James L. McClelland

Publication Activity (10 Years)

Years Active: 1985-2023
Publications (10 Years): 34

Top Topics

Pitman Yor Process

Weighted Constraint Satisfaction

Top Venues

Publications

Takateru Yamakoshi, James L. McClelland, Adele E. Goldberg, Robert D. Hawkins
Causal interventions expose implicit situation models for commonsense language understanding. CoRR (2023)
Yuxuan Li, James L. McClelland
Representations and Computations in Transformers that Support Generalization on Structured Tasks. Trans. Mach. Learn. Res. 2023 (2023)
Takateru Yamakoshi, James L. McClelland, Adele E. Goldberg, Robert D. Hawkins
Causal interventions expose implicit situation models for commonsense language understanding. ACL (Findings) (2023)
Drew A. Hudson, Daniel Zoran, Mateusz Malinowski, Andrew K. Lampinen, Andrew Jaegle, James L. McClelland, Loic Matthey, Felix Hill, Alexander Lerchner
SODA: Bottleneck Diffusion Models for Representation Learning. CoRR (2023)
Andrew Joohun Nam, Mustafa Abdool, Trevor Maxfield, James L. McClelland
Out-of-Distribution Generalization in Algorithmic Reasoning Through Curriculum Learning. CoRR (2022)
Andrew K. Lampinen, Nicholas A. Roy, Ishita Dasgupta, Stephanie Cy Chan, Allison C. Tam, James L. McClelland, Chen Yan, Adam Santoro, Neil C. Rabinowitz, Jane X. Wang, Felix Hill
Tell me why! Explanations support learning relational and causal structure. ICML (2022)
Stephanie Chan, Adam Santoro, Andrew K. Lampinen, Jane Wang, Aaditya Singh, Pierre H. Richemond, James L. McClelland, Felix Hill
Data Distributional Properties Drive Emergent In-Context Learning in Transformers. NeurIPS (2022)
Andrew K. Lampinen, Ishita Dasgupta, Stephanie C. Y. Chan, Kory Matthewson, Michael Henry Tessler, Antonia Creswell, James L. McClelland, Jane X. Wang, Felix Hill
Can language models learn from explanations in context? CoRR (2022)
Yuxuan Li, James L. McClelland
A weighted constraint satisfaction approach to human goal-directed decision making. PLoS Comput. Biol. 18 (6) (2022)
Yuxuan Li, James L. McClelland
Systematic Generalization and Emergent Structures in Transformers Trained on Structured Tasks. CoRR (2022)
Ishita Dasgupta, Andrew K. Lampinen, Stephanie C. Y. Chan, Antonia Creswell, Dharshan Kumaran, James L. McClelland, Felix Hill
Language models show human-like content effects on reasoning. CoRR (2022)
Andrew Joohun Nam, Mengye Ren, Chelsea Finn, James L. McClelland
Learning to Reason With Relational Abstractions. CoRR (2022)
Andrew K. Lampinen, Ishita Dasgupta, Stephanie C. Y. Chan, Kory W. Mathewson, Mh Tessler, Antonia Creswell, James L. McClelland, Jane Wang, Felix Hill
Can language models learn from explanations in context? EMNLP (Findings) (2022)
Andrew K. Lampinen, Nicholas A. Roy, Ishita Dasgupta, Stephanie C. Y. Chan, Allison C. Tam, James L. McClelland, Chen Yan, Adam Santoro, Neil C. Rabinowitz, Jane X. Wang, Felix Hill
Tell me why! - Explanations support learning of relational and causal structure. CoRR (2021)
Andrew Joohun Nam, James L. McClelland
What underlies rapid learning and systematic generalization in humans. CoRR (2021)
Mohammad Rostami, Soheil Kolouri, Praveen K. Pilly, James L. McClelland
Generative Continual Concept Learning. AAAI (2020)
Felix Hill, Andrew K. Lampinen, Rosalia Schneider, Stephen Clark, Matthew Botvinick, James L. McClelland, Adam Santoro
Environmental drivers of systematicity and generalization in a situated agent. ICLR (2020)
Silvester Sabathiel, James L. McClelland, Trygve Solstad
Emerging Representations for Counting in a Neural Network Agent Interacting with a Multimodal Environment. ALIFE (2020)
Andrew K. Lampinen, James L. McClelland
Transforming task representations to allow deep learning models to perform novel tasks. CoRR (2020)
