b'Guodong Long'

Task Aligned Generative Meta-learning for Zero-shot Learning

Zero-shot learning (ZSL) refers to the problem of learning to classify i...
03/03/2021 ∙ by Zhe Liu, et al. ∙ 0 ∙ share

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Emerging Trends in Federated Learning: From Model Fusion to Federated X Learning

Federated learning is a new learning paradigm that decouples data collec...
02/25/2021 ∙ by Shaoxiong Ji, et al. ∙ 17 ∙ share

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Isometric Propagation Network for Generalized Zero-shot Learning

Zero-shot learning (ZSL) aims to classify images of an unseen class only...
02/03/2021 ∙ by Lu Liu, et al. ∙ 6 ∙ share

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SemiNLL: A Framework of Noisy-Label Learning by Semi-Supervised Learning

Deep learning with noisy labels is a challenging task. Recent prominent ...
12/02/2020 ∙ by Zhuowei Wang, et al. ∙ 0 ∙ share

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Confusable Learning for Large-class Few-Shot Classification

Few-shot image classification is challenging due to the lack of ample sa...
11/06/2020 ∙ by Bingcong Li, et al. ∙ 0 ∙ share

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Cooperative Heterogeneous Deep Reinforcement Learning

Numerous deep reinforcement learning agents have been proposed, and each...
11/02/2020 ∙ by Han Zheng, et al. ∙ 0 ∙ share

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RatE: Relation-Adaptive Translating Embedding for Knowledge Graph Completion

Many graph embedding approaches have been proposed for knowledge graph c...
10/10/2020 ∙ by Hao Huang, et al. ∙ 0 ∙ share

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Attribute Propagation Network for Graph Zero-shot Learning

The goal of zero-shot learning (ZSL) is to train a model to classify sam...
09/24/2020 ∙ by Lu Liu, et al. ∙ 10 ∙ share

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BiteNet: Bidirectional Temporal Encoder Network to Predict Medical Outcomes

Electronic health records (EHRs) are longitudinal records of a patient's...
09/24/2020 ∙ by Xueping Peng, et al. ∙ 0 ∙ share

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Many-Class Few-Shot Learning on Multi-Granularity Class Hierarchy

We study many-class few-shot (MCFS) problem in both supervised learning ...
06/28/2020 ∙ by Lu Liu, et al. ∙ 5 ∙ share

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A Universal Representation Transformer Layer for Few-Shot Image Classification

Few-shot classification aims to recognize unseen classes when presented ...
06/21/2020 ∙ by Lu Liu, et al. ∙ 0 ∙ share

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Interpretable Time-series Classification on Few-shot Samples

Recent few-shot learning works focus on training a model with prior meta...
06/03/2020 ∙ by Wensi Tang, et al. ∙ 29 ∙ share

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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...
05/24/2020 ∙ by Zonghan Wu, et al. ∙ 0 ∙ share

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Multi-Center Federated Learning

Federated learning has received great attention for its capability to tr...
05/03/2020 ∙ by Ming Xie, et al. ∙ 10 ∙ share

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Semantic Triple Encoder for Fast Open-Set Link Prediction

We improve both the open-set generalization and efficiency of link predi...
04/30/2020 ∙ by Bo Wang, et al. ∙ 0 ∙ share

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Exploiting Structured Knowledge in Text via Graph-Guided Representation Learning

In this work, we aim at equipping pre-trained language models with struc...
04/29/2020 ∙ by Tao Shen, et al. ∙ 0 ∙ share

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Rethinking 1D-CNN for Time Series Classification: A Stronger Baseline

For time series classification task using 1D-CNN, the selection of kerne...
02/24/2020 ∙ by Wensi Tang, et al. ∙ 3 ∙ share

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Self-Attention Enhanced Selective Gate with Entity-Aware Embedding for Distantly Supervised Relation Extraction

Distantly supervised relation extraction intrinsically suffers from nois...
11/27/2019 ∙ by Yang Li, et al. ∙ 0 ∙ share

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Suicidal Ideation Detection: A Review of Machine Learning Methods and Applications

Suicide is a critical issue in the modern society. Early detection and p...
10/23/2019 ∙ by Shaoxiong Ji, et al. ∙ 18 ∙ share

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Multi-Task Learning for Conversational Question Answering over a Large-Scale Knowledge Base

We consider the problem of conversational question answering over a larg...
10/11/2019 ∙ by Tao Shen, et al. ∙ 0 ∙ share

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Temporal Self-Attention Network for Medical Concept Embedding

In longitudinal electronic health records (EHRs), the event records of a...
09/15/2019 ∙ by Xueping Peng, et al. ∙ 0 ∙ share

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Learning to Propagate for Graph Meta-Learning

