
Isometric Propagation Network for Generalized Zeroshot Learning
Zeroshot learning (ZSL) aims to classify images of an unseen class only...
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Towards Coarse and Finegrained MultiGraph MultiLabel Learning
Multigraph multilabel learning (Mgml) is a supervised learning framewo...
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Cooperative Heterogeneous Deep Reinforcement Learning
Numerous deep reinforcement learning agents have been proposed, and each...
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Deep Reinforcement Learning with Stacked Hierarchical Attention for Textbased Games
We study reinforcement learning (RL) for textbased games, which are int...
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RatE: RelationAdaptive Translating Embedding for Knowledge Graph Completion
Many graph embedding approaches have been proposed for knowledge graph c...
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Attribute Propagation Network for Graph Zeroshot Learning
The goal of zeroshot learning (ZSL) is to train a model to classify sam...
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BiteNet: Bidirectional Temporal Encoder Network to Predict Medical Outcomes
Electronic health records (EHRs) are longitudinal records of a patient's...
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ManyClass FewShot Learning on MultiGranularity Class Hierarchy
We study manyclass fewshot (MCFS) problem in both supervised learning ...
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Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks
Modeling multivariate time series has long been a subject that has attra...
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Learning to Propagate for Graph MetaLearning
Metalearning extracts the common knowledge acquired from learning diffe...
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Attributed Graph Clustering: A Deep Attentional Embedding Approach
Graph clustering is a fundamental task which discovers communities or gr...
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Graph WaveNet for Deep SpatialTemporal Graph Modeling
Spatialtemporal graph modeling is an important task to analyze the spat...
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Prototype Propagation Networks (PPN) for Weaklysupervised Fewshot Learning on Category Graph
A variety of machine learning applications expect to achieve rapid learn...
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Search Efficient Binary Network Embedding
Traditional network embedding primarily focuses on learning a dense vect...
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Attributed Network Embedding via Subspace Discovery
Network embedding aims to learn a latent, lowdimensional vector represe...
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Learning Graph Embedding with Adversarial Training Methods
Graph embedding aims to transfer a graph into vectors to facilitate subs...
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A Comprehensive Survey on Graph Neural Networks
Deep learning has revolutionized many machine learning tasks in recent y...
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A Review for Weighted MinHash Algorithms
Data similarity (or distance) computation is a fundamental research topi...
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Fast Directional SelfAttention Mechanism
In this paper, we propose a selfattention mechanism, dubbed "fast direc...
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BiDirectional Block SelfAttention for Fast and MemoryEfficient Sequence Modeling
Recurrent neural networks (RNN), convolutional neural networks (CNN) and...
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Adversarially Regularized Graph Autoencoder
Graph embedding is an effective method to represent graph data in a low ...
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Reinforced SelfAttention Network: a Hybrid of Hard and Soft Attention for Sequence Modeling
Many natural language processing tasks solely rely on sparse dependencie...
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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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DiSAN: Directional SelfAttention Network for RNN/CNNFree Language Understanding
Recurrent neural nets (RNN) and convolutional neural nets (CNN) are wide...
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Dynamic Concept Composition for ZeroExample Event Detection
In this paper, we focus on automatically detecting events in unconstrain...
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Chengqi Zhang
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