Simon Kornblith
Simon Kornblith
Google Brain
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A simple framework for contrastive learning of visual representations
T Chen, S Kornblith, M Norouzi, G Hinton
Proceedings of the 37th International Conference on Machine Learning, 2020
Do better ImageNet models transfer better?
S Kornblith, J Shlens, QV Le
Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2019
When does label smoothing help?
R Müller, S Kornblith, G Hinton
Advances in Neural Information Processing Systems, 2019, 2019
Big self-supervised models are strong semi-supervised learners
T Chen, S Kornblith, K Swersky, M Norouzi, G Hinton
Advances in Neural Information Processing Systems 33, 2020
Latency and selectivity of single neurons indicate hierarchical processing in the human medial temporal lobe
F Mormann, S Kornblith, RQ Quiroga, A Kraskov, M Cerf, I Fried, C Koch
Journal of Neuroscience 28 (36), 8865-8872, 2008
Similarity of neural network representations revisited
S Kornblith, M Norouzi, H Lee, G Hinton
Proceedings of the 36th International Conference on Machine Learning 97 …, 2019
A category-specific response to animals in the right human amygdala
F Mormann, J Dubois, S Kornblith, M Milosavljevic, M Cerf, M Ison, ...
Nature Neuroscience 14 (10), 1247-1249, 2011
Laminar recordings in frontal cortex suggest distinct layers for maintenance and control of working memory
AM Bastos, R Loonis, S Kornblith, M Lundqvist, EK Miller
Proceedings of the National Academy of Sciences 115 (5), 1117-1122, 2018
A network for scene processing in the macaque temporal lobe
S Kornblith, X Cheng, S Ohayon, DY Tsao
Neuron 79 (4), 766-781, 2013
Domain adaptive transfer learning with specialist models
J Ngiam, D Peng, V Vasudevan, S Kornblith, QV Le, R Pang
arXiv preprint arXiv:1811.07056, 2018
Stimulus load and oscillatory activity in higher cortex
S Kornblith, TJ Buschman, EK Miller
Cerebral Cortex 26 (9), 3772-3784, 2016
Persistent single-neuron activity during working memory in the human medial temporal lobe
S Kornblith, RQ Quiroga, C Koch, I Fried, F Mormann
Current Biology, 2017
The origins and prevalence of texture bias in convolutional neural networks
K Hermann, T Chen, S Kornblith
Advances in Neural Information Processing Systems 33, 2020
Big self-supervised models advance medical image classification
S Azizi, B Mustafa, F Ryan, Z Beaver, J Freyberg, J Deaton, A Loh, ...
arXiv preprint arXiv:2101.05224, 2021
Scene-selective coding by single neurons in the human parahippocampal cortex
F Mormann*, S Kornblith*, M Cerf, MJ Ison, A Kraskov, M Tran, S Knieling, ...
Proceedings of the National Academy of Sciences 114 (5), 1153-1158, 2017
Saccader: Improving accuracy of hard attention models for vision
GF Elsayed, S Kornblith, QV Le
Advances in Neural Information Processing Systems, 2019, 2019
Do wide and deep networks learn the same things? Uncovering how neural network representations vary with width and depth
T Nguyen, M Raghu, S Kornblith
International Conference on Learning Representations, 2021
How thoughts arise from sights: inferotemporal and prefrontal contributions to vision
S Kornblith, DY Tsao
Current Opinion in Neurobiology 46, 208-218, 2017
Custom-fit radiolucent cranial implants for neurophysiological recording and stimulation
GH Mulliken, NP Bichot, A Ghadooshahy, J Sharma, S Kornblith, ...
Journal of neuroscience methods 241, 146-154, 2015
Do Vision Transformers See Like Convolutional Neural Networks?
M Raghu, T Unterthiner, S Kornblith, C Zhang, A Dosovitskiy
Thirty-Fifth Conference on Neural Information Processing Systems, 2021
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