Andrew K. Lampinen, James L. McClelland
Transforming task representations to perform novel tasks. Proc. Natl. Acad. Sci. USA 117 (52) (2020)
Alessandro G. Di Nuovo, James L. McClelland
Developing the knowledge of number digits in a child-like robot. Nat. Mach. Intell. 1 (12) (2019)
Felix Hill, Andrew K. Lampinen, Rosalia Schneider, Stephen Clark, Matthew Botvinick, James L. McClelland, Adam Santoro
Emergent Systematic Generalization in a Situated Agent. CoRR (2019)
Andrew K. Lampinen, James L. McClelland
Embedded Meta-Learning: Toward more flexible deep-learning models. CoRR (2019)
Mohammad Rostami, Soheil Kolouri, James L. McClelland, Praveen K. Pilly
Generative Continual Concept Learning. CoRR (2019)
James L. McClelland, Felix Hill, Maja Rudolph, Jason Baldridge, Hinrich Schütze
Extending Machine Language Models toward Human-Level Language Understanding. CoRR (2019)
Frank J. Kanayet, Andrew Mattarella-Micke, Peter J. Kohler, Anthony M. Norcia, Bruce D. McCandliss, James L. McClelland
Distinct Representations of Magnitude and Spatial Position within Parietal Cortex during Number-Space Mapping. J. Cogn. Neurosci. 30 (2) (2018)
Andrew M. Saxe, James L. McClelland, Surya Ganguli
A mathematical theory of semantic development in deep neural networks. CoRR (2018)
Andrew K. Lampinen, James L. McClelland
One-shot and few-shot learning of word embeddings. CoRR (2017)
Andrew K. Lampinen, Shaw Hsu, James L. McClelland
Analogies Emerge from Learning Dyamics in Neural Networks. CogSci (2017)
Alex Kuefler, Mykel J. Kochenderfer, James L. McClelland
Geometric Concept Acquisition in a Dueling Deep Q-Network. CogSci (2017)
Milena Rabovsky, Steven Stenberg Hansen, James L. McClelland
Neural responses decrease while performance increases with practice: A neural network model. CogSci (2017)
James L. McClelland, Steven Stenberg Hansen, Andrew M. Saxe
Tutorial Workshop on Contemporary Deep Neural Network Models. CogSci (2016)
Milena Rabovsky, Steven Stenberg Hansen, James L. McClelland
N400 amplitudes reflect change in a probabilistic representation of meaning: Evidence from a connectionist model. CogSci (2016)
Arianna Yuan, Te-Lin Wu, James L. McClelland
Emergence of Euclidean geometrical intuitions in hierarchical generative models. CogSci (2016)
Martha W. Alibali, Chuck Kalish, Timothy T. Rogers, Christine Massey, Philip J. Kellman, Vladimir M. Sloutsky, James L. McClelland, Kevin W. Mickey
Connecting learning, memory, and representation in math education. CogSci (2015)
James L. McClelland, Daniel Mirman, Donald J. Bolger, Pranav Khaitan
Interactive Activation and Mutual Constraint Satisfaction in Perception and Cognition. Cogn. Sci. 38 (6) (2014)
Kevin W. Mickey, James L. McClelland
A neural network model of learning mathematical equivalence. CogSci (2014)
Andrew M. Saxe, James L. McClelland, Surya Ganguli
Exact solutions to the nonlinear dynamics of learning in deep linear neural networks. ICLR (2014)
Steven Stenberg Hansen, Cameron R. L. McKenzie, James L. McClelland
Two Plus Three Is Five: Discovering Efficient Addition Strategies without Metacognition. CogSci (2014)
Timothy T. Rogers, James L. McClelland
Parallel Distributed Processing at 25: Further Explorations in the Microstructure of Cognition. Cogn. Sci. 38 (6) (2014)
Amy H. Criss, Mark E. Wheeler, James L. McClelland
A Differentiation Account of Recognition Memory: Evidence from fMRI. J. Cogn. Neurosci. 25 (3) (2013)
Andrew M. Saxe, James L. McClelland, Surya Ganguli
Learning hierarchical categories in deep neural networks. CogSci (2013)
Cameron R. L. McKenzie, James L. McClelland
From symbols to analog magnitudes: A process model of fraction comparison, with fits to experimental data. CogSci (2013)
