1 code implementation • NeurIPS 2021 • Kuan-Chieh Wang, Yan Fu, Ke Li, Ashish Khisti, Richard Zemel, Alireza Makhzani
In this work, we provide a probabilistic interpretation of model inversion attacks, and formulate a variational objective that accounts for both diversity and accuracy.
1 code implementation • 31 Aug 2021 • Zhouyang Xie, Yan Fu, Shengzhao Tian, Junlin Zhou, DuanBing Chen
In this paper, a highly efficient pruning method is proposed to significantly reduce the cost of pruning DCNN.
no code implementations • 21 Nov 2018 • Ya-Hui An, Liangming Pan, Min-Yen Kan, Qiang Dong, Yan Fu
We propose the novel problem of learning resource mention identification in MOOC forums.
no code implementations • ACL 2020 • Fei Yuan, Linjun Shou, Xuanyu Bai, Ming Gong, Yaobo Liang, Nan Duan, Yan Fu, Daxin Jiang
Multilingual pre-trained models could leverage the training data from a rich source language (such as English) to improve performance on low resource languages.
no code implementations • 15 May 2020 • Ya-Hui An, Muthu Kumar Chandresekaran, Min-Yen Kan, Yan Fu
We demonstrate the feasibility of this approach to the automatic identification, linking and resolution -- a task known as Wikification -- of learning resources mentioned on MOOC discussion forums, from a harvested collection of 100K+ resources.
no code implementations • 17 Sep 2020 • Po Li, Lei LI, Yan Fu, Jun Rong, Yu Zhang
At top of the MoE layer, we deploy a transformer layer for each task as task tower to learn task-specific information.
no code implementations • 11 Dec 2020 • Fei Yuan, Linjun Shou, Jian Pei, Wutao Lin, Ming Gong, Yan Fu, Daxin Jiang
When multiple teacher models are available in distillation, the state-of-the-art methods assign a fixed weight to a teacher model in the whole distillation.
no code implementations • 12 May 2021 • Faez Ahmed, Yaxin Cui, Yan Fu, Wei Chen
By representing products as nodes and their relationships as edges of a graph, we show how an inductive graph neural network approach, named GraphSAGE, can efficiently learn continuous representations for nodes and edges.
no code implementations • 19 Jun 2021 • En-Yu Yu, Yan Fu, Jun-Lin Zhou, Hong-Liang Sun, Duan-Bing Chen
Many real-world systems can be expressed in temporal networks with nodes playing far different roles in structure and function and edges representing the relationships between nodes.
no code implementations • 20 Jan 2022 • Enyu Yu, DuanBing Chen, Yan Fu, Yuanyuan Xu
Because of its wide application, critical nodes identification has become an important research topic at the micro level of network science.
no code implementations • 30 Nov 2023 • Weilian Song, Jieliang Luo, Dale Zhao, Yan Fu, Chin-Yi Cheng, Yasutaka Furukawa
This paper proposes an assistive system for architects that converts a large-scale point cloud into a standardized digital representation of a building for Building Information Modeling (BIM) applications.