Simon Kornblith

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Mohammad NorouziGoogle BrainVerified email at google.com
Geoffrey HintonEmeritus Prof. Comp Sci, U.Toronto & Engineering Fellow, GoogleVerified email at cs.toronto.edu
Ting ChenGoogle BrainVerified email at google.com
Quoc V. LeResearch Scientist, Google BrainVerified email at stanford.edu
Rafael Rios MüllerTwitterVerified email at twitter.com
Jonathon ShlensSenior Research Scientist, Machine Learning Research, AppleVerified email at apple.com
Honglak LeeLG AI Research / U. MichiganVerified email at umich.edu
Maithra RaghuCornell University and Google BrainVerified email at cornell.edu
Christof KochChief Scientist, MindScope Program, Allen InstituteVerified email at alleninstitute.org
Rodrigo Quian QuirogaUniversity of LeicesterVerified email at vis.caltech.edu
Florian MormannUniversity of BonnVerified email at ukb.uni-bonn.de
Kevin SwerskyGoogle BrainVerified email at cs.toronto.edu
Earl K. MillerPicower Professor of Neuroscience, Massachusetts Institute of TechnologyVerified email at mit.edu
Alexander (Sasha) KraskovWellcome Trust Senior Research Fellow, Institute of Biosciences, Newcastle UniversityVerified email at ncl.ac.uk
Moran CerfCaltech, UCLA, NYU, Kellogg School of Management, MIT Media LabVerified email at morancerf.com
Ludwig SchmidtUniversity of Washington and Allen Institute for AIVerified email at cs.washington.edu
Ali FarhadiProfessor, Computer Science and Engineering, University of WashingtonVerified email at cs.uw.edu
Mitchell WortsmanUniversity of WashingtonVerified email at cs.washington.edu
Gabriel IlharcoUniversity of WashingtonVerified email at cs.washington.edu
Gamaleldin ElsayedSenior Research Scientist, Google Research, Brain TeamVerified email at google.com

Simon Kornblith

Google Brain

Verified email at google.com

representation learning vision


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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	8169	2020
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	1254	2020
When does label smoothing help? R Müller, S Kornblith, G Hinton Advances in Neural Information Processing Systems, 2019, 2019	1207	2019
Do better ImageNet models transfer better? S Kornblith, J Shlens, QV Le Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2019	983	2019
International conference on machine learning T Chen, S Kornblith, M Norouzi, G Hinton PMLR,, 2020	776	2020
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	562	2019
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	277	2008
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	276	2021
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	204	2018
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	196	2021
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	171	2011
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	153	2020
A network for scene processing in the macaque temporal lobe S Kornblith, X Cheng, S Ohayon, DY Tsao Neuron 79 (4), 766-781, 2013	117	2013
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	111	2021
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	110	2022
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	101	2018
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	94	2022
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	92	2017
Stimulus load and oscillatory activity in higher cortex S Kornblith, TJ Buschman, EK Miller Cerebral Cortex 26 (9), 3772-3784, 2016	71	2016
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	56	2022

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