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07/04/2023
Causal Reinforcement Learning: A Survey
Reinforcement learning is an essential paradigm for solving sequential d...
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06/01/2023
Improving the Robustness of Summarization Systems with Dual Augmentation
A robust summarization system should be able to capture the gist of the ...
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04/09/2023
Does Continual Learning Equally Forget All Parameters?
Distribution shift (e.g., task or domain shift) in continual learning (C...
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01/27/2023
Voting from Nearest Tasks: Meta-Vote Pruning of Pre-trained Models for Downstream Tasks
As a few large-scale pre-trained models become the major choices of vari...
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11/23/2022
Federated Learning on Non-IID Graphs via Structural Knowledge Sharing
Graph neural networks (GNNs) have shown their superiority in modeling gr...
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05/29/2022
Diminishing Empirical Risk Minimization for Unsupervised Anomaly Detection
Unsupervised anomaly detection (AD) is a challenging task in realistic a...
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02/13/2022
On the Convergence of Clustered Federated Learning
In a federated learning system, the clients, e.g. mobile devices and org...
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09/21/2021
Generalization in Text-based Games via Hierarchical Reinforcement Learning
Deep reinforcement learning provides a promising approach for text-based...
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08/24/2021
Federated Learning for Open Banking
Open banking enables individual customers to own their banking data, whi...
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07/20/2021
MIMO: Mutual Integration of Patient Journey and Medical Ontology for Healthcare Representation Learning
Healthcare representation learning on the Electronic Health Record (EHR)...
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07/10/2021
Beyond Low-pass Filtering: Graph Convolutional Networks with Automatic Filtering
Graph convolutional networks are becoming indispensable for deep learnin...
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02/03/2021
Isometric Propagation Network for Generalized Zero-shot Learning
Zero-shot learning (ZSL) aims to classify images of an unseen class only...
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12/19/2020
Towards Coarse and Fine-grained Multi-Graph Multi-Label Learning
Multi-graph multi-label learning (Mgml) is a supervised learning framewo...
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11/02/2020
Cooperative Heterogeneous Deep Reinforcement Learning
Numerous deep reinforcement learning agents have been proposed, and each...
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10/22/2020
Deep Reinforcement Learning with Stacked Hierarchical Attention for Text-based Games
We study reinforcement learning (RL) for text-based games, which are int...
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10/10/2020
RatE: Relation-Adaptive Translating Embedding for Knowledge Graph Completion
Many graph embedding approaches have been proposed for knowledge graph c...
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09/24/2020
Attribute Propagation Network for Graph Zero-shot Learning
The goal of zero-shot learning (ZSL) is to train a model to classify sam...
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09/24/2020
BiteNet: Bidirectional Temporal Encoder Network to Predict Medical Outcomes
Electronic health records (EHRs) are longitudinal records of a patient's...
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06/28/2020
Many-Class Few-Shot Learning on Multi-Granularity Class Hierarchy
We study many-class few-shot (MCFS) problem in both supervised learning ...
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05/24/2020
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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09/11/2019
Learning to Propagate for Graph Meta-Learning
Meta-learning extracts the common knowledge acquired from learning diffe...
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06/15/2019
Attributed Graph Clustering: A Deep Attentional Embedding Approach
Graph clustering is a fundamental task which discovers communities or gr...
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05/31/2019
Graph WaveNet for Deep Spatial-Temporal Graph Modeling
Spatial-temporal graph modeling is an important task to analyze the spat...
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05/10/2019
Prototype Propagation Networks (PPN) for Weakly-supervised Few-shot Learning on Category Graph
A variety of machine learning applications expect to achieve rapid learn...
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01/14/2019
Search Efficient Binary Network Embedding
Traditional network embedding primarily focuses on learning a dense vect...
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01/14/2019
Attributed Network Embedding via Subspace Discovery
Network embedding aims to learn a latent, low-dimensional vector represe...
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01/04/2019
Learning Graph Embedding with Adversarial Training Methods
Graph embedding aims to transfer a graph into vectors to facilitate subs...
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01/03/2019
A Comprehensive Survey on Graph Neural Networks
Deep learning has revolutionized many machine learning tasks in recent y...
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11/12/2018
A Review for Weighted MinHash Algorithms
Data similarity (or distance) computation is a fundamental research topi...
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05/02/2018
Fast Directional Self-Attention Mechanism
In this paper, we propose a self-attention mechanism, dubbed "fast direc...
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04/03/2018
Bi-Directional Block Self-Attention for Fast and Memory-Efficient Sequence Modeling
Recurrent neural networks (RNN), convolutional neural networks (CNN) and...
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02/13/2018
Adversarially Regularized Graph Autoencoder
Graph embedding is an effective method to represent graph data in a low ...
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01/31/2018
Reinforced Self-Attention 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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11/02/2017
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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09/14/2017
DiSAN: Directional Self-Attention Network for RNN/CNN-Free Language Understanding
Recurrent neural nets (RNN) and convolutional neural nets (CNN) are wide...
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01/14/2016
