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Daiyi Peng
Daiyi Peng
Google Research, Brain Team
在 google.com 的电子邮件经过验证
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引用次数
引用次数
年份
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
3482023
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
2232022
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
1262018
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
792021
AutoHAS: Efficient hyperparameter and architecture search
X Dong, M Tan, AW Yu, D Peng, B Gabrys, QV Le
arXiv preprint arXiv:2006.03656, 2020
442020
Towards nngp-guided neural architecture search
DS Park, J Lee, D Peng, Y Cao, J Sohl-Dickstein
arXiv preprint arXiv:2011.06006, 2020
322020
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
312020
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
272021
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
252020
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
152022
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
92023
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
92021
RL-DARTS: differentiable architecture search for reinforcement learning
Y Miao, X Song, D Peng, S Yue, JD Co-Reyes, E Brevdo, A Faust
82021
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
82020
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
32022
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
22023
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
22005
OmniPred: Language Models as Universal Regressors
X Song, O Li, C Lee, D Peng, S Perel, Y Chen
arXiv preprint arXiv:2402.14547, 2024
12024
Predicting neural network performance using neural network gaussian process
J Lee, D Peng, Y Cao, JN Sohl-Dickstein, DSJ Park
US Patent App. 17/377,142, 2022
12022
Joint Architecture And Hyper-Parameter Search For Machine Learning Models
M Tan, X Dong, W Yu, QV Le, D Peng
US Patent App. 17/337,834, 2021
12021
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