Group control and kernels: the 1-d equigrouping problem. - Daniel Yamins; Stephen Waydo; Navin Khaneja (CDC, 2004)

Towards a theory of "local to global" in distributed multi-agent systems (I). - Daniel Yamins (AAMAS, 2005)

Towards a theory of "local to global" in distributed multi-agent systems (II). - Daniel Yamins (AAMAS, 2005)

Dynamic Task Assignment in Robot Swarms. - James McLurkin; Daniel Yamins (Robotics: Science and Systems, 2005)

The emergence of global properties from local interactions: static properties and one-dimensional patterns. - Daniel Yamins (AAMAS, 2006)

Engineering self-organizing multi-agent systems. - Radhika Nagpal; Chih-Han Yu; Daniel Yamins (AAMAS (Demos), 2008)

Automated global-to-local programming in 1-D spatial multi-agent systems. - Daniel Yamins; Radhika Nagpal (AAMAS (2), 2008)

StarFlow: A Script-Centric Data Analysis Environment. - Elaine Angelino; Daniel Yamins; Margo I. Seltzer (IPAW, 2010)

Making a Science of Model Search - James Bergstra; Dan Yamins; David D. Cox (CoRR, 2012)

Making a Science of Model Search: Hyperparameter Optimization in Hundreds of Dimensions for Vision Architectures. - James Bergstra; Daniel Yamins; David D. Cox (ICML (1), 2013)

Hierarchical Modular Optimization of Convolutional Networks Achieves Representations Similar to Macaque IT and Human Ventral Stream. - Daniel Yamins; Ha Hong; Charles F. Cadieu; James J. DiCarlo (NIPS, 2013)

Hyperopt: A Python Library for Optimizing the Hyperparameters of Machine Learning Algorithms. - James Bergstra; Dan Yamins; David D. Cox (SciPy, 2013)

The Neural Representation Benchmark and its Evaluation on Brain and Machine - Charles F. Cadieu; Ha Hong; Dan Yamins; Nicolas Pinto; Najib J. Majaj; James J. DiCarlo (ICLR, 2013)

Mapping core similarity among visual objects across image modalities. - Judith E. Fan; Daniel Yamins; James J. DiCarlo; Nicholas B. Turk-Browne (SIGGRAPH Posters, 2014)

Deep Neural Networks Rival the Representation of Primate IT Cortex for Core Visual Object Recognition. - Charles F. Cadieu; Ha Hong; Daniel Yamins; Nicolas Pinto; Diego Ardila; Ethan A. Solomon; Najib J. Majaj; James J. DiCarlo (PLoS Comput. Biol., 2014)

Common object representations for visual recognition and production. - Judith E. Fan; Daniel Yamins; Nicholas B. Turk-Browne (CogSci, 2015)

Toward Goal-Driven Neural Network Models for the Rodent Whisker-Trigeminal System. - Chengxu Zhuang; Jonas Kubilius; Mitra J. Z. Hartmann; Daniel Yamins (NIPS, 2017)

A Useful Motif for Flexible Task Learning in an Embodied Two-Dimensional Visual Environment. - Kevin T. Feigelis; Daniel L. K. Yamins (CoRR, 2017)

Deep Learning Predicts Correlation between a Functional Signature of Higher Visual Areas and Sparse Firing of Neurons. - Chengxu Zhuang; Yulong Wang; Daniel Yamins; Xiaolin Hu (Frontiers Comput. Neurosci., 2017)

Emergence of Structured Behaviors from Curiosity-Based Intrinsic Motivation. - Nick Haber; Damian Mrowca; Li Fei-Fei 001; Daniel L. K. Yamins (CogSci, 2018)

Modular Continual Learning in a Unified Visual Environment. - Kevin T. Feigelis; Blue Sheffer; Daniel L. K. Yamins (ICLR (Poster), 2018)

Learning to Play With Intrinsically-Motivated, Self-Aware Agents. - Nick Haber; Damian Mrowca; Stephanie Wang; Li Fei-Fei 001; Daniel L. K. Yamins (NeurIPS, 2018)

Flexible neural representation for physics prediction. - Damian Mrowca; Chengxu Zhuang; Elias Wang; Nick Haber; Li Fei-Fei 001; Josh Tenenbaum 001; Daniel L. K. Yamins (NeurIPS, 2018)

