We evaluate the effectiveness of the proposed model in terms of both accurate and calibrated sequential recommendation.
no code implementations • 28 Aug 2022 • Chengshuo Shen, Wei Zheng, Yonghua Ding, Xinkun Ai, Fengming Xue, Yu Zhong, Nengchao Wang, Li Gao, Zhipeng Chen, Zhoujun Yang, Zhongyong Chen, Yuan Pan, J-TEXT team
Understanding why a predictor makes a certain prediction can be as crucial as the prediction's accuracy for future tokamak disruption predictors.
no code implementations • 20 Aug 2022 • Wei Zheng, Fengming Xue, Ming Zhang, Zhongyong Chen, Chengshuo Shen, Xinkun Ai, Nengchao Wang, Dalong Chen, Bihao Guo, Yonghua Ding, Zhipeng Chen, Zhoujun Yang, Biao Shen, Bingjia Xiao, Yuan Pan
Based on the feature extractor trained on J-TEXT, the disruption prediction model was transferred to EAST data with mere 20 discharges from EAST experiment.
In this work, we focus on the calibrated recommendations for sequential recommendation, which is connected to both fairness and diversity.
Accurate predictions in session-based recommendations have progressed, but a few studies have focused on skewed recommendation lists caused by popularity bias.
We propose a space partition approach to solve the game iteratively and show that the value function of the leader is piece-wise linear and the value function of the follower is piece-wise constant for multiple stages.
Existing papers achieve good results when constructing the images of subjects who are in the prior training samples.
Moreover, the multi-view fusion loss, which consists of the segmentation loss, the transition loss and the decision loss, is proposed to facilitate the training process of multi-view learning networks so as to keep the consistency of appearance and space, not only in the process of fusing segmentation results, but also in the process of training the learning network.
Based on the new encoding method and the two objectives, a multiobjective evolutionary algorithm (MOEA) based upon NSGA-II, termed as continuous encoding MOEA, is developed for the transformed community detection problem with continuous decision variables.
As the infection of 2019-nCoV coronavirus is quickly developing into a global pneumonia epidemic, careful analysis of its transmission and cellular mechanisms is sorely needed.
Precision medicine is becoming a focus in medical research recently, as its implementation brings values to all stakeholders in the healthcare system.
Our method is built as an end-to-end framework for segmentation and classification.
In this paper, we propose a method for air quality measurement based on double-channel convolutional neural network ensemble learning to solve the problem of feature extraction for different parts of environmental images.
The rapid development of modern technology facilitates the appearance of numerous unprecedented complex data which do not satisfy the axioms of Euclidean geometry, while most of the statistical hypothesis tests are available in Euclidean or Hilbert spaces.
Results In this article, we present a hierarchical recurrent neural networks (RNNs)-based method to integrate the SDP and sentence sequence for DDI extraction task.