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2020 – today
- 2024
[c32]Mitchell Wortsman, Peter J. Liu, Lechao Xiao, Katie E. Everett, Alexander A. Alemi, Ben Adlam, John D. Co-Reyes, Izzeddin Gur, Abhishek Kumar, Roman Novak, Jeffrey Pennington, Jascha Sohl-Dickstein, Kelvin Xu, Jaehoon Lee, Justin Gilmer, Simon Kornblith:
Small-scale proxies for large-scale Transformer training instabilities. ICLR 2024- 2023
- [j4]Shekoofeh Azizi, Simon Kornblith, Chitwan Saharia, Mohammad Norouzi, David J. Fleet:
Synthetic Data from Diffusion Models Improves ImageNet Classification. Trans. Mach. Learn. Res. 2023 (2023) - [c31]Songwei Ge, Shlok Mishra, Simon Kornblith, Chun-Liang Li, David Jacobs:
Hyperbolic Contrastive Learning for Visual Representations beyond Objects. CVPR 2023: 6840-6849 - [c30]Lucas Beyer, Pavel Izmailov, Alexander Kolesnikov, Mathilde Caron, Simon Kornblith, Xiaohua Zhai, Matthias Minderer, Michael Tschannen, Ibrahim Alabdulmohsin, Filip Pavetic:
FlexiViT: One Model for All Patch Sizes. CVPR 2023: 14496-14506 - [c29]Simon Kornblith, Lala Li, Zirui Wang, Thao Nguyen:
Guiding image captioning models toward more specific captions. ICCV 2023: 15213-15223 - [c28]Lukas Muttenthaler, Jonas Dippel, Lorenz Linhardt, Robert A. Vandermeulen
, Simon Kornblith:
Human alignment of neural network representations. ICLR 2023 - [c27]Mengye Ren, Simon Kornblith, Renjie Liao, Geoffrey E. Hinton:
Scaling Forward Gradient With Local Losses. ICLR 2023 - [c26]Amir-Hossein Karimi, Krikamol Muandet, Simon Kornblith, Bernhard Schölkopf, Been Kim:
On the Relationship Between Explanation and Prediction: A Causal View. ICML 2023: 15861-15883 - [c25]Alex Fang, Simon Kornblith, Ludwig Schmidt:
Does progress on ImageNet transfer to real-world datasets? NeurIPS 2023 - [c24]Lukas Muttenthaler, Lorenz Linhardt, Jonas Dippel, Robert A. Vandermeulen, Katherine L. Hermann, Andrew K. Lampinen, Simon Kornblith:
Improving neural network representations using human similarity judgments. NeurIPS 2023 - [i46]Alex Fang, Simon Kornblith, Ludwig Schmidt:
Does progress on ImageNet transfer to real-world datasets? CoRR abs/2301.04644 (2023) - [i45]Shekoofeh Azizi, Simon Kornblith, Chitwan Saharia, Mohammad Norouzi, David J. Fleet:
Synthetic Data from Diffusion Models Improves ImageNet Classification. CoRR abs/2304.08466 (2023) - [i44]Lukas Muttenthaler, Lorenz Linhardt, Jonas Dippel, Robert A. Vandermeulen, Katherine L. Hermann, Andrew K. Lampinen, Simon Kornblith:
Improving neural network representations using human similarity judgments. CoRR abs/2306.04507 (2023) - [i43]Tao Tu, Shekoofeh Azizi, Danny Driess, Mike Schaekermann, Mohamed Amin, Pi-Chuan Chang, Andrew Carroll, Chuck Lau, Ryutaro Tanno, Ira Ktena, Basil Mustafa, Aakanksha Chowdhery, Yun Liu, Simon Kornblith, David J. Fleet, Philip Andrew Mansfield, Sushant Prakash, Renee Wong, Sunny Virmani, Christopher Semturs, S. Sara Mahdavi, Bradley Green, Ewa Dominowska, Blaise Agüera y Arcas, Joelle K. Barral, Dale R. Webster, Gregory S. Corrado, Yossi Matias, Karan Singhal, Pete Florence, Alan Karthikesalingam, Vivek Natarajan:
Towards Generalist Biomedical AI. CoRR abs/2307.14334 (2023) - [i42]Simon Kornblith, Lala Li, Zirui Wang, Thao Nguyen:
Guiding Image Captioning Models Toward More Specific Captions. CoRR abs/2307.16686 (2023) - [i41]Anas Awadalla, Irena Gao, Josh Gardner, Jack Hessel, Yusuf Hanafy, Wanrong Zhu, Kalyani Marathe, Yonatan Bitton, Samir Yitzhak Gadre, Shiori Sagawa, Jenia Jitsev, Simon Kornblith, Pang Wei Koh, Gabriel Ilharco, Mitchell Wortsman, Ludwig Schmidt:
OpenFlamingo: An Open-Source Framework for Training Large Autoregressive Vision-Language Models. CoRR abs/2308.01390 (2023) - [i40]Mitchell Wortsman, Jaehoon Lee, Justin Gilmer, Simon Kornblith:
Replacing softmax with ReLU in Vision Transformers. CoRR abs/2309.08586 (2023) - [i39]Mitchell Wortsman, Peter J. Liu, Lechao Xiao, Katie Everett, Alex Alemi, Ben Adlam, John D. Co-Reyes, Izzeddin Gur, Abhishek Kumar, Roman Novak, Jeffrey Pennington, Jascha Sohl-Dickstein, Kelvin Xu, Jaehoon Lee, Justin Gilmer, Simon Kornblith:
Small-scale proxies for large-scale Transformer training instabilities. CoRR abs/2309.14322 (2023) - [i38]Ilia Sucholutsky, Lukas Muttenthaler, Adrian Weller, Andi Peng, Andreea Bobu, Been Kim, Bradley C. Love, Erin Grant, Jascha Achterberg, Joshua B. Tenenbaum, Katherine M. Collins, Katherine L. Hermann, Kerem Oktar, Klaus Greff, Martin N. Hebart, Nori Jacoby, Qiuyi Zhang, Raja Marjieh, Robert Geirhos, Sherol Chen, Simon Kornblith, Sunayana Rane, Talia Konkle, Thomas P. O'Connell, Thomas Unterthiner, Andrew K. Lampinen, Klaus-Robert Müller, Mariya Toneva, Thomas L. Griffiths:
Getting aligned on representational alignment. CoRR abs/2310.13018 (2023) - [i37]C. Daniel Freeman, Laura Culp, Aaron Parisi, Maxwell L. Bileschi, Gamaleldin F. Elsayed, Alex Rizkowsky, Isabelle Simpson, Alex Alemi, Azade Nova, Ben Adlam, Bernd Bohnet, Gaurav Mishra, Hanie Sedghi, Igor Mordatch, Izzeddin Gur, Jaehoon Lee, John D. Co-Reyes, Jeffrey Pennington, Kelvin Xu, Kevin Swersky, Kshiteej Mahajan, Lechao Xiao, Rosanne Liu, Simon Kornblith, Noah Constant, Peter J. Liu, Roman Novak, Yundi Qian, Noah Fiedel, Jascha Sohl-Dickstein:
Frontier Language Models are not Robust to Adversarial Arithmetic, or "What do I need to say so you agree 2+2=5? CoRR abs/2311.07587 (2023) - [i36]Thao Nguyen, Simon Kornblith:
Probing clustering in neural network representations. CoRR abs/2311.07864 (2023) - [i35]Felix A. Wichmann, Simon Kornblith, Robert Geirhos:
Neither hype nor gloom do DNNs justice. CoRR abs/2312.05355 (2023) - 2022
- [j3]Kohitij Kar, Simon Kornblith, Evelina Fedorenko:
Interpretability of artificial neural network models in artificial intelligence versus neuroscience. Nat. Mac. Intell. 4(12): 1065-1067 (2022) - [j2]Emmanuel Asiedu Brempong, Simon Kornblith, Ting Chen, Niki Parmar, Matthias Minderer, Mohammad Norouzi:
Decoder Denoising Pretraining for Semantic Segmentation. Trans. Mach. Learn. Res. 2022 (2022) - [j1]Thao Nguyen, Maithra Raghu, Simon Kornblith:
On the Origins of the Block Structure Phenomenon in Neural Network Representations. Trans. Mach. Learn. Res. 2022 (2022) - [c23]Emmanuel Asiedu Brempong, Simon Kornblith, Ting Chen, Niki Parmar, Matthias Minderer, Mohammad Norouzi:
Denoising Pretraining for Semantic Segmentation. CVPR Workshops 2022: 4174-4185 - [c22]Mitchell Wortsman, Gabriel Ilharco, Jong Wook Kim, Mike Li, Simon Kornblith, Rebecca Roelofs, Raphael Gontijo Lopes, Hannaneh Hajishirzi, Ali Farhadi, Hongseok Namkoong, Ludwig Schmidt:
Robust fine-tuning of zero-shot models. CVPR 2022: 7949-7961 - [c21]Trung Dang, Simon Kornblith, Huy Thong Nguyen, Peter Chin, Maryam Khademi:
