Daniel Yamins

Publication Activity (10 Years)

Years Active: 2004-2024
Publications (10 Years): 24

Top Topics

Visual Representations

Object Representations

Image Modalities

Top Venues

Cogn. Syst. Res.

Publications

Rosa Cao, Daniel Yamins
Explanatory models in neuroscience, Part 2: Functional intelligibility and the contravariance principle. Cogn. Syst. Res. 85 (2024)
Rosa Cao, Daniel Yamins
Explanatory models in neuroscience, Part 1: Taking mechanistic abstraction seriously. Cogn. Syst. Res. 87 (2024)
Julio Martinez, Felix Jedidja Binder, Haoliang Wang, Nick Haber, Judith E. Fan, Daniel Yamins
Measuring and Modeling Physical Intrinsic Motivation. CogSci (2023)
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) (2023)
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)
Rosa Cao, Daniel Yamins
Explanatory models in neuroscience: Part 1 - taking mechanistic abstraction seriously. CoRR (2021)
Mike Wu, Milan Mosse, Chengxu Zhuang, Daniel Yamins, Noah D. Goodman
Conditional Negative Sampling for Contrastive Learning of Visual Representations. ICLR (2021)
Rosa Cao, Daniel Yamins
Explanatory models in neuroscience: Part 2 - constraint-based intelligibility. CoRR (2021)
Kuno Kim, Megumi Sano, Julian De Freitas, Nick Haber, Daniel Yamins
Active World Model Learning with Progress Curiosity. ICML (2020)
Aidan Curtis, Minjian Xin, Dilip Arumugam, Kevin T. Feigelis, Daniel Yamins
Flexible and Efficient Long-Range Planning Through Curious Exploration. ICML (2020)
Mike Wu, Milan Mosse, Chengxu Zhuang, Daniel Yamins, Noah D. Goodman
Conditional Negative Sampling for Contrastive Learning of Visual Representations. CoRR (2020)
Mike Wu, Chengxu Zhuang, Milan Mosse, Daniel Yamins, Noah D. Goodman
On Mutual Information in Contrastive Learning for Visual Representations. CoRR (2020)
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)
Kuno Kim, Megumi Sano, Julian De Freitas, Nick Haber, Daniel Yamins
Active World Model Learning with Progress Curiosity. CoRR (2020)
Aidan Curtis, Minjian Xin, Dilip Arumugam, Kevin T. Feigelis, Daniel Yamins
Flexible and Efficient Long-Range Planning Through Curious Exploration. CoRR (2020)
Chengxu Zhuang, Tianwei She, Alex Andonian, Max Sobol Mark, Daniel Yamins
Unsupervised Learning From Video With Deep Neural Embeddings. CVPR (2020)
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)
Chengxu Zhuang, Xuehao Ding, Divyanshu Murli, Daniel Yamins
Local Label Propagation for Large-Scale Semi-Supervised Learning. CoRR (2019)
Chengxu Zhuang, Alex Lin Zhai, Daniel Yamins
Local Aggregation for Unsupervised Learning of Visual Embeddings. ICCV (2019)
Chengxu Zhuang, Alex Lin Zhai, Daniel Yamins
Local Aggregation for Unsupervised Learning of Visual Embeddings. CoRR (2019)
Chengxu Zhuang, Alex Andonian, Daniel Yamins
Unsupervised Learning from Video with Deep Neural Embeddings. CoRR (2019)
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 (2017)
Chengxu Zhuang, Jonas Kubilius, Mitra J. Z. Hartmann, Daniel Yamins
Toward Goal-Driven Neural Network Models for the Rodent Whisker-Trigeminal System. CoRR (2017)
Judith E. Fan, Daniel Yamins, Nicholas B. Turk-Browne
Common object representations for visual recognition and production. CogSci (2015)
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 (2014)
Judith E. Fan, Daniel Yamins, James J. DiCarlo, Nicholas B. Turk-Browne
Mapping core similarity among visual objects across image modalities. SIGGRAPH Posters (2014)
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)
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)
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)
Elaine Angelino, Daniel Yamins, Margo I. Seltzer
StarFlow: A Script-Centric Data Analysis Environment. IPAW (2010)
Daniel Yamins, Radhika Nagpal
Automated global-to-local programming in 1-D spatial multi-agent systems. AAMAS (2) (2008)
Radhika Nagpal, Chih-Han Yu, Daniel Yamins
Engineering self-organizing multi-agent systems. AAMAS (Demos) (2008)
Daniel Yamins
The emergence of global properties from local interactions: static properties and one-dimensional patterns. AAMAS (2006)
Daniel Yamins
Towards a theory of "local to global" in distributed multi-agent systems (II). AAMAS (2005)
James McLurkin, Daniel Yamins
Dynamic Task Assignment in Robot Swarms. Robotics: Science and Systems (2005)
Daniel Yamins
Towards a theory of "local to global" in distributed multi-agent systems (I). AAMAS (2005)
Daniel Yamins, Stephen Waydo, Navin Khaneja
Group control and kernels: the 1-d equigrouping problem. CDC (2004)