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ORCIDDan Yamins
Daniel Yamins – Daniel L. K. Yamins
Person information
- affiliation: Stanford University, Department of Psychology, CA, USA
- affiliation: Massachusetts Institute of Technology (MIT), McGovern Institute for Brain Research, Cambridge, MA, USA
- affiliation: Harvard University, School of Engineering and Applied Sciences, Cambridge, MA, USA
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2020 – today
- 2024
[j11]Rosa Cao
, Daniel Yamins:
Explanatory models in neuroscience, Part 2: Functional intelligibility and the contravariance principle. Cogn. Syst. Res. 85: 101200 (2024)- [j10]Rosa Cao, Daniel Yamins:
Explanatory models in neuroscience, Part 1: Taking mechanistic abstraction seriously. Cogn. Syst. Res. 87: 101244 (2024) - [j9]Daniel Kunin, Javier Sagastuy-Breña, Lauren E. Gillespie, Eshed Margalit, Hidenori Tanaka, Surya Ganguli, Daniel L. K. Yamins:
The Limiting Dynamics of SGD: Modified Loss, Phase-Space Oscillations, and Anomalous Diffusion. Neural Comput. 36(1): 151-174 (2024) - [j8]Honglin Chen, Wanhee Lee, Hong-Xing Yu, Rahul Mysore Venkatesh, Joshua B. Tenenbaum, Daniel Bear, Jiajun Wu, Daniel L. K. Yamins:
Unsupervised 3D Scene Representation Learning via Movable Object Inference. Trans. Mach. Learn. Res. 2024 (2024) - [c50]Rahul Venkatesh, Honglin Chen, Kevin T. Feigelis, Daniel M. Bear, Khaled Jedoui, Klemen Kotar, Felix J. Binder, Wanhee Lee, Sherry Liu, Kevin A. Smith, Judith E. Fan, Daniel L. K. Yamins:
Understanding Physical Dynamics with Counterfactual World Modeling. ECCV (24) 2024: 368-387 - [i39]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:
The BabyView dataset: High-resolution egocentric videos of infants' and young children's everyday experiences. CoRR abs/2406.10447 (2024) - [i38]Logan Cross, Violet Xiang, Agam Bhatia, Daniel L. K. Yamins, Nick Haber:
Hypothetical Minds: Scaffolding Theory of Mind for Multi-Agent Tasks with Large Language Models. CoRR abs/2407.07086 (2024) - 2023
- [j7]Aran Nayebi, Nathan C. L. Kong, Chengxu Zhuang, Justin L. Gardner, Anthony M. Norcia, Daniel L. K. Yamins:
Mouse visual cortex as a limited resource system that self-learns an ecologically-general representation. PLoS Comput. Biol. 19(10) (2023) - [j6]Pardis Miri, Horia Margarit, Andero Uusberg, Keith Marzullo, Tali M. Ball, Daniel Yamins, Robert Flory, James J. Gross:
Challenges in Evaluating Technological Interventions for Affect Regulation. IEEE Trans. Affect. Comput. 14(3): 2430-2442 (2023) - [c49]Felix Jedidja Binder, Logan Matthew Cross, Yoni Friedman, Robert D. Hawkins, Daniel L. K. Yamins, Judith E. Fan:
Advancing Cognitive Science and AI with Cognitive-AI Benchmarking. CogSci 2023 - [c48]Chris Doyle, Sarah Shader, Michelle Lau, Megumi Sano, Daniel L. K. Yamins, Nick Haber:
Developmental Curiosity and Social Interaction in Virtual Agents. CogSci 2023 - [c47]Julio Martinez, Felix Jedidja Binder, Haoliang Wang, Nick Haber, Judith E. Fan, Daniel Yamins:
Measuring and Modeling Physical Intrinsic Motivation. CogSci 2023 - [c46]Haotian Xue, Antonio Torralba, Joshua B. Tenenbaum, Daniel Yamins, Yunzhu Li, Hsiao-Yu Tung:
3D-IntPhys: Towards More Generalized 3D-grounded Visual Intuitive Physics under Challenging Scenes. CVPR Workshops 2023: 3625-3635 - [c45]Klemen Kotar, Stephen Tian, Hong-Xing Yu, Dan Yamins, Jiajun Wu:
Are These the Same Apple? Comparing Images Based on Object Intrinsics. NeurIPS 2023 - [c44]Hsiao-Yu Tung, Mingyu Ding, Zhenfang Chen, Daniel Bear, Chuang Gan, Josh Tenenbaum, Dan Yamins, Judith E. Fan, Kevin A. Smith:
Physion++: Evaluating Physical Scene Understanding that Requires Online Inference of Different Physical Properties. NeurIPS 2023 - [c43]Haotian Xue, Antonio Torralba, Josh Tenenbaum, Dan Yamins, Yunzhu Li, Hsiao-Yu Tung:
3D-IntPhys: Towards More Generalized 3D-grounded Visual Intuitive Physics under Challenging Scenes. NeurIPS 2023 - [i37]Haotian Xue, Antonio Torralba, Joshua B. Tenenbaum, Daniel L. K. Yamins, Yunzhu Li, Hsiao-Yu Tung:
3D-IntPhys: Towards More Generalized 3D-grounded Visual Intuitive Physics under Challenging Scenes. CoRR abs/2304.11470 (2023) - [i36]Chris Doyle, Sarah Shader, Michelle Lau, Megumi Sano, Daniel L. K. Yamins, Nick Haber:
Developmental Curiosity and Social Interaction in Virtual Agents. CoRR abs/2305.13396 (2023) - [i35]Julio Martinez, Felix J. Binder, Haoliang Wang, Nick Haber, Judith E. Fan, Daniel L. K. Yamins:
Measuring and Modeling Physical Intrinsic Motivation. CoRR abs/2305.13452 (2023) - [i34]Daniel M. Bear, Kevin T. Feigelis, Honglin Chen, Wanhee Lee, Rahul Venkatesh, Klemen Kotar, Alex Durango, Daniel L. K. Yamins:
Unifying (Machine) Vision via Counterfactual World Modeling. CoRR abs/2306.01828 (2023) - [i33]Hsiao-Yu Tung, Mingyu Ding, Zhenfang Chen, Daniel Bear, Chuang Gan, Joshua B. Tenenbaum, Daniel L. K. Yamins, Judith E. Fan, Kevin A. Smith:
Physion++: Evaluating Physical Scene Understanding that Requires Online Inference of Different Physical Properties. CoRR abs/2306.15668 (2023) - [i32]Klemen Kotar, Stephen Tian, Hong-Xing Yu, Daniel L. K. Yamins, Jiajun Wu:
Are These the Same Apple? Comparing Images Based on Object Intrinsics. CoRR abs/2311.00750 (2023) - [i31]Rahul Venkatesh, Honglin Chen, Kevin T. Feigelis, Daniel M. Bear, Khaled Jedoui, Klemen Kotar, Felix J. Binder, Wanhee Lee, Sherry Liu, Kevin A. Smith, Judith E. Fan, Daniel L. K. Yamins:
Counterfactual World Modeling for Physical Dynamics Understanding. CoRR abs/2312.06721 (2023) - 2022
- [j5]Aran Nayebi, Javier Sagastuy-Breña, Daniel M. Bear, Kohitij Kar, Jonas Kubilius, Surya Ganguli, David Sussillo, James J. DiCarlo, Daniel L. K. Yamins:
Recurrent Connections in the Primate Ventral Visual Stream Mediate a Trade-Off Between Task Performance and Network Size During Core Object Recognition. Neural Comput. 34(8): 1652-1675 (2022) - [c42]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, Daniel L. K. Yamins, Lawrence Fung, Antonio Hardan, James J. Gross, Keith Marzullo:
