Kornblith, Simon

A simple framework for contrastive learning of visual representations

Chen, T., Kornblith, S., Norouzi, M., & Hinton, G. (2020) · arXiv

Domain Adaptation and Few-Shot LearningAdvanced Neural Network Applications

Cited by 10,857

Do Better ImageNet Models Transfer Better?

Kornblith, S., Shlens, J., & Le, Q. V. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Domain Adaptation and Few-Shot LearningAdvanced Neural Network Applications

Cited by 1,325

When does label smoothing help?

Müller, R., Kornblith, S., & Hinton, G. (2019) · arXiv

Domain Adaptation and Few-Shot LearningAdvanced Neural Network Applications

Cited by 859

Similarity of Neural Network Representations Revisited

Kornblith, S., Norouzi, M., Lee, H., & Hinton, G. (2019) · arXiv

Neural Networks and ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 724

Big Self-Supervised Models Advance Medical Image Classification

Azizi, S., Mustafa, B., Ryan, F., Beaver, Z., Freyberg, J., Deaton, J., Loh, A., Karthikesalingam, A., Kornblith, S., Chen, T., et al. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV)

Domain Adaptation and Few-Shot LearningArtificial Intelligence in Healthcare and Education

Cited by 685

Do Vision Transformers See Like Convolutional Neural Networks?

Raghu, M., Unterthiner, T., Kornblith, S., Zhang, C., & Dosovitskiy, A. (2021) · arXiv

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 663

Towards Generalist Biomedical AI

Tu, T., Azizi, S., Driess, D., Schaekermann, M., Amin, M., Chang, P.-C., Carroll, A., Lau, C., Tanno, R., Ktena, I., et al. (2023) · arXiv

Artificial Intelligence in Healthcare and EducationMultimodal Machine Learning Applications

Cited by 483

Towards Generalist Biomedical AI

Tu, T., Azizi, S., Driess, D., Schaekermann, M., Amin, M., Chang, P.-C., Carroll, A., Lau, C., Tanno, R., Ktena, I., et al. (2024) · NEJM AI

Artificial Intelligence in Healthcare and EducationMultimodal Machine Learning Applications

Cited by 483

Robust fine-tuning of zero-shot models

Wortsman, M., Ilharco, G., Kim, J. W., Li, M., Kornblith, S., Roelofs, R., Lopes, R. G., Hajishirzi, H., Farhadi, A., Namkoong, H., et al. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Domain Adaptation and Few-Shot LearningAdvanced Neural Network Applications

Cited by 428

Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time

Wortsman, M., Ilharco, G., Gadre, S. Y., Roelofs, R., Gontijo-Lopes, R., Morcos, A. S., Namkoong, H., Farhadi, A., Carmon, Y., Kornblith, S., et al. (2022) · arXiv

Domain Adaptation and Few-Shot LearningAdvanced Neural Network Applications

Cited by 307

Laminar recordings in frontal cortex suggest distinct layers for maintenance and control of working memory

Bastos, A. M., Loonis, R., Kornblith, S., Lundqvist, M., & Miller, E. K. (2018) · Proceedings of the National Academy of Sciences

Neural dynamics and brain functionNeuroscience and Neuropharmacology Research

Cited by 290

Robust and data-efficient generalization of self-supervised machine learning for diagnostic imaging

Azizi, S., Culp, L., Freyberg, J., Mustafa, B., Baur, S., Kornblith, S., Chen, T., Tomasev, N., Mitrović, J., Strachan, P., et al. (2023) · Nature Biomedical Engineering

Domain Adaptation and Few-Shot LearningArtificial Intelligence in Healthcare and Education

Cited by 250

Synthetic Data from Diffusion Models Improves ImageNet Classification

Azizi, S., Kornblith, S., Saharia, C., Norouzi, M., & Fleet, D. J. (2023) · arXiv

Generative Adversarial Networks and Image SynthesisAdvanced Neural Network Applications

Cited by 184

Persistent Single-Neuron Activity during Working Memory in the Human Medial Temporal Lobe

Kornblith, S., Quian Quiroga, R., Koch, C., Fried, I., & Mormann, F. (2017) · Current Biology

Memory and Neural MechanismsNeural dynamics and brain function

Cited by 133

Denoising Pretraining for Semantic Segmentation

Brempong, E. A., Kornblith, S., Chen, T., Parmar, N., Minderer, M., & Norouzi, M. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Domain Adaptation and Few-Shot LearningAdvanced Neural Network Applications

Cited by 120

Do Wide and Deep Networks Learn the Same Things? Uncovering How Neural Network Representations Vary with Width and Depth

Nguyen, T., Raghu, M., & Kornblith, S. (2020) · arXiv

Neural Networks and ApplicationsAdvanced Neural Network Applications

Cited by 112

Getting aligned on representational alignment

Sucholutsky, I., Muttenthaler, L., Weller, A., Peng, A., Bobu, A., Kim, B., Love, B. C., Cueva, C. J., Grant, E., Groen, I., et al. (2023) · arXiv

Domain Adaptation and Few-Shot LearningExplainable Artificial Intelligence (XAI)

Cited by 36

Neural signatures of loss of consciousness and its recovery by thalamic stimulation

Donoghue, J. A., Bastos, A. M., Yanar, J., Kornblith, S., Mahnke, M., Brown, E. N., & Miller, E. K. (2019) · bioRxiv (Cold Spring Harbor Laboratory)

Neural dynamics and brain functionAnesthesia and Sedative Agents

Cited by 5

Small-scale proxies for large-scale Transformer training instabilities

Wortsman, M., Liu, P. J., Xiao, L., Everett, K., Alemi, A., Adlam, B., Co-Reyes, J. D., Gur, I., Kumar, A., Novak, R., et al. (2023) · arXiv

Stochastic Gradient Optimization TechniquesTopic Modeling

Cited by 5