| Fixmatch: Simplifying semi-supervised learning with consistency and confidence K Sohn, D Berthelot, N Carlini, Z Zhang, H Zhang, CA Raffel, ED Cubuk, ... Advances in neural information processing systems 33, 596-608, 2020 | 3721 | 2020 |
| Cutpaste: Self-supervised learning for anomaly detection and localization CL Li, K Sohn, J Yoon, T Pfister Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2021 | 850 | 2021 |
| MMD GAN: Towards deeper understanding of moment matching network CL Li, WC Chang, Y Cheng, Y Yang, B Póczos Advances in Neural Information Processing Systems, 2203-2213, 2017 | 848 | 2017 |
| A simple semi-supervised learning framework for object detection K Sohn, Z Zhang, CL Li, H Zhang, CY Lee, T Pfister arXiv preprint arXiv:2005.04757, 2020 | 540 | 2020 |
| One Network to Solve Them All--Solving Linear Inverse Problems Using Deep Projection Models JH Rick Chang, CL Li, B Poczos, BVK Vijaya Kumar, ... Proceedings of the IEEE International Conference on Computer Vision, 5888-5897, 2017 | 391 | 2017 |
| On completeness-aware concept-based explanations in deep neural networks CK Yeh, B Kim, S Arik, CL Li, T Pfister, P Ravikumar Advances in Neural Information Processing Systems 33, 2020 | 346* | 2020 |
| PseudoSeg: Designing Pseudo Labels for Semantic Segmentation Y Zou, Z Zhang, H Zhang, CL Li, X Bian, JB Huang, T Pfister ICLR 2021, 2021 | 336 | 2021 |
| Distilling step-by-step! outperforming larger language models with less training data and smaller model sizes CY Hsieh, CL Li, CK Yeh, H Nakhost, Y Fujii, A Ratner, R Krishna, CY Lee, ... arXiv preprint arXiv:2305.02301, 2023 | 309 | 2023 |
| Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States EY Cramer, EL Ray, VK Lopez, J Bracher, A Brennen, ... Proceedings of the National Academy of Sciences 119 (15), e2113561119, 2022 | 298* | 2022 |
| Point cloud gan CL Li, M Zaheer, Y Zhang, B Poczos, R Salakhutdinov arXiv preprint arXiv:1810.05795, 2018 | 255 | 2018 |
| Learning and Evaluating Representations for Deep One-class Classification K Sohn, CL Li, J Yoon, M Jin, T Pfister ICLR 2021, 2021 | 236 | 2021 |
| Cmu deeplens: deep learning for automatic image-based galaxy–galaxy strong lens finding F Lanusse, Q Ma, N Li, TE Collett, CL Li, S Ravanbakhsh, R Mandelbaum, ... Monthly Notices of the Royal Astronomical Society 473 (3), 3895-3906, 2018 | 217 | 2018 |
| i-Mix: A Strategy for Regularizing Contrastive Representation Learning K Lee, Y Zhu, K Sohn, CL Li, J Shin, H Lee ICLR 2021, 2021 | 154* | 2021 |
| Sobolev gan Y Mroueh, CL Li, T Sercu, A Raj, Y Cheng ICLR, 2018 | 146 | 2018 |
| The strong gravitational lens finding challenge RB Metcalf, M Meneghetti, C Avestruz, F Bellagamba, CR Bom, E Bertin, ... Astronomy & Astrophysics 625, A119, 2019 | 139 | 2019 |
| Beyond Pixel Norm-Balls: Parametric Adversaries using an Analytically Differentiable Renderer HTD Liu, M Tao, CL Li, D Nowrouzezahrai, A Jacobson | 135* | 2019 |
| Tsmixer: An all-mlp architecture for time series forecasting SA Chen, CL Li, N Yoder, SO Arik, T Pfister arXiv preprint arXiv:2303.06053, 2023 | 120 | 2023 |
| Classifier Two-Sample Test for Video Anomaly Detection Y Liu, CL Li, B Póczos | 108 | 2018 |
| Interpretable sequence learning for COVID-19 forecasting S Arik, CL Li, J Yoon, R Sinha, A Epshteyn, L Le, V Menon, S Singh, ... Advances in Neural Information Processing Systems 33, 18807-18818, 2020 | 101 | 2020 |
| High Dimensional Bayesian Optimization via Restricted Projection Pursuit Models CL Li, K Kandasamy, B Póczos, J Schneider Proceedings of the 19th International Conference on Artificial Intelligence …, 2016 | 96 | 2016 |
Kihyuk SohnResearch Scientist, Meta Reality LabsVerified email at meta.com
