Jiangchao Yao

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University of Technology Sydney

Sparse-Interest Network for Sequential Recommendation

Recent methods in sequential recommendation focus on learning an overall...
02/18/2021 ∙ by Qiaoyu Tan, et al. ∙ 0 ∙ share

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Learning on Attribute-Missing Graphs

Graphs with complete node attributes have been widely explored recently....
11/03/2020 ∙ by Xu Chen, et al. ∙ 3 ∙ share

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Decoupled Variational Embedding for Signed Directed Networks

Node representation learning for signed directed networks has received c...
08/28/2020 ∙ by Xu Chen, et al. ∙ 7 ∙ share

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Bayes EMbedding (BEM): Refining Representation by Integrating Knowledge Graphs and Behavior-specific Networks

Low-dimensional embeddings of knowledge graphs and behavior graphs have ...
08/28/2019 ∙ by Yuting Ye, et al. ∙ 0 ∙ share

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Node Attribute Generation on Graphs

Graph structured data provide two-fold information: graph structures and...
07/23/2019 ∙ by Xu Chen, et al. ∙ 0 ∙ share

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Safeguarded Dynamic Label Regression for Generalized Noisy Supervision

Learning with noisy labels, which aims to reduce expensive labors on acc...
03/06/2019 ∙ by Jiangchao Yao, et al. ∙ 2 ∙ share

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How Does Disagreement Benefit Co-teaching?

Learning with noisy labels is one of the most important question in weak...
01/14/2019 ∙ by Xingrui Yu, et al. ∙ 0 ∙ share

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Pumpout: A Meta Approach for Robustly Training Deep Neural Networks with Noisy Labels

It is challenging to train deep neural networks robustly on the industri...
09/28/2018 ∙ by Bo Han, et al. ∙ 8 ∙ share

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Variational Collaborative Learning for User Probabilistic Representation

Collaborative filtering (CF) has been successfully employed by many mode...
09/22/2018 ∙ by Kenan Cui, et al. ∙ 0 ∙ share

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Understanding VAEs in Fisher-Shannon Plane

In information theory, Fisher information and Shannon information (entro...
07/10/2018 ∙ by Huangjie Zheng, et al. ∙ 0 ∙ share

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Masking: A New Perspective of Noisy Supervision

It is important to learn classifiers under noisy labels due to their ubi...
05/21/2018 ∙ by Bo Han, et al. ∙ 0 ∙ share

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Variational Composite Autoencoders

Learning in the latent variable model is challenging in the presence of ...
04/12/2018 ∙ by Jiangchao Yao, et al. ∙ 0 ∙ share

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Degeneration in VAE: in the Light of Fisher Information Loss

Variational Autoencoder (VAE) is one of the most popular generative mode...
02/19/2018 ∙ by Huangjie Zheng, et al. ∙ 0 ∙ share

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Collaborative Learning for Weakly Supervised Object Detection

Weakly supervised object detection has recently received much attention,...
02/10/2018 ∙ by Jiajie Wang, et al. ∙ 0 ∙ share

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Deep Learning from Noisy Image Labels with Quality Embedding

There is an emerging trend to leverage noisy image datasets in many visu...
11/02/2017 ∙ by Jiangchao Yao, et al. ∙ 0 ∙ share

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Jiangchao Yao

Featured Co-authors

Masashi Sugiyama

Rui Zhang

Xu Chen

Mingyuan Zhou

Gang Niu

Xia Hu

Ya Zhang

Jun Sun

Bo Han

Ivor W. Tsang

Hongxia Yang

Sparse-Interest Network for Sequential Recommendation

Learning on Attribute-Missing Graphs

Decoupled Variational Embedding for Signed Directed Networks

Bayes EMbedding (BEM): Refining Representation by Integrating Knowledge Graphs and Behavior-specific Networks

Node Attribute Generation on Graphs

Safeguarded Dynamic Label Regression for Generalized Noisy Supervision

How Does Disagreement Benefit Co-teaching?

Pumpout: A Meta Approach for Robustly Training Deep Neural Networks with Noisy Labels

Variational Collaborative Learning for User Probabilistic Representation

Understanding VAEs in Fisher-Shannon Plane

Masking: A New Perspective of Noisy Supervision

Variational Composite Autoencoders

Degeneration in VAE: in the Light of Fisher Information Loss

Collaborative Learning for Weakly Supervised Object Detection

Deep Learning from Noisy Image Labels with Quality Embedding