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 | 3052 | 2013 |
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 | 2231 | 2014 |
Using goal-driven deep learning models to understand sensory cortex DLK Yamins, JJ DiCarlo Nature neuroscience 19 (3), 356-365, 2016 | 1757 | 2016 |
Hyperopt: A Python Library for Optimizing the Hyperparameters of Machine Learning Algorithms. J Bergstra, D Yamins, DD Cox SciPy 13, 20, 2013 | 999 | 2013 |
Hyperopt: a python library for model selection and hyperparameter optimization J Bergstra, B Komer, C Eliasmith, D Yamins, DD Cox Computational Science & Discovery 8 (1), 014008, 2015 | 932 | 2015 |
A deep learning framework for neuroscience BA Richards, TP Lillicrap, P Beaudoin, Y Bengio, R Bogacz, ... Nature neuroscience 22 (11), 1761-1770, 2019 | 921 | 2019 |
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 | 845 | 2014 |
Pruning neural networks without any data by iteratively conserving synaptic flow H Tanaka, D Kunin, DL Yamins, S Ganguli Advances in neural information processing systems 33, 6377-6389, 2020 | 672 | 2020 |
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 | 581 | 2018 |
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 | 558 | 2018 |
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 | 524 | 2019 |
Unsupervised neural network models of the ventral visual stream C Zhuang, S Yan, A Nayebi, M Schrimpf, MC Frank, JJ DiCarlo, ... Proceedings of the National Academy of Sciences 118 (3), e2014196118, 2021 | 380 | 2021 |
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 | 369 | 2016 |
Threedworld: A platform for interactive multi-modal physical simulation C Gan, J Schwartz, S Alter, D Mrowca, M Schrimpf, J Traer, J De Freitas, ... arXiv preprint arXiv:2007.04954, 2020 | 293 | 2020 |
Brain-like object recognition with high-performing shallow recurrent ANNs J Kubilius, M Schrimpf, K Kar, R Rajalingham, H Hong, N Majaj, E Issa, ... Advances in neural information processing systems 32, 2019 | 286 | 2019 |
Flexible neural representation for physics prediction D Mrowca, C Zhuang, E Wang, N Haber, LF Fei-Fei, J Tenenbaum, ... Advances in neural information processing systems 31, 2018 | 283 | 2018 |
Hierarchical modular optimization of convolutional networks achieves representations similar to macaque IT and human ventral stream DL Yamins, H Hong, C Cadieu, JJ DiCarlo Advances in neural information processing systems 26, 2013 | 202 | 2013 |
Task-driven convolutional recurrent models of the visual system A Nayebi, D Bear, J Kubilius, K Kar, S Ganguli, D Sussillo, JJ DiCarlo, ... Advances in neural information processing systems 31, 2018 | 179 | 2018 |
Cornet: Modeling the neural mechanisms of core object recognition J Kubilius, M Schrimpf, A Nayebi, D Bear, DLK Yamins, JJ DiCarlo BioRxiv, 408385, 2018 | 165 | 2018 |
Learning to play with intrinsically-motivated, self-aware agents N Haber, D Mrowca, S Wang, LF Fei-Fei, DL Yamins Advances in neural information processing systems 31, 2018 | 143 | 2018 |