Daniel Yamins
Daniel Yamins
Assistant Professor of Computer Science and Psychology, Stanford University
Verified email at stanford.edu - Homepage
Cited by
Cited by
Performance-optimized hierarchical models predict neural responses in higher visual cortex
DLK Yamins, H Hong, CF Cadieu, EA Solomon, D Seibert, JJ DiCarlo
Proceedings of the national academy of sciences 111 (23), 8619-8624, 2014
Making a science of model search: Hyperparameter optimization in hundreds of dimensions for vision architectures
J Bergstra, D Yamins, D Cox
International conference on machine learning, 115-123, 2013
Using goal-driven deep learning models to understand sensory cortex
DLK Yamins, JJ DiCarlo
Nature neuroscience 19 (3), 356-365, 2016
Deep neural networks rival the representation of primate IT cortex for core visual object recognition
CF Cadieu, H Hong, DLK Yamins, N Pinto, D Ardila, EA Solomon, ...
PLoS computational biology 10 (12), e1003963, 2014
A task-optimized neural network replicates human auditory behavior, predicts brain responses, and reveals a cortical processing hierarchy
AJE Kell, DLK Yamins, EN Shook, SV Norman-Haignere, JH McDermott
Neuron 98 (3), 630-644. e16, 2018
A deep learning framework for neuroscience
BA Richards, TP Lillicrap, P Beaudoin, Y Bengio, R Bogacz, ...
Nature neuroscience 22 (11), 1761-1770, 2019
Explicit information for category-orthogonal object properties increases along the ventral stream
H Hong, DLK Yamins, NJ Majaj, JJ DiCarlo
Nature neuroscience 19 (4), 613-622, 2016
Local aggregation for unsupervised learning of visual embeddings
C Zhuang, AL Zhai, D Yamins
Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2019
Brain-score: Which artificial neural network for object recognition is most brain-like?
M Schrimpf, J Kubilius, H Hong, NJ Majaj, R Rajalingham, EB Issa, K Kar, ...
BioRxiv, 407007, 2018
Hierarchical modular optimization of convolutional networks achieves representations similar to macaque IT and human ventral stream
D Yamins, H Hong, C Cadieu, JJ DiCarlo
Neural Information Processing Systems Foundation, 2013
Dynamic Task Assignment in Robot Swarms.
J McLurkin, D Yamins
Robotics: Science and Systems 8 (2005), 2005
Flexible neural representation for physics prediction
D Mrowca, C Zhuang, E Wang, N Haber, L Fei-Fei, JB Tenenbaum, ...
arXiv preprint arXiv:1806.08047, 2018
Identification and Functional Validation of the Novel Antimalarial Resistance Locus PF10_0355 in Plasmodium falciparum
D Van Tyne, DJ Park, SF Schaffner, DE Neafsey, E Angelino, JF Cortese, ...
PLoS genetics 7 (4), e1001383, 2011
Growing urban roads
D Yamins, S Rasmussen, D Fogel
Networks and Spatial Economics 3 (1), 69-85, 2003
Task-driven convolutional recurrent models of the visual system
A Nayebi, D Bear, J Kubilius, K Kar, S Ganguli, D Sussillo, JJ DiCarlo, ...
arXiv preprint arXiv:1807.00053, 2018
Direct lung delivery of para-aminosalicylic acid by aerosol particles
N Tsapis, D Bennett, K O’Driscoll, K Shea, MM Lipp, K Fu, RW Clarke, ...
Tuberculosis 83 (6), 379-385, 2003
Learning to play with intrinsically-motivated self-aware agents
N Haber, D Mrowca, L Fei-Fei, DLK Yamins
arXiv preprint arXiv:1802.07442, 2018
Brain-like object recognition with high-performing shallow recurrent ANNs
J Kubilius, M Schrimpf, K Kar, H Hong, NJ Majaj, R Rajalingham, EB Issa, ...
arXiv preprint arXiv:1909.06161, 2019
Pruning neural networks without any data by iteratively conserving synaptic flow
H Tanaka, D Kunin, DLK Yamins, S Ganguli
arXiv preprint arXiv:2006.05467, 2020
Cornet: Modeling the neural mechanisms of core object recognition
J Kubilius, M Schrimpf, A Nayebi, D Bear, DLK Yamins, JJ DiCarlo
BioRxiv, 408385, 2018
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