu, Noah D. Goodman:
STaR: Bootstrapping Reasoning With Reasoning. NeurIPS 2022 - [i87]Jesse Mu, Victor Zhong, Roberta Raileanu, Minqi Jiang, Noah D. Goodman, Tim Rocktäschel, Edward Grefenstette:
Improving Intrinsic Exploration with Language Abstractions. CoRR abs/2202.08938 (2022) - [i86]Rose E. Wang, Esin Durmus, Noah D. Goodman, Tatsunori Hashimoto:
Language modeling via stochastic processes. CoRR abs/2203.11370 (2022) - [i85]Eric Zelikman, Yuhuai Wu, Noah D. Goodman:
STaR: Bootstrapping Reasoning With Reasoning. CoRR abs/2203.14465 (2022) - [i84]Alex Tamkin, Dat Nguyen, Salil Deshpande, Jesse Mu, Noah D. Goodman:
Active Learning Helps Pretrained Models Learn the Intended Task. CoRR abs/2204.08491 (2022) - [i83]Rose E. Wang, Mike Wu, Noah D. Goodman:
Know Thy Student: Interactive Learning with Gaussian Processes. CoRR abs/2204.12072 (2022) - [i82]Julia White, Noah D. Goodman, Robert X. D. Hawkins:
Mixed-effects transformers for hierarchical adaptation. CoRR abs/2205.01749 (2022) - [i81]Fei Fang, Kunal Sinha, Noah D. Goodman, Christopher Potts, Elisa Kreiss:
Color Overmodification Emerges from Data-Driven Learning and Pragmatic Reasoning. CoRR abs/2205.09172 (2022) - [i80]Mike Wu, Noah D. Goodman:
Foundation Posteriors for Approximate Probabilistic Inference. CoRR abs/2205.09735 (2022) - [i79]Ben Prystawski, Paul H. Thibodeau, Noah D. Goodman:
Psychologically-informed chain-of-thought prompts for metaphor understanding in large language models. CoRR abs/2209.08141 (2022) - [i78]Dilip Arumugam, Mark K. Ho, Noah D. Goodman, Benjamin Van Roy:
On Rate-Distortion Theory in Capacity-Limited Cognition & Reinforcement Learning. CoRR abs/2210.16877 (2022) - [i77]Zhening Li, Gabriel Poesia, Omar Costilla-Reyes, Noah D. Goodman, Armando Solar-Lezama:
LEMMA: Bootstrapping High-Level Mathematical Reasoning with Learned Symbolic Abstractions. CoRR abs/2211.08671 (2022) - [i76]Megha Srivastava, Erdem Biyik, Suvir Mirchandani, Noah D. Goodman, Dorsa Sadigh:
Assistive Teaching of Motor Control Tasks to Humans. CoRR abs/2211.14003 (2022) - [i75]Gabriel Poesia, Noah D. Goodman:
Peano: Learning Formal Mathematical Reasoning. CoRR abs/2211.15864 (2022) - [i74]Joy Hsu, Jiajun Wu, Noah D. Goodman:
Geoclidean: Few-Shot Generalization in Euclidean Geometry. CoRR abs/2211.16663 (2022) - [i73]Robert D. Hawkins, Andrew M. Berdahl, Alex 'Sandy' Pentland, Joshua B. Tenenbaum, Noah D. Goodman, P. M. Krafft:
Flexible social inference facilitates targeted social learning when rewards are not observable. CoRR abs/2212.00869 (2022) - [i72]Alex Tamkin, Margalit Glasgow, Xiluo He, Noah D. Goodman:
Feature Dropout: Revisiting the Role of Augmentations in Contrastive Learning. CoRR abs/2212.08378 (2022) - [i71]Eric Zelikman, Qian Huang, Gabriel Poesia, Noah D. Goodman, Nick Haber:
Parsel: A Unified Natural Language Framework for Algorithmic Reasoning. CoRR abs/2212.10561 (2022) - [i70]Alex Tamkin, Kunal Handa, Avash Shrestha, Noah D. Goodman:
Task Ambiguity in Humans and Language Models. CoRR abs/2212.10711 (2022) - 2021
- [j22]Robert X. D. Hawkins, Hyowon Gweon, Noah D. Goodman:
The Division of Labor in Communication: Speakers Help Listeners Account for Asymmetries in Visual Perspective. Cogn. Sci. 45(3) (2021) - [j21]Shyamal Buch, Li Fei-Fei, Noah D. Goodman:
Neural Event Semantics for Grounded Language Understanding. Trans. Assoc. Comput. Linguistics 9: 875-890 (2021) - [j20]Desmond C. Ong, Harold Soh, Jamil Zaki, Noah D. Goodman:
Applying Probabilistic Programming to Affective Computing. IEEE Trans. Affect. Comput. 12(2): 306-317 (2021) - [c112]Gabriel Poesia, Noah D. Goodman:
Pragmatic Code Autocomplete. AAAI 2021: 445-452 - [c111]Megha Srivastava, Noah D. Goodman:
Question Generation for Adaptive Education. ACL/IJCNLP (2) 2021: 692-701 - [c110]Ali Malik, Mike Wu, Vrinda Vasavada, Jinpeng Song, Madison Coots, John Mitchell, Noah D. Goodman, Chris Piech:
Generative Grading: Near Human-level Accuracy for Automated Feedback on Richly Structured Problems. EDM 2021 - [c109]Julia White, Gabriel Poesia, Robert X. D. Hawkins, Dorsa Sadigh, Noah D. Goodman:
Open-domain clarification question generation without question examples. EMNLP (1) 2021: 563-570 - [c108]Rose E. Wang, Julia White, Jesse Mu, Noah D. Goodman:
Calibrate your listeners! Robust communication-based training for pragmatic speakers. EMNLP (Findings) 2021: 977-984 - [c107]Alex Tamkin, Mike Wu, Noah D. Goodman:
Viewmaker Networks: Learning Views for Unsupervised Representation Learning. ICLR 2021 - [c106]Mike Wu, Milan Mosse, Chengxu Zhuang, Daniel Yamins, Noah D. Goodman:
Conditional Negative Sampling for Contrastive Learning of Visual Representations. ICLR 2021 - [c105]Gabriel Poesia, Wenxin Dong, Noah D. Goodman:
Contrastive Reinforcement Learning of Symbolic Reasoning Domains. NeurIPS 2021: 15946-15956 - [c104]Jesse Mu, Noah D. Goodman:
Emergent Communication of Generalizations. NeurIPS 2021: 17994-18007 - [c103]Alex Tamkin, Vincent Liu, Rongfei Lu, Daniel Fein, Colin Schultz, Noah D. Goodman:
DABS: a Domain-Agnostic Benchmark for Self-Supervised Learning. NeurIPS Datasets and Benchmarks 2021 - [c102]Mike Wu, Noah D. Goodman, Stefano Ermon:
Improving Compositionality of Neural Networks by Decoding Representations to Inputs. NeurIPS 2021: 26689-26700 - [i69]Robert X. D. Hawkins, Michael Franke, Michael C. Frank, Kenny Smith, Thomas L. Griffiths, Noah D. Goodman:
From partners to populations: A hierarchical Bayesian account of coordination and convention. CoRR abs/2104.05857 (2021) - [i68]Elisa Kreiss, Noah D. Goodman, Christopher Potts:
Concadia: Tackling image accessibility with context. CoRR abs/2104.08376 (2021) - [i67]Mike Wu, Noah D. Goodman, Stefano Ermon:
Improving Compositionality of Neural Networks by Decoding Representations to Inputs. CoRR abs/2106.00769 (2021) - [i66]Jesse Mu, Noah D. Goodman:
Emergent Communication of Generalizations. CoRR abs/2106.02668 (2021) - [i65]Megha Srivastava, Noah D. Goodman:
Question Generation for Adaptive Education. CoRR abs/2106.04262 (2021) - [i64]Gabriel Poesia, Wenxin Dong, Noah D. Goodman:
Contrastive Reinforcement Learning of Symbolic Reasoning Domains. CoRR abs/2106.09146 (2021) - [i63]Mike Wu, Noah D. Goodman, Chris Piech, Chelsea Finn:
ProtoTransformer: A Meta-Learning Approach to Providing Student Feedback. CoRR abs/2107.14035 (2021) - [i62]Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, Russ B. Altman, Simran Arora, Sydney von Arx, Michael S. Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, Erik Brynjolfsson, Shyamal Buch, Dallas Card, Rodrigo Castellon, Niladri S. Chatterji, Annie S. Chen, Kathleen Creel, Jared Quincy Davis, Dorottya Demszky, Chris Donahue, Moussa Doumbouya, Esin Durmus, Stefano Ermon, John Etchemendy, Kawin Ethayarajh, Li Fei-Fei, Chelsea Finn, Trevor Gale, Lauren Gillespie, Karan Goel, Noah D. Goodman, Shelby Grossman, Neel Guha, Tatsunori Hashimoto, Peter Henderson, John Hewitt, Daniel E. Ho, Jenny Hong, Kyle Hsu, Jing Huang, Thomas Icard, Saahil Jain, Dan Jurafsky, Pratyusha Kalluri, Siddharth Karamcheti, Geoff Keeling, Fereshte Khani, Omar Khattab, Pang Wei Koh, Mark S. Krass, Ranjay Krishna, Rohith Kuditipudi, et al.:
On the Opportunities and Risks of Foundation Models. CoRR abs/2108.07258 (2021) - [i61]Mike Wu, Richard Lee Davis, Benjamin W. Domingue, Chris Piech, Noah D. Goodman:
Modeling Item Response Theory with Stochastic Variational Inference. CoRR abs/2108.11579 (2021) - [i60]Robert D. Hawkins, Megumi Sano, Noah D. Goodman, Judith E. Fan:
Visual resemblance and communicative context constrain the emergence of graphical conventions. CoRR abs/2109.13861 (2021) - [i59]Oliver Zhang, Mike Wu, Jasmine Bayrooti, Noah D. Goodman:
Temperature as Uncertainty in Contrastive Learning. CoRR abs/2110.04403 (2021) - [i58]Rose E. Wang, Julia White, Jesse Mu, Noah D. Goodman:
Calibrate your listeners! Robust communication-based training for pragmatic speakers. CoRR abs/2110.05422 (2021) - [i57]Julia White, Gabriel Poesia, Robert X. D. Hawkins, Dorsa Sadigh, Noah D. Goodman:
Open-domain clarification question generation without question examples. CoRR abs/2110.09779 (2021) - [i56]Alex Tamkin, Vincent Liu, Rongfei Lu, Daniel Fein, Colin Schultz, Noah D. Goodman:
DABS: A Domain-Agnostic Benchmark for Self-Supervised Learning. CoRR abs/2111.12062 (2021) - [i55]Atticus Geiger, Zhengxuan Wu, Hanson Lu, Josh Rozner, Elisa Kreiss, Thomas Icard, Noah D. Goodman, Christopher Potts:
Inducing Causal Structure for Interpretable Neural Networks. CoRR abs/2112.00826 (2021) - [i54]Zhengxuan Wu, Atticus Geiger, Josh Rozner, Elisa Kreiss, Hanson Lu, Thomas Icard, Christopher Potts, Noah D. Goodman:
Causal Distillation for Language Models. CoRR abs/2112.02505 (2021) - [i53]Ananya Karthik, Mike Wu, Noah D. Goodman, Alex Tamkin:
Tradeoffs Between Contrastive and Supervised Learning: An Empirical Study. CoRR abs/2112.05340 (2021) - 2020
- [j19]Robert X. D. Hawkins, Michael C. Frank, Noah D. Goodman:
Characterizing the Dynamics of Learning in Repeated Reference Games. Cogn. Sci. 44(6) (2020) - [j18]Ishita Dasgupta, Demi Guo, Samuel J. Gershman, Noah D. Goodman:
Analyzing Machine-Learned Representations: A Natural Language Case Study. Cogn. Sci. 44(12) (2020) - [j17]Benjamin N. Peloquin, Noah D. Goodman, Michael C. Frank:
The Interactions of Rational, Pragmatic Agents Lead to Efficient Language Structure and Use. Top. Cogn. Sci. 12(1): 433-445 (2020) - [c101]Mike Wu, Kristy Choi, Noah D. Goodman, Stefano Ermon:
Meta-Amortized Variational Inference and Learning. AAAI 2020: 6404-6412 - [c100]Jesse Mu, Percy Liang, Noah D. Goodman:
Shaping Visual Representations with Language for Few-Shot Classification. ACL 2020: 4823-4830 - [c99]Robert X. D. Hawkins, Noah D. Goodman, Adele E. Goldberg, Tom Griffiths:
Generalizing meanings from partners to populations: Hierarchical inference supports convention formation on networks. CogSci 2020 - [c98]Julia White, Jesse Mu, Noah D. Goodman:
Learning to refer informatively by amortizing pragmatic reasoning. CogSci 2020 - [c97]Robert X. D. Hawkins, Minae Kwon, Dorsa Sadigh, Noah D. Goodman:
Continual Adaptation for Efficient Machine Communication. CoNLL 2020: 408-419 - [c96]Mike Wu, Richard Lee Davis, Benjamin W. Domingue, Chris Piech, Noah D. Goodman:
Variational Item Response Theory: Fast, Accurate, and Expressive. EDM 2020 - [c95]Alex Tamkin, Trisha Singh, Davide Giovanardi, Noah D. Goodman:
Investigating Transferability in Pretrained Language Models. EMNLP (Findings) 2020: 1393-1401 - [c94]Alex Tamkin, Dan Jurafsky, Noah D. Goodman:
Language Through a Prism: A Spectral Approach for Multiscale Language Representations. NeurIPS 2020 - [i52]Mike Wu, Richard Lee Davis, Benjamin W. Domingue, Chris Piech, Noah D. Goodman:
Variational Item Response Theory: Fast, Accurate, and Expressive. CoRR abs/2002.00276 (2020) - [i51]Robert X. D. Hawkins, Noah D. Goodman, Adele E. Goldberg, Thomas L. Griffiths:
Generalizing meanings from partners to populations: Hierarchical inference supports convention formation on networks. CoRR abs/2002.01510 (2020) - [i50]Alex Tamkin, Trisha Singh, Davide Giovanardi, Noah D. Goodman:
Investigating Transferability in Pretrained Language Models. CoRR abs/2004.14975 (2020) - [i49]Mike Wu, Chengxu Zhuang, Milan Mosse, Daniel Yamins, Noah D. Goodman:
On Mutual Information in Contrastive Learning for Visual Representations. CoRR abs/2005.13149 (2020) - [i48]Julia White, Jesse Mu, Noah D. Goodman:
Learning to refer informatively by amortizing pragmatic reasoning. CoRR abs/2006.00418 (2020) - [i47]Mike Wu, Milan Mosse, Chengxu Zhuang, Daniel Yamins, Noah D. Goodman:
Conditional Negative Sampling for Contrastive Learning of Visual Representations. CoRR abs/2010.02037 (2020) - [i46]Mike Wu, Noah D. Goodman:
A Simple Framework for Uncertainty in Contrastive Learning. CoRR abs/2010.02038 (2020) - [i45]Alex Tamkin, Mike Wu, Noah D. Goodman:
Viewmaker Networks: Learning Views for Unsupervised Representation Learning. CoRR abs/2010.07432 (2020) - [i44]Alex Tamkin, Dan Jurafsky, Noah D. Goodman:
Language Through a Prism: A Spectral Approach for Multiscale Language Representations. CoRR abs/2011.04823 (2020)
2010 – 2019
- 2019
- [j16]Eli Bingham, Jonathan P. Chen, Martin Jankowiak, Fritz Obermeyer, Neeraj Pradhan, Theofanis Karaletsos, Rohit Singh, Paul A. Szerlip, Paul Horsfall, Noah D. Goodman:
Pyro: Deep Universal Probabilistic Programming. J. Mach. Learn. Res. 20: 28:1-28:6 (2019) - [j15]Desmond C. Ong, Jamil Zaki, Noah D. Goodman:
Computational Models of Emotion Inference in Theory of Mind: A Review and Roadmap. Top. Cogn. Sci. 11(2): 338-357 (2019) - [c93]Mike Wu, Milan Mosse, Noah D. Goodman, Chris Piech:
Zero Shot Learning for Code Education: Rubric Sampling with Deep Learning Inference. AAAI 2019: 782-790 - [c92]Bill McDowell, Noah D. Goodman:
Learning from Omission. ACL (1) 2019: 619-628 - [c91]Allen Nie, Erin Bennett, Noah D. Goodman:
DisSent: Learning Sentence Representations from Explicit Discourse Relations. ACL (1) 2019: 4497-4510 - [c90]Mike Wu, Noah D. Goodman, Stefano Ermon:
Differentiable Antithetic Sampling for Variance Reduction in Stochastic Variational Inference. AISTATS 2019: 2877-2886 - [c89]Emmanuel M. Pothos, Jerome R. Busemeyer, Timothy J. Pleskac, James M. Yearsley, Josh Tenenbaum, Noah D. Goodman, Michael Henry Tessler, Tom Griffiths, Falk Lieder, Ralph Hertwig, Thorsten Pachur, Christina Leuker, Richard M. Shiffrin:
Extending Rationality. CogSci 2019: 39-40 - [c88]Sahil Chopra, Michael Henry Tessler, Noah D. Goodman:
The first crank of the cultural ratchet: Learning and transmitting concepts through language. CogSci 2019: 226-232 - [c87]Robert X. D. Hawkins, Megumi Sano, Noah D. Goodman, Judith W. Fan:
Disentangling contributions of visual information and interaction history in the formation of graphical conventions. CogSci 2019: 415-421 - [c86]Benjamin N. Peloquin, Noah D. Goodman, Michael C. Frank:
The interactions of rational, pragmatic agents lead to efficient language structure and use. CogSci 2019: 912-917 - [c85]Panos Achlioptas, Leonidas J. Guibas, Noah D. Goodman, Judy Fan, Robert X. D. Hawkins:
Shapeglot: Learning Language for Shape Differentiation. ICCV 2019: 8937-8946 - [c84]Fritz Obermeyer, Eli Bingham, Martin Jankowiak, Neeraj Pradhan, Justin T. Chiu, Alexander M. Rush, Noah D. Goodman:
Tensor Variable Elimination for Plated Factor Graphs. ICML 2019: 4871-4880 - [c83]