Meta-learning extracts the common knowledge acquired from learning diffe...
09/11/2019 ∙ by Lu Liu, et al. ∙ 3 ∙ share

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Effective Search of Logical Forms for Weakly Supervised Knowledge-Based Question Answering

Many algorithms for Knowledge-Based Question Answering (KBQA) depend on ...
09/06/2019 ∙ by Tao Shen, et al. ∙ 0 ∙ share

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Attributed Graph Clustering: A Deep Attentional Embedding Approach

Graph clustering is a fundamental task which discovers communities or gr...
06/15/2019 ∙ by Chun Wang, et al. ∙ 0 ∙ share

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Graph WaveNet for Deep Spatial-Temporal Graph Modeling

Spatial-temporal graph modeling is an important task to analyze the spat...
05/31/2019 ∙ by Zonghan Wu, et al. ∙ 0 ∙ share

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Decentralized Learning with Average Difference Aggregation for Proactive Online Social Care

The Internet and the Web are being increasingly used in proactive social...
05/19/2019 ∙ by Shaoxiong Ji, et al. ∙ 0 ∙ share

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Prototype Propagation Networks (PPN) for Weakly-supervised Few-shot Learning on Category Graph

A variety of machine learning applications expect to achieve rapid learn...
05/10/2019 ∙ by Lu Liu, et al. ∙ 6 ∙ share

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DAGCN: Dual Attention Graph Convolutional Networks

Graph convolutional networks (GCNs) have recently become one of the most...
04/04/2019 ∙ by Fengwen Chen, et al. ∙ 0 ∙ share

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Learning Graph Embedding with Adversarial Training Methods

Graph embedding aims to transfer a graph into vectors to facilitate subs...
01/04/2019 ∙ by Shirui Pan, et al. ∙ 0 ∙ share

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A Comprehensive Survey on Graph Neural Networks

Deep learning has revolutionized many machine learning tasks in recent y...
01/03/2019 ∙ by Zonghan Wu, et al. ∙ 0 ∙ share

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Learning Private Neural Language Modeling with Attentive Aggregation

Mobile keyboard suggestion is typically regarded as a word-level languag...
12/17/2018 ∙ by Shaoxiong Ji, et al. ∙ 0 ∙ share

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Distributionally Robust Semi-Supervised Learning for People-Centric Sensing

Semi-supervised learning is crucial for alleviating labelling burdens in...
11/12/2018 ∙ by Kaixuan Chen, et al. ∙ 0 ∙ share

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NeuRec: On Nonlinear Transformation for Personalized Ranking

Modelling user-item interaction patterns is an important task for person...
05/08/2018 ∙ by Shuai Zhang, et al. ∙ 0 ∙ share

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Fast Directional Self-Attention Mechanism

In this paper, we propose a self-attention mechanism, dubbed "fast direc...
05/02/2018 ∙ by Tao Shen, et al. ∙ 0 ∙ share

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Multi-modality Sensor Data Classification with Selective Attention

Multimodal wearable sensor data classification plays an important role i...
04/16/2018 ∙ by Xiang Zhang, et al. ∙ 0 ∙ share

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Bi-Directional Block Self-Attention for Fast and Memory-Efficient Sequence Modeling

Recurrent neural networks (RNN), convolutional neural networks (CNN) and...
04/03/2018 ∙ by Tao Shen, et al. ∙ 0 ∙ share

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Adversarially Regularized Graph Autoencoder

Graph embedding is an effective method to represent graph data in a low ...
02/13/2018 ∙ by Shirui Pan, et al. ∙ 0 ∙ share

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Reinforced Self-Attention Network: a Hybrid of Hard and Soft Attention for Sequence Modeling

Many natural language processing tasks solely rely on sparse dependencie...
01/31/2018 ∙ by Tao Shen, et al. ∙ 0 ∙ share

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Predicting Rich Drug-Drug Interactions via Biomedical Knowledge Graphs and Text Jointly Embedding

Minimizing adverse reactions caused by drug-drug interactions has always...
12/24/2017 ∙ by Meng Wang, et al. ∙ 0 ∙ share

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Safe Medicine Recommendation via Medical Knowledge Graph Embedding

Most of the existing medicine recommendation systems that are mainly bas...
10/16/2017 ∙ by Meng Wang, et al. ∙ 0 ∙ share

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DiSAN: Directional Self-Attention Network for RNN/CNN-Free Language Understanding

Recurrent neural nets (RNN) and convolutional neural nets (CNN) are wide...
09/14/2017 ∙ by Tao Shen, et al. ∙ 0 ∙ share

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Dynamic Concept Composition for Zero-Example Event Detection

In this paper, we focus on automatically detecting events in unconstrain...
01/14/2016 ∙ by Xiaojun Chang, et al. ∙ 0 ∙ share

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Guodong Long

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