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 | 568 | 2023 |
Deepfusion: Lidar-camera deep fusion for multi-modal 3d object detection Y Li, AW Yu, T Meng, B Caine, J Ngiam, D Peng, J Shen, Y Lu, D Zhou, ... Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022 | 255 | 2022 |
Domain adaptive transfer learning with specialist models J Ngiam, D Peng, V Vasudevan, S Kornblith, QV Le, R Pang arXiv preprint arXiv:1811.07056, 2018 | 130 | 2018 |
Evolving reinforcement learning algorithms JD Co-Reyes, Y Miao, D Peng, E Real, S Levine, QV Le, H Lee, A Faust arXiv preprint arXiv:2101.03958, 2021 | 82 | 2021 |
AutoHAS: Efficient hyperparameter and architecture search X Dong, M Tan, AW Yu, D Peng, B Gabrys, QV Le arXiv preprint arXiv:2006.03656, 2020 | 44 | 2020 |
Towards nngp-guided neural architecture search DS Park, J Lee, D Peng, Y Cao, J Sohl-Dickstein arXiv preprint arXiv:2011.06006, 2020 | 33 | 2020 |
PyGlove: Symbolic programming for automated machine learning D Peng, X Dong, E Real, M Tan, Y Lu, G Bender, H Liu, A Kraft, C Liang, ... Advances in Neural Information Processing Systems 33, 96-108, 2020 | 32 | 2020 |
Rethinking co-design of neural architectures and hardware accelerators Y Zhou, X Dong, B Akin, M Tan, D Peng, T Meng, A Yazdanbakhsh, ... arXiv preprint arXiv:2102.08619, 2021 | 29 | 2021 |
Autohas: Differentiable hyper-parameter and architecture search X Dong, M Tan, AW Yu, D Peng, B Gabrys, QV Le arXiv preprint arXiv:2006.03656 4 (5), 2020 | 26 | 2020 |
Towards the co-design of neural networks and accelerators Y Zhou, X Dong, T Meng, M Tan, B Akin, D Peng, A Yazdanbakhsh, ... Proceedings of Machine Learning and Systems 4, 141-152, 2022 | 15 | 2022 |
Brainformers: Trading simplicity for efficiency Y Zhou, N Du, Y Huang, D Peng, C Lan, D Huang, S Shakeri, D So, ... International Conference on Machine Learning, 42531-42542, 2023 | 11 | 2023 |
RL-DARTS: differentiable architecture search for reinforcement learning Y Miao, X Song, D Peng, S Yue, JD Co-Reyes, E Brevdo, A Faust | 9 | 2021 |
ES-ENAS: combining evolution strategies with neural architecture search at no extra cost for reinforcement learning X Song, K Choromanski, J Parker-Holder, Y Tang, D Peng, D Jain, W Gao, ... CoRR, abs/2101.07415, 2021 | 9 | 2021 |
Training machine learning models using adaptive transfer learning V Vasudevan, R Pang, QV Le, D Peng, J Ngiam, S Kornblith US Patent App. 16/586,675, 2020 | 8 | 2020 |
Higher Layers Need More LoRA Experts C Gao, K Chen, J Rao, B Sun, R Liu, D Peng, Y Zhang, X Guo, J Yang, ... arXiv preprint arXiv:2402.08562, 2024 | 4 | 2024 |
Differentiable architecture search for reinforcement learning Y Miao, X Song, JD Co-Reyes, D Peng, S Yue, E Brevdo, A Faust International Conference on Automated Machine Learning, 20/1-17, 2022 | 3 | 2022 |
OmniPred: Language Models as Universal Regressors X Song, O Li, C Lee, D Peng, S Perel, Y Chen arXiv preprint arXiv:2402.14547, 2024 | 2 | 2024 |
Layernas: Neural architecture search in polynomial complexity Y Fan, D Alon, J Shen, D Peng, K Kumar, Y Long, X Wang, F Iliopoulos, ... arXiv preprint arXiv:2304.11517, 2023 | 2 | 2023 |
A Zero-Watermark Schema Based on Direct Wavelet Transform HUA DAI, L ZHANG, D PENG, C TAN, B LI Wavelet Analysis and Active Media Technology: (In 3 Volumes), 87-93, 2005 | 2 | 2005 |
Best Practices and Lessons Learned on Synthetic Data for Language Models R Liu, J Wei, F Liu, C Si, Y Zhang, J Rao, S Zheng, D Peng, D Yang, ... arXiv preprint arXiv:2404.07503, 2024 | 1 | 2024 |