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Sparse-Interest Network for Sequential Recommendation
Recent methods in sequential recommendation focus on learning an overall...
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Learning on Attribute-Missing Graphs
Graphs with complete node attributes have been widely explored recently....
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Decoupled Variational Embedding for Signed Directed Networks
Node representation learning for signed directed networks has received c...
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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 ...
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Node Attribute Generation on Graphs
Graph structured data provide two-fold information: graph structures and...
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Safeguarded Dynamic Label Regression for Generalized Noisy Supervision
Learning with noisy labels, which aims to reduce expensive labors on acc...
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How Does Disagreement Benefit Co-teaching?
Learning with noisy labels is one of the most important question in weak...
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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...
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Variational Collaborative Learning for User Probabilistic Representation
Collaborative filtering (CF) has been successfully employed by many mode...
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Understanding VAEs in Fisher-Shannon Plane
In information theory, Fisher information and Shannon information (entro...
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Masking: A New Perspective of Noisy Supervision
It is important to learn classifiers under noisy labels due to their ubi...
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Variational Composite Autoencoders
Learning in the latent variable model is challenging in the presence of ...
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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...
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Collaborative Learning for Weakly Supervised Object Detection
Weakly supervised object detection has recently received much attention,...
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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...
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