| 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 | 107 | 2018 |
| 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 | 64 | 2022 |
| 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 | 48 | 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 | 31 | 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 | 22 | 2021 |
| 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 | 22 | 2020 |
| Towards nngp-guided neural architecture search DS Park, J Lee, D Peng, Y Cao, J Sohl-Dickstein arXiv preprint arXiv:2011.06006, 2020 | 19 | 2020 |
| 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 | 19 | 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 | 8 | 2022 |
| 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 | 7 | 2021 |
| RL-DARTS: differentiable architecture search for reinforcement learning Y Miao, X Song, D Peng, S Yue, JD Co-Reyes, E Brevdo, A Faust | 5 | 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 | 4 | 2020 |
| ES-ENAS: Blackbox optimization over hybrid spaces via combinatorial and continuous evolution X Song, K Choromanski, J Parker-Holder, Y Tang, Q Zhang, D Peng, ... arXiv preprint arXiv:2101.07415, 2021 | 1 | 2021 |
| NAHAS: Neural Architecture and Hardware Accelerator Search Y Zhou, X Dong, D Peng, E Zhu, A Yazdanbakhsh, B Akin, M Tan, ... | 1 | 2021 |
| Towards nngp-guided neural architecture search D Peng, DS Park, J Lee, J Sohl-dickstein, Y Cao | 1 | 2020 |
| Effective descriptive model of E-commerce merchandise and the multi-website searching mechanism D PENG, D YIN, B LI Journal of Computer Applications 25 (02), 472, 2005 | 1 | 2005 |
| 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 | 1 | 2005 |
| PyGlove: Efficiently Exchanging ML Ideas as Code D Peng, X Dong, E Real, Y Lu, QV Le arXiv preprint arXiv:2302.01918, 2023 | | 2023 |
| Reinforcement learning algorithm search JD Co-Reyes, Y Miao, D Peng, SV Levine, QV Le, LEE Honglak, A Faust US Patent App. 17/338,093, 2022 | | 2022 |
| 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 | | 2022 |
