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ORCIDThomas L. Griffiths 0001
Tom Griffiths 0001
Person information
- affiliation: Princeton University, Department of Psychology, NJ, USA
- affiliation: University of California, Berkeley, Department of Psychology, USA
Other persons with the same name
- Thomas L. Griffiths
- Tom Griffiths 0002 — University of Edinburgh, UK
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2020 – today
- 2024
[j47]Stephan C. Meylan, Thomas L. Griffiths:
Word Forms Reflect Trade-Offs Between Speaker Effort and Robust Listener Recognition. Cogn. Sci. 48(7) (2024)- [j46]Evan M. Russek
, Frederick Callaway, Thomas L. Griffiths:
Inverting Cognitive Models With Neural Networks to Infer Preferences From Fixations. Cogn. Sci. 48(11) (2024) - [j45]Noga Alon, Jonathan D. Cohen, Thomas L. Griffiths, Pasin Manurangsi, Daniel Reichman, Igor Shinkar, Tal Wagner:
Erratum: Multitasking Capacity: Hardness Results and Improved Constructions. SIAM J. Discret. Math. 38(2): 2001-2003 (2024) - [j44]Theodore R. Sumers, Shunyu Yao, Karthik Narasimhan, Thomas L. Griffiths:
Cognitive Architectures for Language Agents. Trans. Mach. Learn. Res. 2024 (2024) - [c224]Ruiqi He, Carlos G. Correa, Tom Griffiths, Mark K. Ho:
Structurally Guided Task Decomposition in Spatial Navigation Tasks (Student Abstract). AAAI 2024: 23512-23513 - [c223]Maya Malaviya, Ilia Sucholutsky, Thomas L. Griffiths:
Pushing the Limits of Learning from Limited Data. AAAI Spring Symposia 2024: 559-561 - [c222]Bonan Zhao, Natalia Vélez, Thomas L. Griffiths:
Comparing Human Behavior to an Optimal Policy for Innovation. AAAI Spring Symposia 2024: 598-599 - [c221]Akshara Prabhakar, Thomas L. Griffiths, R. Thomas McCoy:
Deciphering the Factors Influencing the Efficacy of Chain-of-Thought: Probability, Memorization, and Noisy Reasoning. EMNLP (Findings) 2024: 3710-3724 - [c220]Andi Peng, Andreea Bobu, Belinda Z. Li, Theodore R. Sumers, Ilia Sucholutsky, Nishanth Kumar, Thomas L. Griffiths, Julie A. Shah:
Preference-Conditioned Language-Guided Abstraction. HRI 2024: 572-581 - [c219]Gianluca M. Bencomo, Jake Snell, Thomas L. Griffiths:
Implicit Maximum a Posteriori Filtering via Adaptive Optimization. ICLR 2024 - [c218]Andi Peng, Ilia Sucholutsky, Belinda Z. Li, Theodore R. Sumers, Thomas L. Griffiths, Jacob Andreas, Julie Shah:
Learning with Language-Guided State Abstractions. ICLR 2024 - [c217]Ryan Liu, Theodore R. Sumers, Ishita Dasgupta, Thomas L. Griffiths:
How do Large Language Models Navigate Conflicts between Honesty and Helpfulness? ICML 2024 - [c216]Yufei Tian, Abhilasha Ravichander, Lianhui Qin, Ronan Le Bras, Raja Marjieh, Nanyun Peng, Yejin Choi, Thomas L. Griffiths, Faeze Brahman:
MacGyver: Are Large Language Models Creative Problem Solvers? NAACL-HLT 2024: 5303-5324 - [i134]Sunayana Rane, Polyphony J. Bruna, Ilia Sucholutsky, Christopher T. Kello, Thomas L. Griffiths:
Concept Alignment. CoRR abs/2401.08672 (2024) - [i133]Jian-Qiao Zhu, Thomas L. Griffiths:
