no code implementations • 25 Sep 2024 • Yuanyong Luo, Zhongxing Zhang, Richard Wu, Hu Liu, Ying Jin, Kai Zheng, Minmin Wang, Zhanying He, Guipeng Hu, Luyao Chen, Tianchi Hu, Junsong Wang, Minqi Chen, Mikhaylov Dmitry, Korviakov Vladimir, Bobrin Maxim, Yuhao Hu, Guanfu Chen, Zeyi Huang
This preliminary white paper proposes a novel 8-bit floating-point data format HiFloat8 (abbreviated as HiF8) for deep learning.
no code implementations • 17 Sep 2024 • Wentian Bao, Hu Liu, Kai Zheng, Chao Zhang, Shunyu Zhang, Enyun Yu, Wenwu Ou, Yang song
Personalized search has been extensively studied in various applications, including web search, e-commerce, social networks, etc.
no code implementations • 12 Jun 2024 • Shunyu Zhang, Hu Liu, Wentian Bao, Enyun Yu, Yang song
Second, existing multi-objective methods apply the two inherently conflicting loss functions on a single probabilistic prediction, which results in a sub-optimal trade-off between calibration and ranking.
no code implementations • 12 Dec 2021 • Yaofang Liu, Xinyue Zhang, Wenlong Wan, Shaoyu Liu, Yingdi Liu, Hu Liu, Xueying Zeng, Qing Zhang
Two vascular stenosis detection methods are proposed to assist the diagnosis.
no code implementations • 3 Aug 2021 • Yaofang Liu, Xinyue Zhang, Wenlong Wan, Shaoyu Liu, Yingdi Liu, Hu Liu, Xueying Zeng, Qing Zhang
Two vascular stenosis detection methods are proposed to assist the diagnosis.
no code implementations • NeurIPS 2020 • Hu Liu, Jing Lu, Xiwei Zhao, Sulong Xu, Hao Peng, Yutong Liu, Zehua Zhang, Jian Li, Junsheng Jin, Yongjun Bao, Weipeng Yan
First, conventional attentions mostly limit the attention field only to a single user's behaviors, which is not suitable in e-commerce where users often hunt for new demands that are irrelevant to any historical behaviors.
no code implementations • 18 Jun 2020 • Hu Liu, Jing Lu, Hao Yang, Xiwei Zhao, Sulong Xu, Hao Peng, Zehua Zhang, Wenjie Niu, Xiaokun Zhu, Yongjun Bao, Weipeng Yan
Existing algorithms usually extract visual features using off-the-shelf Convolutional Neural Networks (CNNs) and late fuse the visual and non-visual features for the finally predicted CTR.
1 code implementation • IEEE Transactions on Multimedia 2019 • Runpeng Cui, Hu Liu, ChangShui Zhang
In contrast, our proposed architecture adopts deep convolutional neural networks with stacked temporal fusion layers as the feature extraction module, and bi-directional recurrent neural networks as the sequence learning module.
Ranked #13 on
Sign Language Recognition
on RWTH-PHOENIX-Weather 2014
1 code implementation • NeurIPS 2018 • Hu Liu, Sheng Jin, Chang-Shui Zhang
Connectionist Temporal Classification (CTC) is an objective function for end-to-end sequence learning, which adopts dynamic programming algorithms to directly learn the mapping between sequences.
no code implementations • CVPR 2017 • Runpeng Cui, Hu Liu, Chang-Shui Zhang
This work presents a weakly supervised framework with deep neural networks for vision-based continuous sign language recognition, where the ordered gloss labels but no exact temporal locations are available with the video of sign sentence, and the amount of labeled sentences for training is limited.