| 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 | 900 | 2023 |
| Graph matching networks for learning the similarity of graph structured objects Y Li, C Gu, T Dullien, O Vinyals, P Kohli International conference on machine learning, 3835-3845, 2019 | 611 | 2019 |
| DDSP: Differentiable digital signal processing J Engel, L Hantrakul, C Gu, A Roberts arXiv preprint arXiv:2001.04643, 2020 | 434 | 2020 |
| QLMOR: A projection-based nonlinear model order reduction approach using quadratic-linear representation of nonlinear systems C Gu IEEE Transactions on Computer-Aided Design of Integrated Circuits and …, 2011 | 213 | 2011 |
| 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 | 143 | 2024 |
| Bayesian model fusion: large-scale performance modeling of analog and mixed-signal circuits by reusing early-stage data F Wang, W Zhang, S Sun, X Li, C Gu Proceedings of the 50th Annual Design Automation Conference, 1-6, 2013 | 115 | 2013 |
| Reinforced self-training (rest) for language modeling C Gulcehre, TL Paine, S Srinivasan, K Konyushkova, L Weerts, A Sharma, ... arXiv preprint arXiv:2308.08998, 2023 | 112 | 2023 |
| A generalized framework for population based training A Li, O Spyra, S Perel, V Dalibard, M Jaderberg, C Gu, D Budden, ... Proceedings of the 25th ACM SIGKDD International Conference on Knowledge …, 2019 | 71 | 2019 |
| Model reduction via projection onto nonlinear manifolds, with applications to analog circuits and biochemical systems C Gu, J Roychowdhury 2008 IEEE/ACM International Conference on Computer-Aided Design, 85-92, 2008 | 64 | 2008 |
| Model order reduction of nonlinear dynamical systems C Gu University of California, Berkeley, 2011 | 63 | 2011 |
| An efficient, fully nonlinear, variability-aware non-Monte-Carlo yield estimation procedure with applications to SRAM cells and ring oscillators C Gu, J Roychowdhury 2008 Asia and South Pacific Design Automation Conference, 754-761, 2008 | 57 | 2008 |
| Efficient parametric yield estimation of analog/mixed-signal circuits via Bayesian model fusion X Li, W Zhang, F Wang, S Sun, C Gu Proceedings of the International Conference on Computer-Aided Design, 627-634, 2012 | 42 | 2012 |
| Muzero with self-competition for rate control in vp9 video compression A Mandhane, A Zhernov, M Rauh, C Gu, M Wang, F Xue, W Shang, ... arXiv preprint arXiv:2202.06626, 2022 | 36 | 2022 |
| Extreme Statistics in Nanoscale Memory Design A Singhee, RA Rutenbar Springer Verlag, 2010 | 35 | 2010 |
| QLMOR: A new projection-based approach for nonlinear model order reduction C Gu Proceedings of the 2009 International Conference on Computer-Aided Design …, 2009 | 35 | 2009 |
| Fast and Flexible Neural Audio Synthesis. L Hantrakul, JH Engel, A Roberts, C Gu Ismir, 524-530, 2019 | 31 | 2019 |
| Bayesian model fusion: a statistical framework for efficient pre-silicon validation and post-silicon tuning of complex analog and mixed-signal circuits X Li, F Wang, S Sun, C Gu 2013 IEEE/ACM International Conference on Computer-Aided Design (ICCAD), 795-802, 2013 | 31 | 2013 |
| Efficient moment estimation with extremely small sample size via bayesian inference for analog/mixed-signal validation C Gu, E Chiprout, X Li Proceedings of the 50th Annual Design Automation Conference, 1-7, 2013 | 26 | 2013 |
| Challenges in post-silicon validation of high-speed I/O links C Gu Proceedings of the International Conference on Computer-Aided Design, 547-550, 2012 | 23 | 2012 |
| Efficient bit error rate estimation for high-speed link by Bayesian model fusion C Fang, Q Huang, F Yang, X Zeng, X Li, C Gu 2015 Design, Automation & Test in Europe Conference & Exhibition (DATE …, 2015 | 18 | 2015 |