Task-Driven Convolutional Recurrent Models of the Visual System. - Aran Nayebi; Daniel Bear; Jonas Kubilius; Kohitij Kar; Surya Ganguli; David Sussillo; James J. DiCarlo; Daniel L. K. Yamins (NeurIPS, 2018)

Common Object Representations for Visual Production and Recognition. - Judith E. Fan; Daniel L. K. Yamins; Nicholas B. Turk-Browne (Cogn. Sci., 2018)

Learning to Play with Intrinsically-Motivated Self-Aware Agents. - Nick Haber; Damian Mrowca; Li Fei-Fei 001; Daniel L. K. Yamins (CoRR, 2018)

Local Aggregation for Unsupervised Learning of Visual Embeddings. - Chengxu Zhuang; Alex Lin Zhai; Daniel Yamins (ICCV, 2019)

Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNs. - Jonas Kubilius; Martin Schrimpf; Ha Hong; Najib J. Majaj; Rishi Rajalingham; Elias B. Issa; Kohitij Kar; Pouya Bashivan; Jonathan Prescott-Roy; Kailyn Schmidt; Aran Nayebi; Daniel Bear; et al. (NeurIPS, 2019)

Local Label Propagation for Large-Scale Semi-Supervised Learning. - Chengxu Zhuang; Xuehao Ding; Divyanshu Murli; Daniel Yamins (CoRR, 2019)

Unsupervised Learning from Video with Deep Neural Embeddings. - Chengxu Zhuang; Alex Andonian; Daniel Yamins (CoRR, 2019)

Learning in Social Environments with Curious Neural Agents. - Megumi Sano; Julian De Freitas; Nick Haber; Daniel L. K. Yamins (CogSci, 2020)

Unsupervised Learning From Video With Deep Neural Embeddings. - Chengxu Zhuang; Tianwei She; Alex Andonian; Max Sobol Mark; Daniel Yamins (CVPR, 2020)

Flexible and Efficient Long-Range Planning Through Curious Exploration. - Aidan Curtis; Minjian Xin; Dilip Arumugam; Kevin T. Feigelis; Daniel Yamins (ICML, 2020)

Active World Model Learning with Progress Curiosity. - Kuno Kim; Megumi Sano; Julian De Freitas; Nick Haber; Daniel Yamins (ICML, 2020)

Two Routes to Scalable Credit Assignment without Weight Symmetry. - Daniel Kunin; Aran Nayebi; Javier Sagastuy-Breña; Surya Ganguli; Jonathan M. Bloom; Daniel Yamins (ICML, 2020)

Visual Grounding of Learned Physical Models. - Yunzhu Li; Toru Lin; Kexin Yi; Daniel Bear; Daniel Yamins; Jiajun Wu 001; Joshua B. Tenenbaum; Antonio Torralba 001 (ICML, 2020)

Learning Physical Graph Representations from Visual Scenes. - Daniel Bear; Chaofei Fan; Damian Mrowca; Yunzhu Li; Seth Alter; Aran Nayebi; Jeremy Schwartz; Li Fei-Fei 001; Jiajun Wu 001; Josh Tenenbaum 001; Daniel L. K. Yamins (NeurIPS, 2020)

Identifying Learning Rules From Neural Network Observables. - Aran Nayebi; Sanjana Srivastava; Surya Ganguli; Daniel L. K. Yamins (NeurIPS, 2020)

Pruning neural networks without any data by iteratively conserving synaptic flow. - Hidenori Tanaka; Daniel Kunin; Daniel L. K. Yamins; Surya Ganguli (NeurIPS, 2020)

On Mutual Information in Contrastive Learning for Visual Representations. - Mike Wu; Chengxu Zhuang; Milan Mosse; Daniel Yamins; Noah D. Goodman (CoRR, 2020)

ThreeDWorld: A Platform for Interactive Multi-Modal Physical Simulation. - Chuang Gan 001; Jeremy Schwartz; Seth Alter; Martin Schrimpf; James Traer; Julian De Freitas; Jonas Kubilius; Abhishek Bhandwaldar; Nick Haber; Megumi Sano; Kuno Kim; Elias Wang; et al. (CoRR, 2020)

