
Exploiting Heterogeneous Graph Neural Networks with Latent Worker/Task Correlation Information for Label Aggregation in Crowdsourcing
Crowdsourcing has attracted much attention for its convenience to collec...
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Deep Graph Matching and Searching for Semantic Code Retrieval
Code retrieval is to find the code snippet from a large corpus of source...
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Stronger Transformers for Neural MultiHop Question Generation
Prior work on automated question generation has almost exclusively focus...
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Technical Question Answering across Tasks and Domains
Building automatic technical support system is an important yet challeng...
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Unsupervised ReferenceFree Summary Quality Evaluation via Contrastive Learning
Evaluation of a document summarization system has been a critical factor...
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Hierarchical Graph Matching Networks for Deep Graph Similarity Learning
While the celebrated graph neural networks yield effective representatio...
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Iterative Deep Graph Learning for Graph Neural Networks: Better and Robust Node Embeddings
In this paper, we propose an endtoend graph learning framework, namely...
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Interpretable Deep Graph Generation with NodeEdge CoDisentanglement
Disentangled representation learning has recently attracted a significan...
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A MultiPerspective Architecture for Semantic Code Search
The ability to match pieces of code to their corresponding natural langu...
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Crossing Variational Autoencoders for Answer Retrieval
Answer retrieval is to find the most aligned answer from a large set of ...
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Knowledge GraphAugmented Abstractive Summarization with SemanticDriven Cloze Reward
Sequencetosequence models for abstractive summarization have been stud...
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Toward Subgraph Guided Knowledge Graph Question Generation with Graph Neural Networks
Knowledge graph question generation (QG) aims to generate natural langua...
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Improved Automatic Summarization of Subroutines via Attention to File Context
Software documentation largely consists of short, natural language summa...
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Deep Iterative and Adaptive Learning for Graph Neural Networks
In this paper, we propose an endtoend graph learning framework, namely...
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Efficient Global String Kernel with Random Features: Beyond Counting Substructures
Analysis of largescale sequential data has been one of the most crucial...
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KerGM: Kernelized Graph Matching
Graph matching plays a central role in such fields as computer vision, p...
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Scalable Global Alignment Graph Kernel Using Random Features: From Node Embedding to Graph Embedding
Graph kernels are widely used for measuring the similarity between graph...
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Improving Graph Neural Network Representations of Logical Formulae with Subgraph Pooling
Recent advances in the integration of deep learning with automated theor...
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Natural Question Generation with Reinforcement Learning Based GraphtoSequence Model
Natural question generation (QG) aims to generate questions from a passa...
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MUTLA: A LargeScale Dataset for Multimodal Teaching and Learning Analytics
Automatic analysis of teacher and student interactions could be very imp...
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Multistage Deep Classifier Cascades for Open World Recognition
At present, object recognition studies are mostly conducted in a closed ...
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DynGraph2Seq: DynamicGraphtoSequence Interpretable Learning for Health Stage Prediction in Online Health Forums
Online health communities such as the online breast cancer forum enable ...
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Reinforcement Learning Based GraphtoSequence Model for Natural Question Generation
Natural question generation (QG) is a challenging yet rewarding task, th...
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GraphFlow: Exploiting Conversation Flow with Graph Neural Networks for Conversational Machine Comprehension
Conversational machine reading comprehension (MRC) has proven significan...
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Attacking Graph Convolutional Networks via Rewiring
Graph Neural Networks (GNNs) have boosted the performance of many graph ...
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Reinforcement Learning Based Text Style Transfer without Parallel Training Corpus
Text style transfer rephrases a text from a source style (e.g., informal...
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Bidirectional Attentive Memory Networks for Question Answering over Knowledge Bases
When answering natural language questions over knowledge bases (KB), dif...
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High Fidelity Vector Space Models of Structured Data
Machine learning systems regularly deal with structured data in realwor...
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DQ Scheduler: Deep Reinforcement Learning Based Controller Synchronization in Distributed SDN
In distributed softwaredefined networks (SDN), multiple physical SDN co...
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Discrete Attacks and Submodular Optimization with Applications to Text Classification
Adversarial examples are carefully constructed modifications to an input...
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The Hidden Shape of Stories Reveals Positivity Bias and Gender Bias
To capture the shape of stories is crucial for understanding the mind of...
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Word Mover's Embedding: From Word2Vec to Document Embedding
While the celebrated Word2Vec technique yields semantically rich represe...
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Random Warping Series: A Random Features Method for TimeSeries Embedding
Time series data analytics has been a problem of substantial interests f...
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SQLtoText Generation with GraphtoSequence Model
Previous work approaches the SQLtotext generation task using vanilla S...
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Revisiting Random Binning Features: Fast Convergence and Strong Parallelizability
Kernel method has been developed as one of the standard approaches for n...
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Exploiting Rich Syntactic Information for Semantic Parsing with GraphtoSequence Model
Existing neural semantic parsers mainly utilize a sequence encoder, i.e....
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Quantized Densely Connected UNets for Efficient Landmark Localization
In this paper, we propose quantized densely connected UNets for efficie...
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Financial Forecasting and Analysis for LowWage Workers
Despite the plethora of financial services and products on the market no...
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Fast Incremental von Neumann Graph Entropy Computation: Theory, Algorithm, and Applications
The von Neumann graph entropy (VNGE) facilitates the measure of informat...
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Deep Graph Translation
Inspired by the tremendous success of deep generative models on generati...
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Scalable Spectral Clustering Using Random Binning Features
Spectral clustering is one of the most effective clustering approaches t...
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Graph2Seq: Graph to Sequence Learning with Attentionbased Neural Networks
Celebrated Sequence to Sequence learning (Seq2Seq) and its fruitful vari...
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D2KE: From Distance to Kernel and Embedding
For many machine learning problem settings, particularly with structured...
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TRPL+K: ThickRestart Preconditioned Lanczos+K Method for Large Symmetric Eigenvalue Problems
The Lanczos method is one of the standard approaches for computing a few...
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Revisiting Spectral Graph Clustering with Generative Community Models
The methodology of community detection can be divided into two principle...
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Large Teams Have Developed Science and Technology; Small Teams Have Disrupted It
Teams dominate the production of highimpact science and technology. Ana...
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Similarity Preserving Representation Learning for Time Series Analysis
A considerable amount of machine learning algorithms take instancefeatu...
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PRIMME_SVDS: A HighPerformance Preconditioned SVD Solver for Accurate LargeScale Computations
The increasing number of applications requiring the solution of large sc...
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Lingfei Wu
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