Simon Kornblith
Simon Kornblith
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Cited by
Cited by
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
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
When does label smoothing help?
R Müller, S Kornblith, G Hinton
Advances in Neural Information Processing Systems, 2019, 2019
Do better ImageNet models transfer better?
S Kornblith, J Shlens, QV Le
Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2019
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
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
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
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
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
Openflamingo: An open-source framework for training large autoregressive vision-language models
A Awadalla, I Gao, J Gardner, J Hessel, Y Hanafy, W Zhu, K Marathe, ...
arXiv preprint arXiv:2308.01390, 2023
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
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
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
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
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
Synthetic data from diffusion models improves imagenet classification
S Azizi, S Kornblith, C Saharia, M Norouzi, DJ Fleet
arXiv preprint arXiv:2304.08466, 2023
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
Towards generalist biomedical AI
T Tu, S Azizi, D Driess, M Schaekermann, M Amin, PC Chang, A Carroll, ...
NEJM AI 1 (3), AIoa2300138, 2024
Robust and data-efficient generalization of self-supervised machine learning for diagnostic imaging
S Azizi, L Culp, J Freyberg, B Mustafa, S Baur, S Kornblith, T Chen, ...
Nature Biomedical Engineering 7 (6), 756-779, 2023
A network for scene processing in the macaque temporal lobe
S Kornblith, X Cheng, S Ohayon, DY Tsao
Neuron 79 (4), 766-781, 2013
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