FAR: End-to-End Vibrotactile Distributed System Designed to Facilitate Affect Regulation in Children Diagnosed with Autism Spectrum Disorder Through Slow Breathing. CHI 2022: 168:1-168:20 - [c41]Honglin Chen, Rahul Venkatesh, Yoni Friedman, Jiajun Wu, Joshua B. Tenenbaum, Daniel L. K. Yamins, Daniel M. Bear:
Unsupervised Segmentation in Real-World Images via Spelke Object Inference. ECCV (29) 2022: 719-735 - [c40]Chuang Gan, Siyuan Zhou, Jeremy Schwartz, Seth Alter, Abhishek Bhandwaldar, Dan Gutfreund, Daniel L. K. Yamins, James J. DiCarlo, Josh H. McDermott, Antonio Torralba, Joshua B. Tenenbaum:
The ThreeDWorld Transport Challenge: A Visually Guided Task-and-Motion Planning Benchmark Towards Physically Realistic Embodied AI. ICRA 2022: 8847-8854 - [c39]Chengxu Zhuang, Ziyu Xiang, Yoon Bai, Xiaoxuan Jia, Nicholas B. Turk-Browne, Kenneth A. Norman, James J. DiCarlo, Dan Yamins:
How Well Do Unsupervised Learning Algorithms Model Human Real-time and Life-long Learning? NeurIPS 2022 - [i30]Honglin Chen, Rahul Venkatesh, Yoni Friedman, Jiajun Wu, Joshua B. Tenenbaum, Daniel L. K. Yamins, Daniel M. Bear:
Unsupervised Segmentation in Real-World Images via Spelke Object Inference. CoRR abs/2205.08515 (2022) - 2021
- [j4]Chengxu Zhuang, Siming Yan, Aran Nayebi, Martin Schrimpf, Michael C. Frank, James J. DiCarlo, Daniel L. K. Yamins:
Unsupervised neural network models of the ventral visual stream. Proc. Natl. Acad. Sci. USA 118(3): e2014196118 (2021) - [c38]Cameron Holdaway, Daniel M. Bear, Samaher Radwan, Michael C. Frank, Daniel L. K. Yamins, Judith E. Fan:
Measuring and predicting variation in the interestingness of physical structures. CogSci 2021 - [c37]George Kachergis, Samaher Radwan, Bria Long, Judith E. Fan, Michael Lingelbach, Daniel M. Bear, Daniel L. K. Yamins, Michael C. Frank:
Predicting children's and adults' preferences in physical interactions via physics simulation. CogSci 2021 - [c36]Daniel Kunin, Javier Sagastuy-Breña, Surya Ganguli, Daniel L. K. Yamins, Hidenori Tanaka:
Neural Mechanics: Symmetry and Broken Conservation Laws in Deep Learning Dynamics. ICLR 2021 - [c35]Mike Wu, Milan Mosse, Chengxu Zhuang, Daniel Yamins, Noah D. Goodman:
Conditional Negative Sampling for Contrastive Learning of Visual Representations. ICLR 2021 - [c34]Daniel Bear, Elias Wang, Damian Mrowca, Felix J. Binder, Hsiao-Yu Tung, R. T. Pramod, Cameron Holdaway, Sirui Tao, Kevin A. Smith, Fan-Yun Sun, Fei-Fei Li, Nancy Kanwisher, Josh Tenenbaum, Dan Yamins, Judith E. Fan:
Physion: Evaluating Physical Prediction from Vision in Humans and Machines. NeurIPS Datasets and Benchmarks 2021 - [c33]Chuang Gan, Jeremy Schwartz, Seth Alter, Damian Mrowca, Martin Schrimpf, James Traer, Julian De Freitas, Jonas Kubilius, Abhishek Bhandwaldar, Nick Haber, Megumi Sano, Kuno Kim, Elias Wang, Michael Lingelbach, Aidan Curtis, Kevin T. Feigelis, Daniel Bear, Dan Gutfreund, David D. Cox, Antonio Torralba, James J. DiCarlo, Josh Tenenbaum, Josh H. McDermott, Dan Yamins:
ThreeDWorld: A Platform for Interactive Multi-Modal Physical Simulation. NeurIPS Datasets and Benchmarks 2021 - [c32]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:
Explaining heterogeneity in medial entorhinal cortex with task-driven neural networks. NeurIPS 2021: 12167-12179 - [i29]Chuang Gan, Siyuan Zhou, Jeremy Schwartz, Seth Alter, Abhishek Bhandwaldar, Dan Gutfreund, Daniel L. K. Yamins, James J. DiCarlo, Josh H. McDermott, Antonio Torralba, Joshua B. Tenenbaum:
The ThreeDWorld Transport Challenge: A Visually Guided Task-and-Motion Planning Benchmark for Physically Realistic Embodied AI. CoRR abs/2103.14025 (2021) - [i28]Rosa Cao, Daniel Yamins:
Explanatory models in neuroscience: Part 2 - constraint-based intelligibility. CoRR abs/2104.01489 (2021) - [i27]Rosa Cao, Daniel Yamins:
Explanatory models in neuroscience: Part 1 - taking mechanistic abstraction seriously. CoRR abs/2104.01490 (2021) - [i26]Daniel M. Bear, Elias Wang, Damian Mrowca, Felix J. Binder, Hsiau-Yu Fish Tung, R. T. Pramod, Cameron Holdaway, Sirui Tao, Kevin A. Smith, Fan-Yun Sun, Li Fei-Fei, Nancy Kanwisher, Joshua B. Tenenbaum, Daniel L. K. Yamins, Judith E. Fan:
Physion: Evaluating Physical Prediction from Vision in Humans and Machines. CoRR abs/2106.08261 (2021) - [i25]Daniel Kunin, Javier Sagastuy-Breña, Lauren E. Gillespie, Eshed Margalit, Hidenori Tanaka, Surya Ganguli, Daniel L. K. Yamins:
Rethinking the limiting dynamics of SGD: modified loss, phase space oscillations, and anomalous diffusion. CoRR abs/2107.09133 (2021) - 2020
- [c31]Megumi Sano, Julian De Freitas, Nick Haber, Daniel L. K. Yamins:
Learning in Social Environments with Curious Neural Agents. CogSci 2020 - [c30]Chengxu Zhuang, Tianwei She, Alex Andonian, Max Sobol Mark, Daniel Yamins:
Unsupervised Learning From Video With Deep Neural Embeddings. CVPR 2020: 9560-9569 - [c29]Aidan Curtis, Minjian Xin, Dilip Arumugam, Kevin T. Feigelis, Daniel Yamins:
Flexible and Efficient Long-Range Planning Through Curious Exploration. ICML 2020: 2238-2249 - [c28]Kuno Kim, Megumi Sano, Julian De Freitas, Nick Haber, Daniel Yamins:
Active World Model Learning with Progress Curiosity. ICML 2020: 5306-5315 - [c27]Daniel Kunin, Aran Nayebi, Javier Sagastuy-Breña, Surya Ganguli, Jonathan M. Bloom, Daniel Yamins:
Two Routes to Scalable Credit Assignment without Weight Symmetry. ICML 2020: 5511-5521 - [c26]Yunzhu Li, Toru Lin, Kexin Yi, Daniel Bear, Daniel Yamins, Jiajun Wu, Joshua B. Tenenbaum, Antonio Torralba:
Visual Grounding of Learned Physical Models. ICML 2020: 5927-5936 - [c25]Daniel Bear, Chaofei Fan, Damian Mrowca, Yunzhu Li, Seth Alter, Aran Nayebi, Jeremy Schwartz, Li Fei-Fei, Jiajun Wu, Josh Tenenbaum, Daniel L. K. Yamins:
Learning Physical Graph Representations from Visual Scenes. NeurIPS 2020 - [c24]Aran Nayebi, Sanjana Srivastava, Surya Ganguli, Daniel L. K. Yamins:
Identifying Learning Rules From Neural Network Observables. NeurIPS 2020 - [c23]Hidenori Tanaka, Daniel Kunin, Daniel L. K. Yamins, Surya Ganguli:
