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Simon Kornblith
Simon Kornblith
Google Brain
Zweryfikowany adres z google.com
Tytuł
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Cytowane przez
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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
98302020
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
13932020
When does label smoothing help?
R Müller, S Kornblith, G Hinton
Advances in Neural Information Processing Systems, 2019, 2019
13212019
Do better ImageNet models transfer better?
S Kornblith, J Shlens, QV Le
Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2019
10422019
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
6132019
Do vision transformers see like convolutional neural networks?
M Raghu, T Unterthiner, S Kornblith, C Zhang, A Dosovitskiy
Advances in Neural Information Processing Systems 34, 12116-12128, 2021
3232021
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
2822008
Big self-supervised models advance medical image classification
S Azizi, B Mustafa, F Ryan, Z Beaver, J Freyberg, J Deaton, A Loh, ...
Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2021
2272021
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
2092018
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
1772011
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
1682020
Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time
M Wortsman, G Ilharco, SY Gadre, R Roelofs, R Gontijo-Lopes, ...
International Conference on Machine Learning, 23965-23998, 2022
1442022
Robust fine-tuning of zero-shot models
M Wortsman, G Ilharco, JW Kim, M Li, S Kornblith, R Roelofs, RG Lopes, ...
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022
1252022
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
1232021
A network for scene processing in the macaque temporal lobe
S Kornblith, X Cheng, S Ohayon, DY Tsao
Neuron 79 (4), 766-781, 2013
1182013
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
1052018
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
952017
Stimulus load and oscillatory activity in higher cortex
S Kornblith, TJ Buschman, EK Miller
Cerebral Cortex 26 (9), 3772-3784, 2016
742016
Boosting contrastive self-supervised learning with false negative cancellation
T Huynh, S Kornblith, MR Walter, M Maire, M Khademi
Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2022
632022
Why do better loss functions lead to less transferable features?
S Kornblith, T Chen, H Lee, M Norouzi
Advances in Neural Information Processing Systems 34, 28648-28662, 2021
62*2021
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