A Study on Self-Supervised Object Detection Pretraining. ECCV Workshops (4) 2022: 86-99 - [c20]Mitchell Wortsman, Gabriel Ilharco, Samir Yitzhak Gadre, Rebecca Roelofs, Raphael Gontijo Lopes, Ari S. Morcos, Hongseok Namkoong, Ali Farhadi, Yair Carmon, Simon Kornblith, Ludwig Schmidt:
Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time. ICML 2022: 23965-23998 - [c19]Gabriel Ilharco, Mitchell Wortsman, Samir Yitzhak Gadre, Shuran Song, Hannaneh Hajishirzi, Simon Kornblith, Ali Farhadi, Ludwig Schmidt:
Patching open-vocabulary models by interpolating weights. NeurIPS 2022 - [c18]Tri Huynh, Simon Kornblith, Matthew R. Walter, Michael Maire, Maryam Khademi:
Boosting Contrastive Self-Supervised Learning with False Negative Cancellation. WACV 2022: 986-996 - [i34]Thao Nguyen, Maithra Raghu, Simon Kornblith:
On the Origins of the Block Structure Phenomenon in Neural Network Representations. CoRR abs/2202.07184 (2022) - [i33]Mitchell Wortsman, Gabriel Ilharco, Samir Yitzhak Gadre, Rebecca Roelofs, Raphael Gontijo Lopes, Ari S. Morcos, Hongseok Namkoong, Ali Farhadi, Yair Carmon, Simon Kornblith, Ludwig Schmidt:
Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time. CoRR abs/2203.05482 (2022) - [i32]Shekoofeh Azizi, Laura Culp, Jan Freyberg, Basil Mustafa, Sebastien Baur, Simon Kornblith, Ting Chen, Patricia MacWilliams, S. Sara Mahdavi, Ellery Wulczyn, Boris Babenko, Megan Wilson, Aaron Loh, Po-Hsuan Cameron Chen, Yuan Liu, Pinal Bavishi, Scott Mayer McKinney, Jim Winkens, Abhijit Guha Roy, Zachary Beaver, Fiona Ryan, Justin Krogue, Mozziyar Etemadi, Umesh Telang, Yun Liu, Lily Peng, Gregory S. Corrado, Dale R. Webster, David J. Fleet, Geoffrey E. Hinton, Neil Houlsby, Alan Karthikesalingam, Mohammad Norouzi, Vivek Natarajan:
Robust and Efficient Medical Imaging with Self-Supervision. CoRR abs/2205.09723 (2022) - [i31]Emmanuel Asiedu Brempong, Simon Kornblith, Ting Chen, Niki Parmar, Matthias Minderer, Mohammad Norouzi:
Decoder Denoising Pretraining for Semantic Segmentation. CoRR abs/2205.11423 (2022) - [i30]Trung Dang, Simon Kornblith, Huy Thong Nguyen, Peter Chin, Maryam Khademi:
A Study on Self-Supervised Object Detection Pretraining. CoRR abs/2207.04186 (2022) - [i29]Gabriel Ilharco, Mitchell Wortsman, Samir Yitzhak Gadre, Shuran Song, Hannaneh Hajishirzi, Simon Kornblith, Ali Farhadi, Ludwig Schmidt:
Patching open-vocabulary models by interpolating weights. CoRR abs/2208.05592 (2022) - [i28]Mengye Ren, Simon Kornblith, Renjie Liao, Geoffrey E. Hinton:
Scaling Forward Gradient With Local Losses. CoRR abs/2210.03310 (2022) - [i27]Berk Iskender, Zhenlin Xu, Simon Kornblith, Enhung Chu, Maryam Khademi:
Improving Dense Contrastive Learning with Dense Negative Pairs. CoRR abs/2210.05063 (2022) - [i26]Renjie Liao, Simon Kornblith, Mengye Ren, David J. Fleet, Geoffrey E. Hinton:
Gaussian-Bernoulli RBMs Without Tears. CoRR abs/2210.10318 (2022) - [i25]Lukas Muttenthaler, Jonas Dippel, Lorenz Linhardt, Robert A. Vandermeulen, Simon Kornblith:
Human alignment of neural network representations. CoRR abs/2211.01201 (2022) - [i24]Songwei Ge, Shlok Mishra, Simon Kornblith, Chun-Liang Li, David Jacobs:
Hyperbolic Contrastive Learning for Visual Representations beyond Objects. CoRR abs/2212.00653 (2022) - [i23]Amir-Hossein Karimi, Krikamol Muandet, Simon Kornblith, Bernhard Schölkopf, Been Kim:
On the Relationship Between Explanation and Prediction: A Causal View. CoRR abs/2212.06925 (2022) - [i22]Lucas Beyer, Pavel Izmailov, Alexander Kolesnikov, Mathilde Caron, Simon Kornblith, Xiaohua Zhai, Matthias Minderer, Michael Tschannen, Ibrahim Alabdulmohsin, Filip Pavetic:
FlexiViT: One Model for All Patch Sizes. CoRR abs/2212.08013 (2022) - 2021
- [c17]Baptiste Angles, Yuhe Jin, Simon Kornblith, Andrea Tagliasacchi, Kwang Moo Yi:
MIST: Multiple Instance Spatial Transformer. CVPR 2021: 2412-2422 - [c16]Shekoofeh Azizi, Basil Mustafa, Fiona Ryan, Zachary Beaver, Jan Freyberg, Jonathan Deaton, Aaron Loh, Alan Karthikesalingam, Simon Kornblith, Ting Chen, Vivek Natarajan, Mohammad Norouzi:
Big Self-Supervised Models Advance Medical Image Classification. ICCV 2021: 3458-3468 - [c15]Thao Nguyen, Maithra Raghu, Simon Kornblith:
Do Wide and Deep Networks Learn the Same Things? Uncovering How Neural Network Representations Vary with Width and Depth. ICLR 2021 - [c14]Aniruddh Raghu, Maithra Raghu, Simon Kornblith, David Duvenaud, Geoffrey E. Hinton:
Teaching with Commentaries. ICLR 2021 - [c13]Zac Cranko, Zhan Shi, Xinhua Zhang, Richard Nock, Simon Kornblith:
Generalised Lipschitz Regularisation Equals Distributional Robustness. ICML 2021: 2178-2188 - [c12]Alex H. Williams, Erin Kunz, Simon Kornblith, Scott W. Linderman:
Generalized Shape Metrics on Neural Representations. NeurIPS 2021: 4738-4750 - [c11]Maithra Raghu, Thomas Unterthiner, Simon Kornblith, Chiyuan Zhang, Alexey Dosovitskiy:
Do Vision Transformers See Like Convolutional Neural Networks? NeurIPS 2021: 12116-12128 - [c10]Aniruddh Raghu, Jonathan Lorraine, Simon Kornblith, Matthew McDermott, David Duvenaud:
Meta-learning to Improve Pre-training. NeurIPS 2021: 23231-23244 - [c9]Simon Kornblith, Ting Chen, Honglak Lee, Mohammad Norouzi:
Why Do Better Loss Functions Lead to Less Transferable Features? NeurIPS 2021: 28648-28662 - [i21]Shekoofeh Azizi, Basil Mustafa, Fiona Ryan, Zachary Beaver, Jan Freyberg, Jonathan Deaton, Aaron Loh, Alan Karthikesalingam, Simon Kornblith, Ting Chen, Vivek Natarajan, Mohammad Norouzi:
Big Self-Supervised Models Advance Medical Image Classification. CoRR abs/2101.05224 (2021) - [i20]Maithra Raghu, Thomas Unterthiner, Simon Kornblith, Chiyuan Zhang, Alexey Dosovitskiy:
Do Vision Transformers See Like Convolutional Neural Networks? CoRR abs/2108.08810 (2021) - [i19]Alex H. Williams, Erin Kunz, Simon Kornblith, Scott W. Linderman:
Generalized Shape Metrics on Neural Representations. CoRR abs/2110.14739 (2021) - [i18]Aniruddh Raghu, Jonathan Lorraine, Simon Kornblith, Matthew McDermott, David Duvenaud:
Meta-Learning to Improve Pre-Training. CoRR abs/2111.01754 (2021) - 2020
- [c8]Ting Chen, Simon Kornblith, Mohammad Norouzi, Geoffrey E. Hinton:
A Simple Framework for Contrastive Learning of Visual Representations. ICML 2020: 1597-1607 - [c7]Gamaleldin F. Elsayed, Prajit Ramachandran, Jonathon Shlens, Simon Kornblith:
Revisiting Spatial Invariance with Low-Rank Local Connectivity. ICML 2020: 2868-2879 - [c6]Ting Chen, Simon Kornblith, Kevin Swersky, Mohammad Norouzi, Geoffrey E. Hinton:
Big Self-Supervised Models are Strong Semi-Supervised Learners. NeurIPS 2020 - [c5]Katherine L. Hermann, Ting Chen, Simon Kornblith:
The Origins and Prevalence of Texture Bias in Convolutional Neural Networks. NeurIPS 2020 - [i17]Gamaleldin F. Elsayed, Prajit Ramachandran, Jonathon Shlens, Simon Kornblith:
Revisiting Spatial Invariance with Low-Rank Local Connectivity. CoRR abs/2002.02959 (2020) - [i16]Rafael Müller, Simon Kornblith, Geoffrey E. Hinton:
Subclass Distillation. CoRR abs/2002.03936 (2020) - [i15]Zac Cranko, Zhan Shi, Xinhua Zhang, Richard Nock, Simon Kornblith:
Generalised Lipschitz Regularisation Equals Distributional Robustness. CoRR abs/2002.04197 (2020) - [i14]Ting Chen, Simon Kornblith, Mohammad Norouzi, Geoffrey E. Hinton:
A Simple Framework for Contrastive Learning of Visual Representations. CoRR abs/2002.05709 (2020) - [i13]Ting Chen, Simon Kornblith, Kevin Swersky, Mohammad Norouzi, Geoffrey E. Hinton:
Big Self-Supervised Models are Strong Semi-Supervised Learners. CoRR abs/2006.10029 (2020) - [i12]Thao Nguyen, Maithra Raghu, Simon Kornblith:
Do Wide and Deep Networks Learn the Same Things? Uncovering How Neural Network Representations Vary with Width and Depth. CoRR abs/2010.15327 (2020) - [i11]Simon Kornblith, Honglak Lee, Ting Chen, Mohammad Norouzi:
What's in a Loss Function for Image Classification? CoRR abs/2010.16402 (2020) - [i10]Aniruddh Raghu, Maithra Raghu, Simon Kornblith, David Duvenaud, Geoffrey E. Hinton:
Teaching with Commentaries. CoRR abs/2011.03037 (2020) - [i9]Tri Huynh, Simon Kornblith, Matthew R. Walter, Michael Maire, Maryam Khademi:
Boosting Contrastive Self-Supervised Learning with False Negative Cancellation. CoRR abs/2011.11765 (2020)
2010 – 2019
- 2019
- [c4]Simon Kornblith, Jonathon Shlens, Quoc V. Le:
Do Better ImageNet Models Transfer Better? CVPR 2019: 2661-2671 - [c3]Simon Kornblith, Mohammad Norouzi, Honglak Lee, Geoffrey E. Hinton:
Similarity of Neural Network Representations Revisited. ICML 2019: 3519-3529 - [c2]Gamaleldin F. Elsayed, Simon Kornblith, Quoc V. Le:
Saccader: Improving Accuracy of Hard Attention Models for Vision. NeurIPS 2019: 700-712 - [c1]Rafael Müller, Simon Kornblith, Geoffrey E. Hinton:
When does label smoothing help? NeurIPS 2019: 4696-4705 - [i8]Simon Kornblith, Mohammad Norouzi, Honglak Lee, Geoffrey E. Hinton:
Similarity of Neural Network Representations Revisited. CoRR abs/1905.00414 (2019) - [i7]Boyang Deng, Simon Kornblith, Geoffrey E. Hinton:
Cerberus: A Multi-headed Derenderer. CoRR abs/1905.11940 (2019) - [i6]Rafael Müller, Simon Kornblith, Geoffrey E. Hinton:
When Does Label Smoothing Help? CoRR abs/1906.02629 (2019) - [i5]Gamaleldin F. Elsayed, Simon Kornblith, Quoc V. Le:
Saccader: Improving Accuracy of Hard Attention Models for Vision. CoRR abs/1908.07644 (2019) - [i4]Katherine L. Hermann, Simon Kornblith:
Exploring the Origins and Prevalence of Texture Bias in Convolutional Neural Networks. CoRR abs/1911.09071 (2019) - 2018
- [i3]Simon Kornblith, Jonathon Shlens, Quoc V. Le:
Do Better ImageNet Models Transfer Better? CoRR abs/1805.08974 (2018) - [i2]Zac Cranko, Simon Kornblith, Zhan Shi, Richard Nock:
Lipschitz Networks and Distributional Robustness. CoRR abs/1809.01129 (2018) - [i1]Jiquan Ngiam, Daiyi Peng, Vijay Vasudevan, Simon Kornblith, Quoc V. Le, Ruoming Pang:
Domain Adaptive Transfer Learning with Specialist Models. CoRR abs/1811.07056 (2018)
Coauthor Index
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