Pruning neural networks without any data by iteratively conserving synaptic flow. NeurIPS 2020 - [i24]Daniel Kunin, Aran Nayebi, Javier Sagastuy-Breña, Surya Ganguli, Jonathan M. Bloom, Daniel L. K. Yamins:
Two Routes to Scalable Credit Assignment without Weight Symmetry. CoRR abs/2003.01513 (2020) - [i23]Aidan Curtis, Minjian Xin, Dilip Arumugam, Kevin T. Feigelis, Daniel Yamins:
Flexible and Efficient Long-Range Planning Through Curious Exploration. CoRR abs/2004.10876 (2020) - [i22]Yunzhu Li, Toru Lin, Kexin Yi, Daniel Bear, Daniel L. K. Yamins, Jiajun Wu, Joshua B. Tenenbaum, Antonio Torralba:
Visual Grounding of Learned Physical Models. CoRR abs/2004.13664 (2020) - [i21]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) - [i20]Hidenori Tanaka, Daniel Kunin, Daniel L. K. Yamins, Surya Ganguli:
Pruning neural networks without any data by iteratively conserving synaptic flow. CoRR abs/2006.05467 (2020) - [i19]Daniel M. Bear, Chaofei Fan, Damian Mrowca, Yunzhu Li, Seth Alter, Aran Nayebi, Jeremy Schwartz, Li Fei-Fei, Jiajun Wu, Joshua B. Tenenbaum, Daniel L. K. Yamins:
Learning Physical Graph Representations from Visual Scenes. CoRR abs/2006.12373 (2020) - [i18]Chuang Gan, Jeremy Schwartz, Seth Alter, Martin Schrimpf, James Traer, Julian De Freitas, Jonas Kubilius, Abhishek Bhandwaldar, Nick Haber, Megumi Sano, Kuno Kim, Elias Wang, Damian Mrowca, Michael Lingelbach, Aidan Curtis, Kevin T. Feigelis, Daniel M. Bear, Dan Gutfreund, David D. Cox, James J. DiCarlo, Josh H. McDermott, Joshua B. Tenenbaum, Daniel L. K. Yamins:
ThreeDWorld: A Platform for Interactive Multi-Modal Physical Simulation. CoRR abs/2007.04954 (2020) - [i17]Kuno Kim, Megumi Sano, Julian De Freitas, Nick Haber, Daniel Yamins:
Active World Model Learning with Progress Curiosity. CoRR abs/2007.07853 (2020) - [i16]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) - [i15]Aran Nayebi, Sanjana Srivastava, Surya Ganguli, Daniel L. K. Yamins:
Identifying Learning Rules From Neural Network Observables. CoRR abs/2010.11765 (2020) - [i14]Daniel Kunin, Javier Sagastuy-Breña, Surya Ganguli, Daniel L. K. Yamins, Hidenori Tanaka:
Neural Mechanics: Symmetry and Broken Conservation Laws in Deep Learning Dynamics. CoRR abs/2012.04728 (2020)
2010 – 2019
- 2019
- [c22]Chengxu Zhuang, Alex Lin Zhai, Daniel Yamins:
Local Aggregation for Unsupervised Learning of Visual Embeddings. ICCV 2019: 6001-6011 - [c21]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, Daniel L. K. Yamins, James J. DiCarlo:
Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNs. NeurIPS 2019: 12785-12796 - [i13]Chengxu Zhuang, Alex Lin Zhai, Daniel Yamins:
Local Aggregation for Unsupervised Learning of Visual Embeddings. CoRR abs/1903.12355 (2019) - [i12]Chengxu Zhuang, Xuehao Ding, Divyanshu Murli, Daniel Yamins:
Local Label Propagation for Large-Scale Semi-Supervised Learning. CoRR abs/1905.11581 (2019) - [i11]Chengxu Zhuang, Alex Andonian, Daniel Yamins:
Unsupervised Learning from Video with Deep Neural Embeddings. CoRR abs/1905.11954 (2019) - [i10]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, Daniel L. K. Yamins, James J. DiCarlo:
Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNs. CoRR abs/1909.06161 (2019) - 2018
- [j3]Judith E. Fan, Daniel L. K. Yamins, Nicholas B. Turk-Browne:
Common Object Representations for Visual Production and Recognition. Cogn. Sci. 42(8): 2670-2698 (2018) - [c20]Nick Haber, Damian Mrowca, Li Fei-Fei, Daniel L. K. Yamins:
Emergence of Structured Behaviors from Curiosity-Based Intrinsic Motivation. CogSci 2018 - [c19]Kevin T. Feigelis, Blue Sheffer, Daniel L. K. Yamins:
Modular Continual Learning in a Unified Visual Environment. ICLR (Poster) 2018 - [c18]Aran Nayebi, Daniel Bear, Jonas Kubilius, Kohitij Kar, Surya Ganguli, David Sussillo, James J. DiCarlo, Daniel L. K. Yamins:
Task-Driven Convolutional Recurrent Models of the Visual System. NeurIPS 2018: 5295-5306 - [c17]Nick Haber, Damian Mrowca, Stephanie Wang, Li Fei-Fei, Daniel L. K. Yamins:
Learning to Play With Intrinsically-Motivated, Self-Aware Agents. NeurIPS 2018: 8398-8409 - [c16]Damian Mrowca, Chengxu Zhuang, Elias Wang, Nick Haber, Li Fei-Fei, Josh Tenenbaum, Daniel L. K. Yamins:
Flexible neural representation for physics prediction. NeurIPS 2018: 8813-8824 - [i9]Nick Haber, Damian Mrowca, Li Fei-Fei, Daniel L. K. Yamins:
Learning to Play with Intrinsically-Motivated Self-Aware Agents. CoRR abs/1802.07442 (2018) - [i8]Nick Haber, Damian Mrowca, Li Fei-Fei, Daniel L. K. Yamins:
Emergence of Structured Behaviors from Curiosity-Based Intrinsic Motivation. CoRR abs/1802.07461 (2018) - [i7]Damian Mrowca, Chengxu Zhuang, Elias Wang, Nick Haber, Li Fei-Fei, Joshua B. Tenenbaum, Daniel L. K. Yamins:
Flexible Neural Representation for Physics Prediction. CoRR abs/1806.08047 (2018) - [i6]Aran Nayebi, Daniel Bear, Jonas Kubilius, Kohitij Kar, Surya Ganguli, David Sussillo, James J. DiCarlo, Daniel L. K. Yamins:
Task-Driven Convolutional Recurrent Models of the Visual System. CoRR abs/1807.00053 (2018) - 2017
- [j2]Chengxu Zhuang, Yulong Wang, Daniel Yamins, Xiaolin Hu:
Deep Learning Predicts Correlation between a Functional Signature of Higher Visual Areas and Sparse Firing of Neurons. Frontiers Comput. Neurosci. 11: 100 (2017) - [c15]Chengxu Zhuang, Jonas Kubilius, Mitra J. Z. Hartmann, Daniel Yamins:
Toward Goal-Driven Neural Network Models for the Rodent Whisker-Trigeminal System. NIPS 2017: 2555-2565 - [i5]Kevin T. Feigelis, Daniel L. K. Yamins:
A Useful Motif for Flexible Task Learning in an Embodied Two-Dimensional Visual Environment. CoRR abs/1706.07147 (2017) - [i4]Chengxu Zhuang, Jonas Kubilius, Mitra J. Z. Hartmann, Daniel Yamins:
Toward Goal-Driven Neural Network Models for the Rodent Whisker-Trigeminal System. CoRR abs/1706.07555 (2017) - [i3]Kevin T. Feigelis, Blue Sheffer, Daniel L. K. Yamins:
Modular Continual Learning in a Unified Visual Environment. CoRR abs/1711.07425 (2017) - 2015
- [c14]Judith E. Fan, Daniel Yamins, Nicholas B. Turk-Browne:
Common object representations for visual recognition and production. CogSci 2015 - 2014
