Zobrazeno 1 - 10
of 44
pro vyhledávání: '"Yuandong Tian"'
Publikováno v:
Proceedings of the AAAI Conference on Artificial Intelligence. 36:8267-8276
Long Short-Term Memory (LSTM) and Transformers are two popular neural architectures used for natural language processing tasks. Theoretical results show that both are Turing-complete and can represent any context-free language (CFL).In practice, it i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f645931abb2eb2f0124ca30ce861bc5c
https://doi.org/10.1609/aaai.v36i8.20801
https://doi.org/10.1609/aaai.v36i8.20801
Publikováno v:
IEEE Antennas and Propagation Magazine. 64:128-130
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::99cc5def74293dba3b9cd950f213509d
https://doi.org/10.1109/map.2022.3196032
https://doi.org/10.1109/map.2022.3196032
Publikováno v:
Integration of Constraint Programming, Artificial Intelligence, and Operations Research ISBN: 9783031332708
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1d90491e8eb3f70fc5841e1c5151160d
https://doi.org/10.1007/978-3-031-33271-5_7
https://doi.org/10.1007/978-3-031-33271-5_7
Autor:
Weilin Cong, Yanhong Wu, Yuandong Tian, Mengting Gu, Yinglong Xia, Chun-cheng Jason Chen, Mehrdad Mahdavi
Publikováno v:
Proceedings of the 2023 SIAM International Conference on Data Mining (SDM) ISBN: 9781611977653
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b91fa404c581b7ae55e960379226f0e8
https://doi.org/10.1137/1.9781611977653.ch50
https://doi.org/10.1137/1.9781611977653.ch50
Autor:
Cheng Fu, Hanxian Huang, Bram Wasti, Chris Cummins, Riyadh Baghdadi, Kim Hazelwood, Yuandong Tian, Jishen Zhao, Hugh Leather
Publikováno v:
Proceedings of the International Conference on Parallel Architectures and Compilation Techniques.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::46508b13ec625b55fd3fb3b8e69aa6d7
https://doi.org/10.1145/3559009.3569673
https://doi.org/10.1145/3559009.3569673
Autor:
Daochen Zha, Louis Feng, Bhargav Bhushanam, Dhruv Choudhary, Jade Nie, Yuandong Tian, Jay Chae, Yinbin Ma, Arun Kejariwal, Xia Hu
Embedding learning is an important technique in deep recommendation models to map categorical features to dense vectors. However, the embedding tables often demand an extremely large number of parameters, which become the storage and efficiency bottl
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2233d85fa36040b0e144539ca89f610d
http://arxiv.org/abs/2208.06399
http://arxiv.org/abs/2208.06399
Publikováno v:
IEEE Robotics and Automation Letters. 6:2682-2689
Hierarchical learning has been successful at learning generalizable locomotion skills on walking robots in a sample-efficient manner. However, the low-dimensional "latent" action used to communicate between two layers of the hierarchy is typically us
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bd260cf047df86f53e5597ba94e22b04
https://doi.org/10.1109/lra.2021.3062342
https://doi.org/10.1109/lra.2021.3062342
Publikováno v:
IEEE Micro. 40:46-55
Neural architecture search (NAS) finds favorable network topologies for better task performance. Existing hardware-aware NAS techniques only target to reduce inference latency on single CPU/GPU systems and the searched model can hardly be parallelize
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::955691425f1b4eaba44cb4ddb9795996
https://doi.org/10.1109/mm.2020.3004538
https://doi.org/10.1109/mm.2020.3004538
Publikováno v:
SIGCOMM
Network planning is critical to the performance, reliability and cost of web services. This problem is typically formulated as an Integer Linear Programming (ILP) problem. Today's practice relies on hand-tuned heuristics from human experts to address
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::314d97601136915566d0b4e060de16d0
https://doi.org/10.1145/3452296.3472902
https://doi.org/10.1145/3452296.3472902
Autor:
Matthew Yu, Zijian He, Yuandong Tian, Kan Chen, Alvin Wan, Peter Vajda, Xiaoliang Dai, Bichen Wu, Zhen Wei, Peizhao Zhang, Joseph E. Gonzalez
Publikováno v:
CVPR
Neural Architecture Search (NAS) yields state-of-the-art neural networks that outperform their best manually-designed counterparts. However, previous NAS methods search for architectures under one set of training hyper-parameters (i.e., a training re
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6f7a86b0a55880d595ba9f9461edc42b
https://doi.org/10.1109/cvpr46437.2021.01601
https://doi.org/10.1109/cvpr46437.2021.01601