Lipschitz Networks and Distributional Robustness. - Zac Cranko; Simon Kornblith; Zhan Shi; Richard Nock (CoRR, 2018)

Domain Adaptive Transfer Learning with Specialist Models. - Jiquan Ngiam; Daiyi Peng; Vijay Vasudevan; Simon Kornblith; Quoc V. Le; Ruoming Pang (CoRR, 2018)

Do Better ImageNet Models Transfer Better? - Simon Kornblith; Jonathon Shlens; Quoc V. Le (CVPR, 2019)

Similarity of Neural Network Representations Revisited. - Simon Kornblith; Mohammad Norouzi 002; Honglak Lee; Geoffrey E. Hinton (ICML, 2019)

Saccader: Improving Accuracy of Hard Attention Models for Vision. - Gamaleldin F. Elsayed; Simon Kornblith; Quoc V. Le (NeurIPS, 2019)

When does label smoothing help? - Rafael Müller; Simon Kornblith; Geoffrey E. Hinton (NeurIPS, 2019)

Cerberus: A Multi-headed Derenderer. - Boyang Deng; Simon Kornblith; Geoffrey E. Hinton (CoRR, 2019)

Exploring the Origins and Prevalence of Texture Bias in Convolutional Neural Networks. - Katherine L. Hermann; Simon Kornblith (CoRR, 2019)

A Simple Framework for Contrastive Learning of Visual Representations. - Ting Chen 007; Simon Kornblith; Mohammad Norouzi 002; Geoffrey E. Hinton (ICML, 2020)

Revisiting Spatial Invariance with Low-Rank Local Connectivity. - Gamaleldin F. Elsayed; Prajit Ramachandran; Jonathon Shlens; Simon Kornblith (ICML, 2020)

Big Self-Supervised Models are Strong Semi-Supervised Learners. - Ting Chen 007; Simon Kornblith; Kevin Swersky; Mohammad Norouzi 002; Geoffrey E. Hinton (NeurIPS, 2020)

The Origins and Prevalence of Texture Bias in Convolutional Neural Networks. - Katherine L. Hermann; Ting Chen; Simon Kornblith (NeurIPS, 2020)

Subclass Distillation. - Rafael Müller; Simon Kornblith; Geoffrey E. Hinton (CoRR, 2020)

What's in a Loss Function for Image Classification? - Simon Kornblith; Honglak Lee; Ting Chen 007; Mohammad Norouzi 002 (CoRR, 2020)

MIST: Multiple Instance Spatial Transformer. - Baptiste Angles; Yuhe Jin; Simon Kornblith; Andrea Tagliasacchi; Kwang Moo Yi (CVPR, 2021)

Big Self-Supervised Models Advance Medical Image Classification. - Shekoofeh Azizi; Basil Mustafa; Fiona Ryan; Zachary Beaver; Jan Freyberg; Jonathan Deaton; Aaron Loh; Alan Karthikesalingam; Simon Kornblith; Ting Chen 007; Vivek Natarajan; Mohammad Norouzi 002 (ICCV, 2021)

Do Wide and Deep Networks Learn the Same Things? Uncovering How Neural Network Representations Vary with Width and Depth. - Thao Nguyen; Maithra Raghu; Simon Kornblith (ICLR, 2021)

Teaching with Commentaries. - Aniruddh Raghu; Maithra Raghu; Simon Kornblith; David Duvenaud; Geoffrey E. Hinton (ICLR, 2021)

Generalised Lipschitz Regularisation Equals Distributional Robustness. - Zac Cranko; Zhan Shi; Xinhua Zhang; Richard Nock; Simon Kornblith (ICML, 2021)

Why Do Better Loss Functions Lead to Less Transferable Features? - Simon Kornblith; Ting Chen 007; Honglak Lee; Mohammad Norouzi 002 (NeurIPS, 2021)

Meta-learning to Improve Pre-training. - Aniruddh Raghu; Jonathan Lorraine; Simon Kornblith; Matthew McDermott; David Duvenaud (NeurIPS, 2021)