Measuring and predicting variation in the interestingness of physical structures. - Cameron Holdaway; Daniel M. Bear; Samaher Radwan; Michael C. Frank; Daniel L. K. Yamins; Judith E. Fan (CogSci, 2021)

Predicting children's and adults' preferences in physical interactions via physics simulation. - George Kachergis; Samaher Radwan; Bria Long; Judith E. Fan; Michael Lingelbach; Daniel M. Bear; Daniel L. K. Yamins; Michael C. Frank (CogSci, 2021)

Neural Mechanics: Symmetry and Broken Conservation Laws in Deep Learning Dynamics. - Daniel Kunin; Javier Sagastuy-Breña; Surya Ganguli; Daniel L. K. Yamins; Hidenori Tanaka (ICLR, 2021)

Conditional Negative Sampling for Contrastive Learning of Visual Representations. - Mike Wu; Milan Mosse; Chengxu Zhuang; Daniel Yamins; Noah D. Goodman (ICLR, 2021)

Physion: Evaluating Physical Prediction from Vision in Humans and Machines. - Daniel Bear; Elias Wang; Damian Mrowca; Felix J. Binder; Hsiao-Yu Tung; R. T. Pramod; Cameron Holdaway; Sirui Tao; Kevin A. Smith 001; Fan-Yun Sun; Fei-Fei Li 001; Nancy Kanwisher; et al. (NeurIPS Datasets and Benchmarks, 2021)

ThreeDWorld: A Platform for Interactive Multi-Modal Physical Simulation. - Chuang Gan 001; Jeremy Schwartz; Seth Alter; Damian Mrowca; Martin Schrimpf; James Traer; Julian De Freitas; Jonas Kubilius; Abhishek Bhandwaldar; Nick Haber; Megumi Sano; Kuno Kim; et al. (NeurIPS Datasets and Benchmarks, 2021)

Explaining heterogeneity in medial entorhinal cortex with task-driven neural networks. - Aran Nayebi; Alexander Attinger; Malcolm Campbell; Kiah Hardcastle; Isabel Low; Caitlin S. Mallory; Gabriel Mel; Ben Sorscher; Alex H. Williams; Surya Ganguli; Lisa M. Giocomo; Daniel L. K. Yamins (NeurIPS, 2021)

The ThreeDWorld Transport Challenge: A Visually Guided Task-and-Motion Planning Benchmark for Physically Realistic Embodied AI. - Chuang Gan 001; Siyuan Zhou; Jeremy Schwartz; Seth Alter; Abhishek Bhandwaldar; Dan Gutfreund; Daniel L. K. Yamins; James J. DiCarlo; Josh H. McDermott; Antonio Torralba 001; Joshua B. Tenenbaum (CoRR, 2021)

Explanatory models in neuroscience: Part 2 - constraint-based intelligibility. - Rosa Cao; Daniel Yamins (CoRR, 2021)

Physion: Evaluating Physical Prediction from Vision in Humans and Machines. - Daniel M. Bear; Elias Wang; Damian Mrowca; Felix J. Binder; Hsiau-Yu Fish Tung; R. T. Pramod; Cameron Holdaway; Sirui Tao; Kevin A. Smith 001; Fan-Yun Sun; Li Fei-Fei 001; Nancy Kanwisher; et al. (CoRR, 2021)

Rethinking the limiting dynamics of SGD: modified loss, phase space oscillations, and anomalous diffusion. - Daniel Kunin; Javier Sagastuy-Breña; Lauren E. Gillespie; Eshed Margalit; Hidenori Tanaka; Surya Ganguli; Daniel L. K. Yamins (CoRR, 2021)

Unsupervised neural network models of the ventral visual stream. - Chengxu Zhuang; Siming Yan; Aran Nayebi; Martin Schrimpf; Michael C. Frank; James J. DiCarlo; Daniel L. K. Yamins (Proc. Natl. Acad. Sci. USA, 2021)

FAR: End-to-End Vibrotactile Distributed System Designed to Facilitate Affect Regulation in Children Diagnosed with Autism Spectrum Disorder Through Slow Breathing. - Pardis Miri; Mehul Arora; Aman Malhotra; Robert Flory; Stephanie Hu; Ashley Lowber; Ishan Goyal; Jacqueline Nguyen; John P. Hegarty; Marlo D. Kohn; David Schneider; Heather Culbertson; et al. (CHI, 2022)