Incoherent Probability Judgments in Large Language Models. CoRR abs/2401.16646 (2024) - [i132]Jian-Qiao Zhu, Haijiang Yan, Thomas L. Griffiths:
Recovering Mental Representations from Large Language Models with Markov Chain Monte Carlo. CoRR abs/2401.16657 (2024) - [i131]Andi Peng, Andreea Bobu, Belinda Z. Li, Theodore R. Sumers, Ilia Sucholutsky, Nishanth Kumar, Thomas L. Griffiths, Julie A. Shah:
Preference-Conditioned Language-Guided Abstraction. CoRR abs/2402.03081 (2024) - [i130]Sreejan Kumar, Raja Marjieh, Byron Zhang, Declan Campbell, Michael Y. Hu, Umang Bhatt, Brenden M. Lake, Thomas L. Griffiths:
Comparing Abstraction in Humans and Large Language Models Using Multimodal Serial Reproduction. CoRR abs/2402.03618 (2024) - [i129]Xuechunzi Bai, Angelina Wang, Ilia Sucholutsky, Thomas L. Griffiths:
Measuring Implicit Bias in Explicitly Unbiased Large Language Models. CoRR abs/2402.04105 (2024) - [i128]Declan Campbell, Sreejan Kumar, Tyler Giallanza, Thomas L. Griffiths, Jonathan D. Cohen:
Human-Like Geometric Abstraction in Large Pre-trained Neural Networks. CoRR abs/2402.04203 (2024) - [i127]Raja Marjieh, Pol van Rijn, Ilia Sucholutsky, Harin Lee, Thomas L. Griffiths, Nori Jacoby:
A Rational Analysis of the Speech-to-Song Illusion. CoRR abs/2402.06992 (2024) - [i126]Ioana Marinescu, R. Thomas McCoy, Thomas L. Griffiths:
Distilling Symbolic Priors for Concept Learning into Neural Networks. CoRR abs/2402.07035 (2024) - [i125]Ryan Liu, Theodore R. Sumers, Ishita Dasgupta, Thomas L. Griffiths:
How do Large Language Models Navigate Conflicts between Honesty and Helpfulness? CoRR abs/2402.07282 (2024) - [i124]Carlos G. Correa, Thomas L. Griffiths, Nathaniel D. Daw:
Program-Based Strategy Induction for Reinforcement Learning. CoRR abs/2402.16668 (2024) - [i123]Andi Peng, Ilia Sucholutsky, Belinda Z. Li, Theodore R. Sumers, Thomas L. Griffiths, Jacob Andreas, Julie A. Shah:
Learning with Language-Guided State Abstractions. CoRR abs/2402.18759 (2024) - [i122]Xudong Guo, Kaixuan Huang, Jiale Liu, Wenhui Fan, Natalia Vélez, Qingyun Wu, Huazheng Wang, Thomas L. Griffiths, Mengdi Wang:
Embodied LLM Agents Learn to Cooperate in Organized Teams. CoRR abs/2403.12482 (2024) - [i121]Allison Chen, Ilia Sucholutsky, Olga Russakovsky, Thomas L. Griffiths:
Analyzing the Roles of Language and Vision in Learning from Limited Data. CoRR abs/2403.19669 (2024) - [i120]Jian-Qiao Zhu, Haijiang Yan, Thomas L. Griffiths:
Language Models Trained to do Arithmetic Predict Human Risky and Intertemporal Choice. CoRR abs/2405.19313 (2024) - [i119]Raja Marjieh, Sreejan Kumar, Declan Campbell, Liyi Zhang, Gianluca M. Bencomo, Jake Snell, Thomas L. Griffiths:
Using Contrastive Learning with Generative Similarity to Learn Spaces that Capture Human Inductive Biases. CoRR abs/2405.19420 (2024) - [i118]Jian-Qiao Zhu, Thomas L. Griffiths:
Eliciting the Priors of Large Language Models using Iterated In-Context Learning. CoRR abs/2406.01860 (2024) - [i117]Liyi Zhang, Logan Nelson, Thomas L. Griffiths:
Analyzing the Benefits of Prototypes for Semi-Supervised Category Learning. CoRR abs/2406.02268 (2024) - [i116]Liyi Zhang, Michael Y. Li, Thomas L. Griffiths:
What Should Embeddings Embed? Autoregressive Models Represent Latent Generating Distributions. CoRR abs/2406.03707 (2024) - [i115]Ilia Sucholutsky, Katherine M. Collins, Maya Malaviya, Nori Jacoby, Weiyang Liu, Theodore R. Sumers, Michalis Korakakis, Umang Bhatt, Mark K. Ho, Joshua B. Tenenbaum, Bradley C. Love, Zachary A. Pardos, Adrian Weller, Thomas L. Griffiths:
Representational Alignment Supports Effective Machine Teaching. CoRR abs/2406.04302 (2024) - [i114]Ryan Liu, Jiayi Geng, Joshua C. Peterson, Ilia Sucholutsky, Thomas L. Griffiths:
Large Language Models Assume People are More Rational than We Really are. CoRR abs/2406.17055 (2024) - [i113]Akshara Prabhakar, Thomas L. Griffiths, R. Thomas McCoy:
Deciphering the Factors Influencing the Efficacy of Chain-of-Thought: Probability, Memorization, and Noisy Reasoning. CoRR abs/2407.01687 (2024) - [i112]Katherine M. Collins, Ilia Sucholutsky, Umang Bhatt, Kartik Chandra, Lionel Wong, Mina Lee, Cedegao E. Zhang, Tan Zhi-Xuan, Mark K. Ho, Vikash Mansinghka, Adrian Weller, Joshua B. Tenenbaum, Thomas L. Griffiths:
Building Machines that Learn and Think with People. CoRR abs/2408.03943 (2024) - [i111]Jian-Qiao Zhu, Joshua C. Peterson, Benjamin Enke, Thomas L. Griffiths:
Capturing the Complexity of Human Strategic Decision-Making with Machine Learning. CoRR abs/2408.07865 (2024) - [i110]Sebastian Musslick, Laura Bartlett, Suyog H. Chandramouli, Marina Dubova, Fernand Gobet, Thomas L. Griffiths, Jessica Hullman, Ross D. King, J. Nathan Kutz, Christopher G. Lucas, Suhas Mahesh, Franco Pestilli, Sabina J. Sloman, William R. Holmes:
Automating the Practice of Science - Opportunities, Challenges, and Implications. CoRR abs/2409.05890 (2024) - [i109]R. Thomas McCoy, Shunyu Yao, Dan Friedman, Mathew D. Hardy, Thomas L. Griffiths:
When a language model is optimized for reasoning, does it still show embers of autoregression? An analysis of OpenAI o1. CoRR abs/2410.01792 (2024) - [i108]C. Nicolò De Sabbata, Theodore R. Sumers, Thomas L. Griffiths:
Rational Metareasoning for Large Language Models. CoRR abs/2410.05563 (2024) - [i107]Marcel Binz, Elif Akata, Matthias Bethge, Franziska Brändle, Fred Callaway, Julian Coda-Forno, Peter Dayan, Can Demircan, Maria K. Eckstein, Noémi Élteto, Thomas L. Griffiths, Susanne Haridi, Akshay K. Jagadish, Li Ji-An, Alexander Kipnis, Sreejan Kumar, Tobias Ludwig, Marvin Mathony, Marcelo G. Mattar, Alireza Modirshanechi, Surabhi S. Nath, Joshua C. Peterson, Milena Rmus, Evan M. Russek, Tankred Saanum, Natalia Scharfenberg, Johannes A. Schubert, Luca M. Schulze Buschoff, Nishad Singhi, Xin Sui, Mirko Thalmann, Fabian J. Theis, Vuong Truong, Vishaal Udandarao, Konstantinos Voudouris, Robert Wilson, Kristin Witte, Shuchen Wu, Dirk Wulff, Huadong Xiong, Eric Schulz:
Centaur: a foundation model of human cognition. CoRR abs/2410.20268 (2024) - [i106]Ryan Liu, Jiayi Geng, Addison J. Wu, Ilia Sucholutsky, Tania Lombrozo, Thomas L. Griffiths:
Mind Your Step (by Step): Chain-of-Thought can Reduce Performance on Tasks where Thinking Makes Humans Worse. CoRR abs/2410.21333 (2024) - 2023
- [j43]Aditi Jha, Joshua C. Peterson, Thomas L. Griffiths:
Extracting Low-Dimensional Psychological Representations from Convolutional Neural Networks. Cogn. Sci. 47(1) (2023) - [j42]Natalia Vélez, Brian R. Christian, Mathew D. Hardy, Bill D. Thompson, Thomas L. Griffiths:
How do Humans Overcome Individual Computational Limitations by Working Together? Cogn. Sci. 47(1) (2023) - [j41]Michael Y. Li, Fred Callaway, William D. Thompson, Ryan P. Adams, Thomas L. Griffiths:
Learning to Learn Functions. Cogn. Sci. 47(4) (2023) - [j40]Daniel Reichman, Falk Lieder, David D. Bourgin, Nimrod Talmon, Thomas L. Griffiths:
The Computational Challenges of Means Selection Problems: Network Structure of Goal Systems Predicts Human Performance. Cogn. Sci. 47(8) (2023) - [j39]Carlos G. Correa, Mark K. Ho, Frederick Callaway, Nathaniel D. Daw, Thomas L. Griffiths:
Humans decompose tasks by trading off utility and computational cost. PLoS Comput. Biol. 19(6) (2023) - [j38]Sreejan Kumar, Ishita Dasgupta, Nathaniel D. Daw, Jonathan D. Cohen, Thomas L. Griffiths:
Disentangling Abstraction from Statistical Pattern Matching in Human and Machine Learning. PLoS Comput. Biol. 19(8) (2023) - [c215]Mathew D. Hardy, Ilia Sucholutsky, Bill Thompson, Tom Griffiths:
Large language models meet cognitive science: LLMs as tools, models, and participants. CogSci 2023 - [c214]Raja Marjieh, Ilia Sucholutsky, Pol van Rijn, Nori Jacoby, Tom Griffiths:
What Language Reveals about Perception: Distilling Psychophysical Knowledge from Large Language Models. CogSci 2023 - [c213]Joshua C. Peterson, Marina Mancoridis, Tom Griffiths:
To each their own theory: Exploring the limits of individual differences in decisions under risk. CogSci 2023 - [c212]Sunayana Rane, Mira L. Nencheva, Zeyu Wang, Casey Lew-Williams, Olga Russakovsky, Tom Griffiths:
Predicting Word Learning in Children from the Performance of Computer Vision Systems. CogSci 2023 - [c211]Cameron Rouse Turner, Thomas J. H. Morgan, Tom Griffiths:
The joint evolution of sensory systems and decision policy allows cognition. CogSci 2023 - [c210]Feng Xia, Jian-Qiao Zhu, Tom Griffiths:
Comparing Human Predictions from Expert Advice to On-line Optimization Algorithms. CogSci 2023 - [c209]Jian-Qiao Zhu, Adam Sanborn, Nick Chater, Tom Griffiths:
Computation-Limited Bayesian Updating. CogSci 2023 - [c208]Michael Chang, Alyssa L. Dayan, Franziska Meier, Thomas L. Griffiths, Sergey Levine, Amy Zhang:
Hierarchical Abstraction for Combinatorial Generalization in Object Rearrangement. ICLR 2023 - [c207]Raja Marjieh, Pol van Rijn, Ilia Sucholutsky, Theodore R. Sumers, Harin Lee, Thomas L. Griffiths, Nori Jacoby:
Words are all you need? Language as an approximation for human similarity judgments. ICLR 2023 - [c206]Raja Marjieh, Ilia Sucholutsky, Thomas A. Langlois, Nori Jacoby, Thomas L. Griffiths:
Analyzing Diffusion as Serial Reproduction. ICML 2023: 24005-24019 - [c205]Bhishma Dedhia, Michael Chang, Jake Snell, Tom Griffiths, Niraj K. Jha:
Im-Promptu: In-Context Composition from Image Prompts. NeurIPS 2023 - [c204]Ilia Sucholutsky, Tom Griffiths:
Alignment with human representations supports robust few-shot learning. NeurIPS 2023 - [c203]Zi Wang, Alexander Ku, Jason Baldridge, Tom Griffiths, Been Kim:
Gaussian Process Probes (GPP) for Uncertainty-Aware Probing. NeurIPS 2023 - [c202]Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Tom Griffiths, Yuan Cao, Karthik Narasimhan:
Tree of Thoughts: Deliberate Problem Solving with Large Language Models. NeurIPS 2023 - [c201]Michael Y. Li, Erin Grant, Thomas L. Griffiths:
Gaussian Process Surrogate Models for Neural Networks. UAI 2023: 1241-1252 - [c200]Ilia Sucholutsky, Ruairidh M. Battleday, Katherine M. Collins, Raja Marjieh, Joshua C. Peterson, Pulkit Singh, Umang Bhatt, Nori Jacoby, Adrian Weller, Thomas L. Griffiths:
On the informativeness of supervision signals. UAI 2023: 2036-2046 - [i105]Ilia Sucholutsky, Thomas L. Griffiths:
Alignment with human representations supports robust few-shot learning. CoRR abs/2301.11990 (2023) - [i104]Raja Marjieh, Ilia Sucholutsky, Pol van Rijn, Nori Jacoby, Thomas L. Griffiths:
What Language Reveals about Perception: Distilling Psychophysical Knowledge from Large Language Models. CoRR abs/2302.01308 (2023) - [i103]Minkyu Shin, Jin Kim, Bas van Opheusden, Thomas L. Griffiths:
Superhuman Artificial Intelligence Can Improve Human Decision Making by Increasing Novelty. CoRR abs/2303.07462 (2023) - [i102]Michael Chang, Alyssa L. Dayan, Franziska Meier, Thomas L. Griffiths, Sergey Levine, Amy Zhang:
Neural Constraint Satisfaction: Hierarchical Abstraction for Combinatorial Generalization in Object Rearrangement. CoRR abs/2303.11373 (2023) - [i101]Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Thomas L. Griffiths, Yuan Cao, Karthik Narasimhan:
Tree of Thoughts: Deliberate Problem Solving with Large Language Models. CoRR abs/2305.10601 (2023) - [i100]R. Thomas McCoy, Thomas L. Griffiths:
Modeling rapid language learning by distilling Bayesian priors into artificial neural networks. CoRR abs/2305.14701 (2023) - [i99]Bhishma Dedhia, Michael Chang, Jake C. Snell, Thomas L. Griffiths, Niraj K. Jha:
Im-Promptu: In-Context Composition from Image Prompts. CoRR abs/2305.17262 (2023) - [i98]Zi Wang, Alexander Ku, Jason Baldridge, Thomas L. Griffiths, Been Kim:
Gaussian Process Probes (GPP) for Uncertainty-Aware Probing. CoRR abs/2305.18213 (2023) - [i97]Raja Marjieh, Nori Jacoby, Joshua C. Peterson, Thomas L. Griffiths:
The Universal Law of Generalization Holds for Naturalistic Stimuli. CoRR abs/2306.08564 (2023) - [i96]Theodore R. Sumers, Shunyu Yao, Karthik Narasimhan, Thomas L. Griffiths:
Cognitive Architectures for Language Agents. CoRR abs/2309.02427 (2023) - [i95]R. Thomas McCoy, Shunyu Yao, Dan Friedman, Matthew Hardy, Thomas L. Griffiths:
Embers of Autoregression: Understanding Large Language Models Through the Problem They are Trained to Solve. CoRR abs/2309.13638 (2023) - [i94]Ruiqi He, Carlos G. Correa, Thomas L. Griffiths, Mark K. Ho:
Structurally guided task decomposition in spatial navigation tasks. CoRR abs/2310.02221 (2023) - [i93]Kerem Oktar, Ilia Sucholutsky, Tania Lombrozo, Thomas L. Griffiths:
Dimensions of Disagreement: Unpacking Divergence and Misalignment in Cognitive Science and Artificial Intelligence. CoRR abs/2310.12994 (2023) - [i92]Ilia Sucholutsky, Lukas Muttenthaler, Adrian Weller, Andi Peng, Andreea Bobu, Been Kim, Bradley C. Love, Erin Grant, Jascha Achterberg, Joshua B. Tenenbaum, Katherine M. Collins, Katherine L. Hermann, Kerem Oktar, Klaus Greff, Martin N. Hebart, Nori Jacoby, Qiuyi Zhang, Raja Marjieh, Robert Geirhos, Sherol Chen, Simon Kornblith, Sunayana Rane, Talia Konkle, Thomas P. O'Connell, Thomas Unterthiner, Andrew K. Lampinen, Klaus-Robert Müller, Mariya Toneva, Thomas L. Griffiths:
Getting aligned on representational alignment. CoRR abs/2310.13018 (2023) - [i91]Sunayana Rane, Mark K. Ho, Ilia Sucholutsky, Thomas L. Griffiths:
Concept Alignment as a Prerequisite for Value Alignment. CoRR abs/2310.20059 (2023) - [i90]Ryan Liu, Howard Yen, Raja Marjieh, Thomas L. Griffiths, Ranjay Krishna:
Improving Interpersonal Communication by Simulating Audiences with Language Models. CoRR abs/2311.00687 (2023) - [i89]Yufei Tian, Abhilasha Ravichander, Lianhui Qin, Ronan Le Bras, Raja Marjieh, Nanyun Peng, Yejin Choi, Thomas L. Griffiths, Faeze Brahman:
MacGyver: Are Large Language Models Creative Problem Solvers? CoRR abs/2311.09682 (2023) - [i88]Thomas L. Griffiths, Jian-Qiao Zhu, Erin Grant, R. Thomas McCoy:
Bayes in the age of intelligent machines. CoRR abs/2311.10206 (2023) - [i87]Gianluca M. Bencomo, Jake C. Snell, Thomas L. Griffiths:
Implicit Maximum a Posteriori Filtering via Adaptive Optimization. CoRR abs/2311.10580 (2023) - [i86]Levin Brinkmann, Fabian Baumann, Jean-François Bonnefon, Maxime Derex, Thomas F. Müller, Anne-Marie Nussberger, Agnieszka Czaplicka, Alberto Acerbi, Thomas L. Griffiths, Joseph Henrich, Joel Z. Leibo, Richard McElreath, Pierre-Yves Oudeyer, Jonathan Stray, Iyad Rahwan:
Machine Culture. CoRR abs/2311.11388 (2023) - [i85]Jake C. Snell, Gianluca M. Bencomo, Thomas L. Griffiths:
A Metalearned Neural Circuit for Nonparametric Bayesian Inference. CoRR abs/2311.14601 (2023) - [i84]Carlos G. Correa, Sophia Sanborn, Mark K. Ho, Frederick Callaway, Nathaniel D. Daw, Thomas L. Griffiths:
Exploring the hierarchical structure of human plans via program generation. CoRR abs/2311.18644 (2023) - [i83]Qihong Lu, Tan T. Nguyen, Qiong Zhang, Uri Hasson, Thomas L. Griffiths, Jeffrey M. Zacks, Samuel J. Gershman, Kenneth A. Norman:
Toward a More Biologically Plausible Neural Network Model of Latent Cause Inference. CoRR abs/2312.08519 (2023) - [i82]Andrea Wynn, Ilia Sucholutsky, Thomas L. Griffiths:
Learning Human-like Representations to Enable Learning Human Values. CoRR abs/2312.14106 (2023) - [i81]Liyi Zhang, R. Thomas McCoy, Theodore R. Sumers, Jian-Qiao Zhu, Thomas L. Griffiths:
Deep de Finetti: Recovering Topic Distributions from Large Language Models. CoRR abs/2312.14226 (2023) - 2022
- [j37]Mark K. Ho, Thomas L. Griffiths:
Cognitive Science as a Source of Forward and Inverse Models of Human Decisions for Robotics and Control. Annu. Rev. Control. Robotics Auton. Syst. 5: 33-53 (2022) - [j36]Rachit Dubey, Thomas L. Griffiths, Peter Dayan:
The pursuit of happiness: A reinforcement learning perspective on habituation and comparisons. PLoS Comput. Biol. 18(8) (2022) - [j35]Mathew D. Hardy, Peaks M. Krafft, Bill Thompson, Thomas L. Griffiths:
Overcoming Individual Limitations Through Distributed Computation: Rational Information Accumulation in Multigenerational Populations. Top. Cogn. Sci. 14(3): 550-573 (2022) - [c199]Takateru Yamakoshi, Thomas L. Griffiths, Robert D. Hawkins:
Probing BERT's priors with serial reproduction chains. ACL (Findings) 2022: 3977-3992 - [c198]Maya Malaviya, Ilia Sucholutsky, Kerem Oktar, Tom Griffiths:
Can Humans Do Less-Than-One-Shot Learning? CogSci 2022 - [c197]Raja Marjieh, Ilia Sucholutsky, Theodore R. Sumers, Nori Jacoby, Tom Griffiths:
Predicting Human Similarity Judgments Using Large Language Models. CogSci 2022 - [c196]Ishita Dasgupta, Erin Grant, Tom Griffiths:
Distinguishing rule and exemplar-based generalization in learning systems. ICML 2022: 4816-4830 - [c195]Michael Chang, Tom Griffiths, Sergey Levine:
Object Representations as Fixed Points: Training Iterative Refinement Algorithms with Implicit Differentiation. NeurIPS 2022 - [c194]Sreejan Kumar, Carlos G. Correa, Ishita Dasgupta, Raja Marjieh, Michael Y. Hu, Robert D. Hawkins, Jonathan D. Cohen, Nathaniel D. Daw, Karthik Narasimhan, Tom Griffiths:
Using natural language and program abstractions to instill human inductive biases in machines. NeurIPS 2022 - [c193]Theodore R. Sumers, Robert D. Hawkins, Mark K. Ho, Tom Griffiths, Dylan Hadfield-Menell:
How to talk so AI will learn: Instructions, descriptions, and autonomy. NeurIPS 2022 - [i80]Maya Malaviya, Ilia Sucholutsky, Kerem Oktar, Thomas L. Griffiths:
Can Humans Do Less-Than-One-Shot Learning? CoRR abs/2202.04670 (2022) - [i79]Raja Marjieh, Ilia Sucholutsky, Theodore R. Sumers, Nori Jacoby, Thomas L. Griffiths:
Predicting Human Similarity Judgments Using Large Language Models. CoRR abs/2202.04728 (2022) - [i78]Takateru Yamakoshi, Robert D. Hawkins, Thomas L. Griffiths:
Probing BERT's priors with serial reproduction chains. CoRR abs/2202.12226 (2022) - [i77]Sreejan Kumar, Ishita Dasgupta, Raja Marjieh, Nathaniel D. Daw, Jonathan D. Cohen, Thomas L. Griffiths:
Disentangling Abstraction from Statistical Pattern Matching in Human and Machine Learning. CoRR abs/2204.01437 (2022) - [i76]Theodore R. Sumers, Robert D. Hawkins, Mark K. Ho, Thomas L. Griffiths, Dylan Hadfield-Menell:
Linguistic communication as (inverse) reward design. CoRR abs/2204.05091 (2022) - [i75]Sreejan Kumar, Carlos G. Correa, Ishita Dasgupta, Raja Marjieh, Michael Y. Hu, Robert D. Hawkins, Nathaniel D. Daw, Jonathan D. Cohen, Karthik Narasimhan, Thomas L. Griffiths:
Using Natural Language and Program Abstractions to Instill Human Inductive Biases in Machines. CoRR abs/2205.11558 (2022) - [i74]Raja Marjieh, Pol van Rijn, Ilia Sucholutsky, Theodore R. Sumers, Harin Lee, Thomas L. Griffiths, Nori Jacoby:
Words are all you need? Capturing human sensory similarity with textual descriptors. CoRR abs/2206.04105 (2022) - [i73]Theodore R. Sumers, Robert D. Hawkins, Mark K. Ho, Thomas L. Griffiths, Dylan Hadfield-Menell:
How to talk so your robot will learn: Instructions, descriptions, and pragmatics. CoRR abs/2206.07870 (2022) - [i72]Michael Chang, Thomas L. Griffiths, Sergey Levine:
Object Representations as Fixed Points: Training Iterative Refinement Algorithms with Implicit Differentiation. CoRR abs/2207.00787 (2022) - [i71]Sunayana Rane, Mira L. Nencheva, Zeyu Wang, Casey Lew-Williams, Olga Russakovsky, Thomas L. Griffiths:
Predicting Word Learning in Children from the Performance of Computer Vision Systems. CoRR abs/2207.09847 (2022) - [i70]Michael Y. Li, Erin Grant, Thomas L. Griffiths:
Gaussian process surrogate models for neural networks. CoRR abs/2208.06028 (2022) - [i69]Mathew D. Hardy, Bill D. Thompson, P. M. Krafft, Thomas L. Griffiths:
Bias amplification in experimental social networks is reduced by resampling. CoRR abs/2208.07261 (2022) - [i68]Raja Marjieh, Ilia Sucholutsky, Thomas A. Langlois, Nori Jacoby, Thomas L. Griffiths:
Analyzing Diffusion as Serial Reproduction. CoRR abs/2209.14821 (2022) - [i67]Ilia Sucholutsky, Raja Marjieh, Nori Jacoby, Thomas L. Griffiths:
On the Informativeness of Supervision Signals. CoRR abs/2211.01407 (2022) - [i66]Carlos G. Correa, Mark K. Ho, Frederick Callaway, Nathaniel D. Daw, Thomas L. Griffiths:
Humans decompose tasks by trading off utility and computational cost. CoRR abs/2211.03890 (2022) - 2021
- [j34]Stephan C. Meylan, Thomas L. Griffiths:
The Challenges of Large-Scale, Web-Based Language Datasets: Word Length and Predictability Revisited. Cogn. Sci. 45(6) (2021) - [j33]Frederick Callaway, Antonio Rangel, Thomas L. Griffiths:
Fixation patterns in simple choice reflect optimal information sampling. PLoS Comput. Biol. 17(3) (2021) - [c192]Theodore R. Sumers, Mark K. Ho, Robert X. D. Hawkins, Karthik Narasimhan, Thomas L. Griffiths:
Learning Rewards From Linguistic Feedback. AAAI 2021: 6002-6010 - [c191]