Anna C. Schapiro, James L. McClelland, Stephen R. Welbourne, Timothy T. Rogers, Matthew A. Lambon Ralph
Why Bilateral Damage Is Worse than Unilateral Damage to the Brain. J. Cogn. Neurosci. 25 (12) (2013)
Kevin W. Mickey, James L. McClelland
Running circles around symbol manipulation in trigonometry. CogSci (2013)
Will Y. Zou, James L. McClelland
Progressive Development of the Number Sense in a Deep Neural Network. CogSci (2013)
Brenden M. Lake, James L. McClelland
Estimating the strength of unlabeled information during semi-supervised learning. CogSci (2011)
Erin M. Ingvalson, James L. McClelland, Lori L. Holt
Predicting native English-like performance by native Japanese speakers. J. Phonetics 39 (4) (2011)
Cynthia M. Henderson, James L. McClelland
A PDP model of the simultaneous perception of multiple objects. Connect. Sci. 23 (2) (2011)
James L. McClelland
Emergence in Cognitive Science. Top. Cogn. Sci. 2 (4) (2010)
James L. McClelland
The Place of Modeling in Cognitive Science. Top. Cogn. Sci. 1 (1) (2009)
James L. McClelland
Is a Machine Realization of Truly Human-Like Intelligence Achievable? Cogn. Comput. 1 (1) (2009)
Brenden M. Lake, Gautam K. Vallabha, James L. McClelland
Modeling Unsupervised Perceptual Category Learning. IEEE Trans. Auton. Ment. Dev. 1 (1) (2009)
Daniel Mirman, James L. McClelland, Lori L. Holt, James S. Magnuson
Effects of Attention on the Strength of Lexical Influences on Speech Perception: Behavioral Experiments and Computational Mechanisms. Cogn. Sci. 32 (2) (2008)
James L. McClelland, Kim Plunkett, Juyang Weng
Guest Editorial: Convergent Approaches to the Understanding of Autonomous Mental Development. IEEE Trans. Evol. Comput. 11 (2) (2007)
Elizabeth Tricomi, Mauricio R. Delgado, Bruce D. McCandliss, James L. McClelland, Julie A. Fiez
Performance Feedback Drives Caudate Activation in a Phonological Learning Task. J. Cogn. Neurosci. 18 (6) (2006)
Andrea Mechelli, Jennifer T. Crinion, Steven Long, Karl J. Friston, Matthew A. Lambon Ralph, Karalyn Patterson, James L. McClelland, Cathy J. Price
Dissociating Reading Processes on the Basis of Neuronal Interactions. J. Cogn. Neurosci. 17 (11) (2005)
Karalyn Patterson, Matthew A. Lambon Ralph, Helen Bird, John R. Hodges, James L. McClelland
Normal and impaired processing in quasi-regular domains of language: the case of English past-tense verbs. INTERSPEECH (2000)
Javier R. Movellan, James L. McClelland
Information Factorization in Connectionist Models of Perception. NIPS (1999)
Randall C. O'Reilly, Kenneth A. Norman, James L. McClelland
A Hippocampal Model of Recognition Memory. NIPS (1997)
Javier R. Movellan, James L. McClelland
Learning Continuous Probability Distributions with Symmetric Diffusion Networks. Cogn. Sci. 17 (4) (1993)
David Servan-Schreiber, Axel Cleeremans, James L. McClelland
Graded State Machines: The Representation of Temporal Contingencies in Simple Recurrent Networks. Mach. Learn. 7 (1991)
Mark F. St. John, James L. McClelland
Learning and Applying Contextual Constraints in Sentence Comprehension. Artif. Intell. 46 (1-2) (1990)
James L. McClelland
Connectionist Models of Language. IWPT (1989)
Axel Cleeremans, David Servan-Schreiber, James L. McClelland
Finite State Automata and Simple Recurrent Networks. Neural Comput. 1 (3) (1989)
David Servan-Schreiber, Axel Cleeremans, James L. McClelland
Learning Subsequential Structure in Simple Recurrent Networks. NIPS (1988)
James L. McClelland
Parallel Distributed Processing and Role Assignment Constraints. TINLAP (1987)
Geoffrey E. Hinton, James L. McClelland
Learning Representations by Recirculation. NIPS (1987)
James L. McClelland
Putting Knowledge in its Place: A Scheme for Programming Parallel Processing Structures on the Fly. Cogn. Sci. 9 (1) (1985)