- [j1]Charles F. Cadieu, Ha Hong, Daniel Yamins, Nicolas Pinto, Diego Ardila, Ethan A. Solomon, Najib J. Majaj, James J. DiCarlo:
Deep Neural Networks Rival the Representation of Primate IT Cortex for Core Visual Object Recognition. PLoS Comput. Biol. 10(12) (2014) - [c13]Judith E. Fan, Daniel Yamins, James J. DiCarlo, Nicholas B. Turk-Browne:
Mapping core similarity among visual objects across image modalities. SIGGRAPH Posters 2014: 67:1 - [i2]Charles F. Cadieu, Ha Hong, Daniel Yamins, Nicolas Pinto, Diego Ardila, Ethan A. Solomon, Najib J. Majaj, James J. DiCarlo:
Deep Neural Networks Rival the Representation of Primate IT Cortex for Core Visual Object Recognition. CoRR abs/1406.3284 (2014) - 2013
- [c12]James Bergstra, Daniel Yamins, David D. Cox:
Making a Science of Model Search: Hyperparameter Optimization in Hundreds of Dimensions for Vision Architectures. ICML (1) 2013: 115-123 - [c11]Daniel Yamins, Ha Hong, Charles F. Cadieu, James J. DiCarlo:
Hierarchical Modular Optimization of Convolutional Networks Achieves Representations Similar to Macaque IT and Human Ventral Stream. NIPS 2013: 3093-3101 - [c10]James Bergstra, Dan Yamins, David D. Cox:
Hyperopt: A Python Library for Optimizing the Hyperparameters of Machine Learning Algorithms. SciPy 2013: 13-19 - [c9]Charles F. Cadieu, Ha Hong, Dan Yamins, Nicolas Pinto, Najib J. Majaj, James J. DiCarlo:
The Neural Representation Benchmark and its Evaluation on Brain and Machine. ICLR 2013 - 2012
- [i1]James Bergstra, Dan Yamins, David D. Cox:
Making a Science of Model Search. CoRR abs/1209.5111 (2012) - 2010
- [c8]Elaine Angelino, Daniel Yamins, Margo I. Seltzer:
StarFlow: A Script-Centric Data Analysis Environment. IPAW 2010: 236-250
2000 – 2009
- 2008
- [c7]Daniel Yamins, Radhika Nagpal:
Automated global-to-local programming in 1-D spatial multi-agent systems. AAMAS (2) 2008: 615-622 - [c6]Radhika Nagpal, Chih-Han Yu, Daniel Yamins:
Engineering self-organizing multi-agent systems. AAMAS (Demos) 2008: 1717 - 2007
- [e1]Sven Brueckner, Salima Hassas, Márk Jelasity, Daniel Yamins:
Engineering Self-Organising Systems, 4th International Workshop, ESOA 2006, Hakodate, Japan, May 9, 2006, Revised and Invited Papers. Lecture Notes in Computer Science 4335, Springer 2007, ISBN 978-3-540-69867-8 [contents] - 2006
- [c5]Daniel Yamins:
The emergence of global properties from local interactions: static properties and one-dimensional patterns. AAMAS 2006: 1122-1124 - 2005
- [c4]Daniel Yamins:
Towards a theory of "local to global" in distributed multi-agent systems (I). AAMAS 2005: 183-190 - [c3]Daniel Yamins:
Towards a theory of "local to global" in distributed multi-agent systems (II). AAMAS 2005: 191-198 - [c2]James McLurkin, Daniel Yamins:
Dynamic Task Assignment in Robot Swarms. Robotics: Science and Systems 2005: 129-136 - 2004
- [c1]Daniel Yamins, Stephen Waydo, Navin Khaneja:
Group control and kernels: the 1-d equigrouping problem. CDC 2004: 2460-2466
Coauthor Index
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