Do Vision Transformers See Like Convolutional Neural Networks? - Maithra Raghu; Thomas Unterthiner; Simon Kornblith; Chiyuan Zhang; Alexey Dosovitskiy (NeurIPS, 2021)

Generalized Shape Metrics on Neural Representations. - Alex H. Williams; Erin Kunz; Simon Kornblith; Scott W. Linderman (NeurIPS, 2021)

Denoising Pretraining for Semantic Segmentation. - Emmanuel Asiedu Brempong; Simon Kornblith; Ting Chen 007; Niki Parmar; Matthias Minderer; Mohammad Norouzi 002 (CVPR Workshops, 2022)

Robust fine-tuning of zero-shot models. - Mitchell Wortsman; Gabriel Ilharco; Jong Wook Kim; Mike Li; Simon Kornblith; Rebecca Roelofs; Raphael Gontijo Lopes; Hannaneh Hajishirzi; Ali Farhadi; Hongseok Namkoong; Ludwig Schmidt (CVPR, 2022)

A Study on Self-Supervised Object Detection Pretraining. - Trung Dang 002; Simon Kornblith; Huy Thong Nguyen; Peter Chin 001; Maryam Khademi (ECCV Workshops (4), 2022)

Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time. - 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 (ICML, 2022)

Patching open-vocabulary models by interpolating weights. - Gabriel Ilharco; Mitchell Wortsman; Samir Yitzhak Gadre; Shuran Song; Hannaneh Hajishirzi; Simon Kornblith; Ali Farhadi; Ludwig Schmidt (NeurIPS, 2022)

Boosting Contrastive Self-Supervised Learning with False Negative Cancellation. - Tri Huynh; Simon Kornblith; Matthew R. Walter; Michael Maire; Maryam Khademi (WACV, 2022)

Robust and Efficient Medical Imaging with Self-Supervision. - Shekoofeh Azizi; Laura Culp; Jan Freyberg; Basil Mustafa; Sebastien Baur; Simon Kornblith; Ting Chen 007; Patricia MacWilliams; S. Sara Mahdavi; Ellery Wulczyn; Boris Babenko; Megan Wilson; et al. (CoRR, 2022)

Improving Dense Contrastive Learning with Dense Negative Pairs. - Berk Iskender; Zhenlin Xu; Simon Kornblith; Enhung Chu; Maryam Khademi (CoRR, 2022)

Gaussian-Bernoulli RBMs Without Tears. - Renjie Liao; Simon Kornblith; Mengye Ren; David J. Fleet; Geoffrey E. Hinton (CoRR, 2022)

Decoder Denoising Pretraining for Semantic Segmentation. - Emmanuel Asiedu Brempong; Simon Kornblith; Ting Chen 007; Niki Parmar; Matthias Minderer; Mohammad Norouzi 002 (Trans. Mach. Learn. Res., 2022)

On the Origins of the Block Structure Phenomenon in Neural Network Representations. - Thao Nguyen; Maithra Raghu; Simon Kornblith (Trans. Mach. Learn. Res., 2022)

Interpretability of artificial neural network models in artificial intelligence versus neuroscience. - Kohitij Kar; Simon Kornblith; Evelina Fedorenko (Nat. Mac. Intell., 2022)

FlexiViT: One Model for All Patch Sizes. - Lucas Beyer; Pavel Izmailov; Alexander Kolesnikov 003; Mathilde Caron; Simon Kornblith; Xiaohua Zhai; Matthias Minderer; Michael Tschannen; Ibrahim Alabdulmohsin; Filip Pavetic (CVPR, 2023)

Hyperbolic Contrastive Learning for Visual Representations beyond Objects. - Songwei Ge; Shlok Mishra; Simon Kornblith; Chun-Liang Li; David Jacobs 001 (CVPR, 2023)

Guiding image captioning models toward more specific captions. - Simon Kornblith; Lala Li; Zirui Wang; Thao Nguyen (ICCV, 2023)