Unsupervised Segmentation in Real-World Images via Spelke Object Inference. - Honglin Chen; Rahul Mysore Venkatesh; Yoni Friedman; Jiajun Wu 001; Joshua B. Tenenbaum; Daniel L. K. Yamins; Daniel M. Bear (ECCV (29), 2022)

The ThreeDWorld Transport Challenge: A Visually Guided Task-and-Motion Planning Benchmark Towards Physically Realistic Embodied AI. - Chuang Gan 001; Siyuan Zhou; Jeremy Schwartz; Seth Alter; Abhishek Bhandwaldar; Dan Gutfreund; Daniel L. K. Yamins; James J. DiCarlo; Josh H. McDermott; Antonio Torralba 001; Joshua B. Tenenbaum (ICRA, 2022)

How Well Do Unsupervised Learning Algorithms Model Human Real-time and Life-long Learning? - Chengxu Zhuang; Ziyu Xiang; Yoon Bai; Xiaoxuan Jia; Nicholas B. Turk-Browne; Kenneth A. Norman; James J. DiCarlo; Dan Yamins (NeurIPS, 2022)

Recurrent Connections in the Primate Ventral Visual Stream Mediate a Trade-Off Between Task Performance and Network Size During Core Object Recognition. - Aran Nayebi; Javier Sagastuy-Breña; Daniel M. Bear; Kohitij Kar; Jonas Kubilius; Surya Ganguli; David Sussillo; James J. DiCarlo; Daniel L. K. Yamins (Neural Comput., 2022)

Advancing Cognitive Science and AI with Cognitive-AI Benchmarking. - Felix Jedidja Binder; Logan Matthew Cross; Yoni Friedman; Robert D. Hawkins; Daniel L. K. Yamins; Judith E. Fan (CogSci, 2023)

Developmental Curiosity and Social Interaction in Virtual Agents. - Chris Doyle; Sarah Shader; Michelle Lau; Megumi Sano; Daniel L. K. Yamins; Nick Haber (CogSci, 2023)

Measuring and Modeling Physical Intrinsic Motivation. - Julio Martinez; Felix Jedidja Binder; Haoliang Wang; Nick Haber; Judith E. Fan; Daniel Yamins (CogSci, 2023)

Are These the Same Apple? Comparing Images Based on Object Intrinsics. - Klemen Kotar; Stephen Tian; Hong-Xing Yu; Dan Yamins; Jiajun Wu 001 (NeurIPS, 2023)

Physion++: Evaluating Physical Scene Understanding that Requires Online Inference of Different Physical Properties. - Hsiao-Yu Tung; Mingyu Ding; Zhenfang Chen; Daniel Bear; Chuang Gan 001; Josh Tenenbaum 001; Dan Yamins; Judith E. Fan; Kevin A. Smith 001 (NeurIPS, 2023)

3D-IntPhys: Towards More Generalized 3D-grounded Visual Intuitive Physics under Challenging Scenes. - Haotian Xue; Antonio Torralba 001; Josh Tenenbaum 001; Dan Yamins; Yunzhu Li; Hsiao-Yu Tung (NeurIPS, 2023)

Unifying (Machine) Vision via Counterfactual World Modeling. - Daniel M. Bear; Kevin T. Feigelis; Honglin Chen; Wanhee Lee; Rahul Mysore Venkatesh; Klemen Kotar; Alex Durango; Daniel L. K. Yamins (CoRR, 2023)

Counterfactual World Modeling for Physical Dynamics Understanding. - Rahul Mysore Venkatesh; Honglin Chen; Kevin T. Feigelis; Daniel M. Bear; Khaled Jedoui; Klemen Kotar; Felix J. Binder; Wanhee Lee; Sherry Liu; Kevin A. Smith 001; Judith E. Fan; Daniel L. K. Yamins (CoRR, 2023)

Challenges in Evaluating Technological Interventions for Affect Regulation. - Pardis Miri; Horia Margarit; Andero Uusberg; Keith Marzullo; Tali M. Ball; Daniel Yamins; Robert Flory; James J. Gross (IEEE Trans. Affect. Comput., 2023)

