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6 de abr. de 2022 · Test-time adaptation (TTA) seeks to tackle potential distribution shifts between training and testing data by adapting a given model w.r.t. any testing sample. This task is particularly important for deep models when the test environment changes frequently. Although some recent attempts have been made to handle this task, we still face two practical challenges: 1) existing methods have to ...
Neural architecture search (NAS) has gained increasing attention in the community of architecture design. One of the key factors behind the success lies in the training efficiency brought by the weight sharing (WS) technique. However, WS-based NAS methods often suffer from a performance disturbance (PD) issue.
Yuzhi Guo1, Jiaxiang Wu2, Hehuan Ma1, Junzhou Huang1* 1University of Texas at Arlington, Arlington, TX, 76019, USA 2Tencent AI Lab, Shenzhen, 518057, China Abstract The protein tertiary structure largely determines its interac-tion with other molecules. Despite its importance in various structure-related tasks, fully-supervised data are often time-
24 de feb. de 2023 · Test-time adaptation (TTA) has shown to be effective at tackling distribution shifts between training and testing data by adapting a given model on test samples. However, the online model updating of TTA may be unstable and this is often a key obstacle preventing existing TTA methods from being deployed in the real world. Specifically, TTA may fail to improve or even harm the model performance ...
Jiaxiang Wu Tencent AI Lab Bingzhe Wuy Tencent AI Lab Abstract Large-scale language models (LLMs) have demonstrated impressive performance, but their deployment presents challenges due to their significant memory usage. This issue can be alleviated through quantization.
31 de may. de 2023 · Qingtao Liu, Jieyuan Liu, Xiaofang Liu, Yu Wang, Song Hong, Jianbo Wu, Jiaxiang Shang, Ronghai Yu, Jungang Miao, Jianglan Shui; Surface activation of platinum group metal clusters for efficient and durable oxygen reduction in proton exchange membrane fuel cells. Appl. Phys. Rev. 1 June 2023; 10 (2): 021415.
