| 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 | 14795 | 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 | 1899 | 2020 |
| When does label smoothing help? R Müller, S Kornblith, G Hinton Advances in Neural Information Processing Systems, 2019, 2019 | 1770 | 2019 |
| Do better ImageNet models transfer better? S Kornblith, J Shlens, QV Le Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2019 | 1259 | 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 | 919 | 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 | 613 | 2021 |
| 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 | 388 | 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 | 359 | 2022 |
| 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 | 294 | 2008 |
| 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 | 289 | 2022 |
| 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 | 250 | 2018 |
| 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 | 242 | 2020 |
| 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 | 198 | 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 | 196 | 2011 |
| A network for scene processing in the macaque temporal lobe S Kornblith, X Cheng, S Ohayon, DY Tsao Neuron 79 (4), 766-781, 2013 | 125 | 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 | 118 | 2018 |
| 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 | 107 | 2017 |
| 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 | 105 | 2022 |
| 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 | 88* | 2021 |
| Stimulus load and oscillatory activity in higher cortex S Kornblith, TJ Buschman, EK Miller Cerebral Cortex 26 (9), 3772-3784, 2016 | 82 | 2016 |
Mohammad NorouziIdeogramVerified email at ideogram.ai