Mouse visual cortex as a limited resource system that self-learns an ecologically-general representation. - Aran Nayebi; Nathan C. L. Kong; Chengxu Zhuang; Justin L. Gardner; Anthony M. Norcia; Daniel L. K. Yamins (PLoS Comput. Biol., 2023)

Understanding Physical Dynamics with Counterfactual World Modeling. - Rahul Mysore Venkatesh; Honglin Chen; Kevin T. Feigelis; Daniel M. Bear; Khaled Jedoui; Klemen Kotar; Felix J. Binder; Wanhee Lee; Sherry Liu; Kevin A. Smith 001; Judith E. Fan; Daniel L. K. Yamins (ECCV (24), 2024)

Explanatory models in neuroscience, Part 2: Functional intelligibility and the contravariance principle. - Rosa Cao; Daniel Yamins (Cogn. Syst. Res., 2024)

Explanatory models in neuroscience, Part 1: Taking mechanistic abstraction seriously. - Rosa Cao; Daniel Yamins (Cogn. Syst. Res., 2024)

The BabyView dataset: High-resolution egocentric videos of infants' and young children's everyday experiences. - Bria Long; Violet Xiang; Stefan Stojanov; Robert Z. Sparks; Zi Yin; Grace E. Keene; Alvin W. M. Tan; Steven Y. Feng; Chengxu Zhuang; Virginia A. Marchman; Daniel L. K. Yamins; Michael C. Frank (CoRR, 2024)

Unsupervised 3D Scene Representation Learning via Movable Object Inference. - Honglin Chen; Wanhee Lee; Hong-Xing Yu; Rahul Mysore Venkatesh; Joshua B. Tenenbaum; Daniel Bear; Jiajun Wu 001; Daniel L. K. Yamins (Trans. Mach. Learn. Res., 2024)

The Limiting Dynamics of SGD: Modified Loss, Phase-Space Oscillations, and Anomalous Diffusion. - Daniel Kunin; Javier Sagastuy-Breña; Lauren E. Gillespie; Eshed Margalit; Hidenori Tanaka; Surya Ganguli; Daniel L. K. Yamins (Neural Comput., 2024)

Hypothetical Minds: Scaffolding Theory of Mind for Multi-Agent Tasks with Large Language Models. - Logan Cross; Violet Xiang; Agam Bhatia; Daniel L. K. Yamins; Nick Haber (ICLR, 2025)

Self-Supervised Learning of Motion Concepts by Optimizing Counterfactuals. - Stefan Stojanov; David Wendt; Seungwoo Kim; Rahul Mysore Venkatesh; Kevin T. Feigelis; Jiajun Wu 001; Daniel L. K. Yamins (CoRR, 2025)

3D Scene Understanding Through Local Random Access Sequence Modeling. - Wanhee Lee; Klemen Kotar; Rahul Mysore Venkatesh; Jared Watrous; Honglin Chen; Khai Loong Aw; Daniel L. K. Yamins (CoRR, 2025)

Taming generative video models for zero-shot optical flow extraction. - Seungwoo Kim; Khai Loong Aw; Klemen Kotar; Cristóbal Eyzaguirre; Wanhee Lee; Yunong Liu; Jared Watrous; Stefan Stojanov; Juan Carlos Niebles; Jiajun Wu 001; Daniel L. K. Yamins (CoRR, 2025)

Discovering and using Spelke segments. - Rahul Mysore Venkatesh; Klemen Kotar; Lilian Naing Chen; Seungwoo Kim; Luca Thomas Wheeler; Jared Watrous; Ashley Xu; Gia Ancone; Wanhee Lee; Honglin Chen; Daniel Bear; Stefan Stojanov; et al. (CoRR, 2025)

Validating Generative Agent-Based Models of Social Norm Enforcement: From Replication to Novel Predictions. - Logan Matthew Cross; Nick Haber; Daniel L. K. Yamins (CoRR, 2025)

Representing Speech Through Autoregressive Prediction of Cochlear Tokens. - Greta Tuckute; Klemen Kotar; Evelina Fedorenko; Daniel L. K. Yamins (CoRR, 2025)