Human alignment of neural network representations. - Lukas Muttenthaler; Jonas Dippel; Lorenz Linhardt; Robert A. Vandermeulen; Simon Kornblith (ICLR, 2023)

Scaling Forward Gradient With Local Losses. - Mengye Ren; Simon Kornblith; Renjie Liao; Geoffrey E. Hinton (ICLR, 2023)

On the Relationship Between Explanation and Prediction: A Causal View. - Amir-Hossein Karimi; Krikamol Muandet; Simon Kornblith; Bernhard Schölkopf; Been Kim (ICML, 2023)

Does progress on ImageNet transfer to real-world datasets? - Alex Fang; Simon Kornblith; Ludwig Schmidt (NeurIPS, 2023)

Improving neural network representations using human similarity judgments. - Lukas Muttenthaler; Lorenz Linhardt; Jonas Dippel; Robert A. Vandermeulen; Katherine L. Hermann; Andrew K. Lampinen; Simon Kornblith (NeurIPS, 2023)

Towards Generalist Biomedical AI. - Tao Tu 001; Shekoofeh Azizi; Danny Driess; Mike Schaekermann; Mohamed Amin; Pi-Chuan Chang; Andrew Carroll; Chuck Lau; Ryutaro Tanno; Ira Ktena; Basil Mustafa; Aakanksha Chowdhery; et al. (CoRR, 2023)

OpenFlamingo: An Open-Source Framework for Training Large Autoregressive Vision-Language Models. - Anas Awadalla; Irena Gao; Josh Gardner 001; Jack Hessel; Yusuf Hanafy; Wanrong Zhu; Kalyani Marathe; Yonatan Bitton; Samir Yitzhak Gadre; Shiori Sagawa; Jenia Jitsev; Simon Kornblith; et al. (CoRR, 2023)

Replacing softmax with ReLU in Vision Transformers. - Mitchell Wortsman; Jaehoon Lee 001; Justin Gilmer; Simon Kornblith (CoRR, 2023)

Getting aligned on representational alignment. - 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; et al. (CoRR, 2023)

Frontier Language Models are not Robust to Adversarial Arithmetic, or "What do I need to say so you agree 2+2=5? - 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; et al. (CoRR, 2023)

Probing clustering in neural network representations. - Thao Nguyen; Simon Kornblith (CoRR, 2023)

Neither hype nor gloom do DNNs justice. - Felix A. Wichmann; Simon Kornblith; Robert Geirhos (CoRR, 2023)

Synthetic Data from Diffusion Models Improves ImageNet Classification. - Shekoofeh Azizi; Simon Kornblith; Chitwan Saharia; Mohammad Norouzi 002; David J. Fleet (Trans. Mach. Learn. Res., 2023)

Small-scale proxies for large-scale Transformer training instabilities. - 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; et al. (ICLR, 2024)

When does perceptual alignment benefit vision representations? - Shobhita Sundaram; Stephanie Fu; Lukas Muttenthaler; Netanel Tamir; Lucy Chai; Simon Kornblith; Trevor Darrell; Phillip Isola (NeurIPS, 2024)

Training Language Models on the Knowledge Graph: Insights on Hallucinations and Their Detectability. - Jiri Hron; Laura Culp; Gamaleldin F. Elsayed; Rosanne Liu; Ben Adlam; Maxwell L. Bileschi; Bernd Bohnet; JD Co-Reyes; Noah Fiedel; C. Daniel Freeman; Izzeddin Gur; Kathleen Kenealy; et al. (CoRR, 2024)

Aligning Machine and Human Visual Representations across Abstraction Levels. - Lukas Muttenthaler; Klaus Greff; Frieda Born; Bernhard Spitzer; Simon Kornblith; Michael C. Mozer; Klaus-Robert Müller; Thomas Unterthiner; Andrew K. Lampinen (CoRR, 2024)

Training objective drives the consistency of representational similarity across datasets. - Laure Ciernik; Lorenz Linhardt; Marco Morik; Jonas Dippel; Simon Kornblith; Lukas Muttenthaler (CoRR, 2024)