| Gemini: a family of highly capable multimodal models G Team, R Anil, S Borgeaud, Y Wu, JB Alayrac, J Yu, R Soricut, ... arXiv preprint arXiv:2312.11805, 2023 | 924 | 2023 |
| On the (In)fidelity and Sensitivity for Explanations CK Yeh, CY Hsieh, AS Suggala, D Inouye, P Ravikumar Advances in Neural Information Processing Systems, 2019, 2019 | 441 | 2019 |
| Multi-label zero-shot learning with structured knowledge graphs CW Lee, W Fang, CK Yeh, YCF Wang Proceedings of the IEEE conference on computer vision and pattern …, 2018 | 329 | 2018 |
| Learning Deep Latent Spaces for Multi-Label Classification. CK Yeh, WC Wu, WJ Ko, YCF Wang AAAI, 2838-2844, 2017 | 320 | 2017 |
| On Completeness-Aware Concept-Based Explanations in Deep Neural Networks CK Yeh, B Kim, SO Arik, CL Li, T Pfister, P Ravikumar Advances in Neural Information Processing Systems, 2020, 2020 | 317* | 2020 |
| Representer point selection for explaining deep neural networks CK Yeh, J Kim, IEH Yen, PK Ravikumar Advances in neural information processing systems, 2018, 9291-9301, 2018 | 261 | 2018 |
| 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, ... ACL 2023, 2023 | 239 | 2023 |
| Order-free rnn with visual attention for multi-label classification SF Chen, YC Chen, CK Yeh, YC Wang Proceedings of the AAAI conference on artificial intelligence 32 (1), 2018 | 170 | 2018 |
| Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context M Reid, N Savinov, D Teplyashin, D Lepikhin, T Lillicrap, J Alayrac, ... arXiv preprint arXiv:2403.05530, 2024 | 154 | 2024 |
| Evaluations and methods for explanation through robustness analysis CY Hsieh, CK Yeh, X Liu, P Ravikumar, S Kim, S Kumar, CJ Hsieh International Conference on Representation Learning 2021, 2020 | 56 | 2020 |
| Automatic bridge bidding using deep reinforcement learning CK Yeh, CY Hsieh, HT Lin IEEE Transactions on Games 10 (4), 365-377, 2018 | 53 | 2018 |
| Minimizing flops to learn efficient sparse representations B Paria, CK Yeh, IEH Yen, N Xu, P Ravikumar, B Póczos International Conference on Learning Representation 2020, 2020 | 47 | 2020 |
| Unsupervised Speech Recognition via Segmental Empirical Output Distribution Matching CK Yeh, J Chen, C Yu, D Yu International Conference on Representation Learning 2019, 2018 | 45 | 2018 |
| Faith-Shap: The Faithful Shapley Interaction Index CP Tsai, CK Yeh, P Ravikumar Journal of Machine Learning Research, 2022 | 42 | 2022 |
| Deep generative models for weakly-supervised multi-label classification HM Chu, CK Yeh, YCF Wang Proceedings of the European Conference on Computer Vision (ECCV), 400-415, 2018 | 41 | 2018 |
| Human-Centered Concept Explanations for Neural Networks CK Yeh, B Kim, P Ravikumar IOS Press, 2022 | 29 | 2022 |
| First is Better Than Last for Language Data Influence CK Yeh, A Taly, M Sundararajan, F Liu, P Ravikumar NeurIPS 2022, 2022 | 19* | 2022 |
| Concept Gradient: Concept-based Interpretation Without Linear Assumption A Bai, CK Yeh, P Ravikumar, NYC Lin, CJ Hsieh International Conference on Representation Learning 2023, 2022 | 12 | 2022 |
| Threading the Needle of On and Off-Manifold Value Functions for Shapley Explanations CK Yeh, KY Lee, F Liu, P Ravikumar International Conference on Artificial Intelligence and Statistics 2022, 2022 | 10 | 2022 |
| Sample based explanations via generalized representers CP Tsai, CK Yeh, P Ravikumar Advances in Neural Information Processing Systems 36, 2024 | 6 | 2024 |
Pradeep RavikumarProfessor, School of Computer Science, Carnegie Mellon University在 cs.cmu.edu 的电子邮件经过验证
