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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
- 2024
[j25]Gabriel Poesia, Kanishk Gandhi, Eric Zelikman, Noah D. Goodman:
Certified Deductive Reasoning with Language Models. Trans. Mach. Learn. Res. 2024 (2024)- [c152]Atticus Geiger, Zhengxuan Wu, Christopher Potts, Thomas Icard, Noah D. Goodman:
Finding Alignments Between Interpretable Causal Variables and Distributed Neural Representations. CLeaR 2024: 160-187 - [c151]Rose E. Wang, Pawan Wirawarn, Omar Khattab, Noah D. Goodman, Dorottya Demszky:
Backtracing: Retrieving the Cause of the Query. EACL (Findings) 2024: 722-735 - [c150]Joy He-Yueya, Noah D. Goodman, Emma Brunskill:
Evaluating and Optimizing Educational Content with Large Language Model Judgments. EDM 2024 - [c149]Steven Y. Feng, Noah D. Goodman, Michael Frank:
Is Child-Directed Speech Effective Training Data for Language Models? EMNLP 2024: 22055-22071 - [c148]Ruocheng Wang, Eric Zelikman, Gabriel Poesia, Yewen Pu, Nick Haber, Noah D. Goodman:
Hypothesis Search: Inductive Reasoning with Language Models. ICLR 2024 - [c147]Michael Y. Li, Emily B. Fox, Noah D. Goodman:
Automated Statistical Model Discovery with Language Models. ICML 2024 - [c146]Alex Tamkin, Mohammad Taufeeque, Noah D. Goodman:
Codebook Features: Sparse and Discrete Interpretability for Neural Networks. ICML 2024 - [c145]Zhengxuan Wu, Atticus Geiger, Aryaman Arora, Jing Huang
, Zheng Wang, Noah D. Goodman, Christopher D. Manning, Christopher Potts:
pyvene: A Library for Understanding and Improving PyTorch Models via Interventions. NAACL (Demonstrations) 2024: 158-165 - [i127]Zhengxuan Wu, Atticus Geiger, Jing Huang, Aryaman Arora, Thomas Icard, Christopher Potts, Noah D. Goodman:
A Reply to Makelov et al. (2023)'s "Interpretability Illusion" Arguments. CoRR abs/2401.12631 (2024) - [i126]Michael Y. Li, Emily B. Fox, Noah D. Goodman:
Automated Statistical Model Discovery with Language Models. CoRR abs/2402.17879 (2024) - [i125]Joy He-Yueya, Noah D. Goodman, Emma Brunskill:
Evaluating and Optimizing Educational Content with Large Language Model Judgments. CoRR abs/2403.02795 (2024) - [i124]Rose E. Wang, Pawan Wirawarn, Omar Khattab, Noah D. Goodman, Dorottya Demszky:
Backtracing: Retrieving the Cause of the Query. CoRR abs/2403.03956 (2024) - [i123]Kunal Handa, Yarin Gal, Ellie Pavlick, Noah D. Goodman, Jacob Andreas, Alex Tamkin, Belinda Z. Li:
Bayesian Preference Elicitation with Language Models. CoRR abs/2403.05534 (2024) - [i122]Zhengxuan Wu, Atticus Geiger, Aryaman Arora, Jing Huang, Zheng Wang, Noah D. Goodman, Christopher D. Manning, Christopher Potts:
pyvene: A Library for Understanding and Improving PyTorch Models via Interventions. CoRR abs/2403.07809 (2024) - [i121]Eric Zelikman, Georges Harik, Yijia Shao, Varuna Jayasiri, Nick Haber, Noah D. Goodman:
Quiet-STaR: Language Models Can Teach Themselves to Think Before Speaking. CoRR abs/2403.09629 (2024) - [i120]Chinmaya Andukuri, Jan-Philipp Fränken, Tobias Gerstenberg, Noah D. Goodman:
STaR-GATE: Teaching Language Models to Ask Clarifying Questions. CoRR abs/2403.19154 (2024) - [i119]Kanishk Gandhi, Denise Lee, Gabriel Grand, Muxin Liu, Winson Cheng, Archit Sharma, Noah D. Goodman:
Stream of Search (SoS): Learning to Search in Language. CoRR abs/2404.03683 (2024) - [i118]Jan-Philipp Fränken, Kanishk Gandhi, Tori Qiu, Ayesha Khawaja, Noah D. Goodman, Tobias Gerstenberg:
Procedural Dilemma Generation for Evaluating Moral Reasoning in Humans and Language Models. CoRR abs/2404.10975 (2024) - [i117]Jan-Philipp Fränken, Eric Zelikman, Rafael Rafailov, Kanishk Gandhi, Tobias Gerstenberg, Noah D. Goodman:
Self-Supervised Alignment with Mutual Information: Learning to Follow Principles without Preference Labels. CoRR abs/2404.14313 (2024) - [i116]Gabriel Poesia, David Broman, Nick Haber, Noah D. Goodman:
Learning Formal Mathematics From Intrinsic Motivation. CoRR abs/2407.00695 (2024) - [i115]Shubhra Mishra, Gabriel Poesia, Belinda Mo, Noah D. Goodman:
MathCAMPS: Fine-grained Synthesis of Mathematical Problems From Human Curricula. CoRR abs/2407.00900 (2024) - [i114]Zachary Kenton, Noah Y. Siegel, János Kramár, Jonah Brown-Cohen, Samuel Albanie, Jannis Bulian, Rishabh Agarwal, David Lindner, Yunhao Tang, Noah D. Goodman, Rohin Shah:
On scalable oversight with weak LLMs judging strong LLMs. CoRR abs/2407.04622 (2024) - [i113]Joy He-Yueya, Wanjing Anya Ma, Kanishk Gandhi, Benjamin W. Domingue, Emma Brunskill, Noah D. Goodman:
Psychometric Alignment: Capturing Human Knowledge Distributions via Language Models. CoRR abs/2407.15645 (2024) - [i112]Steven Y. Feng, Noah D. Goodman, Michael C. Frank:
Is Child-Directed Speech Effective Training Data for Language Models? CoRR abs/2408.03617 (2024) - [i111]Joy Hsu, Jiayuan Mao, Joshua B. Tenenbaum, Noah D. Goodman, Jiajun Wu:
What Makes a Maze Look Like a Maze? CoRR abs/2409.08202 (2024) - [i110]Kanishk Gandhi, Zoe Lynch, Jan-Philipp Fränken, Kayla Patterson, Sharon Wambu, Tobias Gerstenberg, Desmond C. Ong, Noah D. Goodman:
Human-like Affective Cognition in Foundation Models. CoRR abs/2409.11733 (2024) - [i109]Aryaman Arora, Dan Jurafsky, Christopher Potts, Noah D. Goodman:
Bayesian scaling laws for in-context learning. CoRR abs/2410.16531 (2024) - 2023
- [c144]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 - [c143]Ben Prystawski, Dilip Arumugam, Noah D. Goodman:
Cultural reinforcement learning: a framework for modeling cumulative culture on a limited channel. CogSci 2023 - [c142]Ben Prystawski, Paul H. Thibodeau, Christopher Potts, Noah D. Goodman:
Psychologically-informed chain-of-thought prompts for metaphor understanding in large language models. CogSci 2023 - [c141]Polina Tsvilodub, Michael Franke, Robert D. Hawkins, Noah D. Goodman:
Overinformative Question Answering by Humans and Machines. CogSci 2023 - [c140]Dhara Yu, Noah D. Goodman, Jesse Mu:
Characterizing tradeoffs between teaching via language and demonstrations in multi-agent systems. CogSci 2023 - [c139]Jasmine Bayrooti, Noah D. Goodman, Alex Tamkin:
Multispectral Contrastive Learning with Viewmaker Networks. CVPR Workshops 2023: 440-448 - [c138]Joy Hsu, Gabriel Poesia, Jiajun Wu, Noah D. Goodman:
Can Visual Scratchpads With Diagrammatic Abstractions Augment LLM Reasoning? ICBINB 2023: 21-28 - [c137]Alex Tamkin, Kunal Handa, Avash Shrestha, Noah D. Goodman:
Task Ambiguity in Humans and Language Models. ICLR 2023 - [c136]Megha Srivastava, Noah D. Goodman, Dorsa Sadigh:
Generating Language Corrections for Teaching Physical Control Tasks. ICML 2023: 32561-32574 - [c135]Kanishk Gandhi, Jan-Philipp Fränken, Tobias Gerstenberg, Noah D. Goodman:
Understanding Social Reasoning in Language Models with Language Models. NeurIPS 2023 - [c134]Jesse Mu, Xiang Li, Noah D. Goodman:
Learning to Compress Prompts with Gist Tokens. NeurIPS 2023 - [c133]Ben Prystawski, Michael Li, Noah D. Goodman:
Why think step by step? Reasoning emerges from the locality of experience. NeurIPS 2023 - [c132]Alex Tamkin, Margalit Glasgow, Xiluo He, Noah D. Goodman:
Feature Dropout: Revisiting the Role of Augmentations in Contrastive Learning. NeurIPS 2023 - [c131]Zhengxuan Wu, Atticus Geiger, Thomas Icard, Christopher Potts, Noah D. Goodman:
Interpretability at Scale: Identifying Causal Mechanisms in Alpaca. NeurIPS 2023 - [c130]Eric Zelikman, Qian Huang, Gabriel Poesia, Noah D. Goodman, Nick Haber:
Parsel🦆: Algorithmic Reasoning with Language Models by Composing Decompositions. NeurIPS 2023 - [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) - [c129]Julia White, Amy Burkhardt, Jason D. Yeatman, Noah D. Goodman:
Automated generation of sentence reading fluency test items. CogSci 2022 - [c128]Fei Fang, Kunal Sinha, Noah D. Goodman, Christopher Potts, Elisa Kreiss:
Color Overmodification Emerges from Data-Driven Learning and Pragmatic Reasoning. CogSci 2022 - [c127]Veronica Boyce, Robert D. Hawkins, Noah D. Goodman, Michael C. Frank:
Two's company but six is a crowd: emergence of conventions in multiparty communication games. CogSci 2022 - [c126]Gabriel Poesia Reis e Silva, Noah D. Goodman:
Left to the Reader: Abstracting Solutions in Mathematical Reasoning. CogSci 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]