Zobrazeno 1 - 10
of 186
pro vyhledávání: '"Ouyang, Kai"'
Autor:
Ouyang, Kai, Xu, Xianghong, Chen, Miaoxin, Xie, Zuotong, Zheng, Hai-Tao, Song, Shuangyong, Zhao, Yu
Session-based Recommendation (SR) aims to predict users' next click based on their behavior within a short period, which is crucial for online platforms. However, most existing SR methods somewhat ignore the fact that user preference is not necessari
Externí odkaz:
http://arxiv.org/abs/2306.11610
Autor:
Ouyang, Kai, Tang, Chen, Zheng, Wenhao, Xie, Xiangjin, Xiao, Xuanji, Dong, Jian, Zheng, Hai-Tao, Wang, Zhi
One of the main challenges in modern recommendation systems is how to effectively utilize multimodal content to achieve more personalized recommendations. Despite various proposed solutions, most of them overlook the mismatch between the knowledge ga
Externí odkaz:
http://arxiv.org/abs/2305.07419
Post-click Conversion Rate (CVR) prediction task plays an essential role in industrial applications, such as recommendation and advertising. Conventional CVR methods typically suffer from the data sparsity problem as they rely only on samples where t
Externí odkaz:
http://arxiv.org/abs/2304.01169
SEAM: Searching Transferable Mixed-Precision Quantization Policy through Large Margin Regularization
Mixed-precision quantization (MPQ) suffers from the time-consuming process of searching the optimal bit-width allocation i.e., the policy) for each layer, especially when using large-scale datasets such as ISLVRC-2012. This limits the practicality of
Externí odkaz:
http://arxiv.org/abs/2302.06845
Data augmentation with \textbf{Mixup} has been proven an effective method to regularize the current deep neural networks. Mixup generates virtual samples and corresponding labels at once through linear interpolation. However, this one-stage generatio
Externí odkaz:
http://arxiv.org/abs/2206.02734
Conventional model quantization methods use a fixed quantization scheme to different data samples, which ignores the inherent "recognition difficulty" differences between various samples. We propose to feed different data samples with varying quantiz
Externí odkaz:
http://arxiv.org/abs/2204.09992
The exponentially large discrete search space in mixed-precision quantization (MPQ) makes it hard to determine the optimal bit-width for each layer. Previous works usually resort to iterative search methods on the training set, which consume hundreds
Externí odkaz:
http://arxiv.org/abs/2203.08368
Autor:
Li, Yuanfang, Zhao, Baiwei, Peng, Juzheng, Tang, Hailin, Wang, Sicheng, Peng, Sicheng, Ye, Feng, Wang, Junye, Ouyang, Kai, Li, Jianjun, Cai, Manbo, Chen, Yongming
Publikováno v:
In Drug Resistance Updates March 2024 73
Autor:
Niu, Jinfen, Zhang, Yue, Wang, Xiaoyan, Xu, Zhiliang, Ouyang, Kai, Yu, Xiaojiao, Yao, Binghua, Wei, Hong
Publikováno v:
In Colloids and Surfaces A: Physicochemical and Engineering Aspects 5 February 2024 682
Publikováno v:
In Journal of Alloys and Compounds 5 January 2024 970