46 Result(s)
within
English
Artificial Intelligence
Information Storage and Retrieval
Dong-**ng Wang
Conference Paper
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Chapter and Conference Paper
User Interaction-Aware Knowledge Graphs for Recommender Systems
The performance of recommender systems can be improved effectively by using knowledge graphs as auxiliary information. However, most of the knowledge graph-based recommendations focus on learning item represen...
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Chapter and Conference Paper
Graph Attention Networks for New Product Sales Forecasting in E-Commerce
Aiming to discover competitive new products, sales forecasting has been playing an increasingly important role in real-world E-Commerce systems. Current methods either only utilize historical sales records wit...
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Chapter and Conference Paper
Relation Classification in Scientific Papers Based on Convolutional Neural Network
Scientific papers are important for scholars to track trends in specific research areas. With the increase in the number of scientific papers, it is difficult for scholars to read all the papers to extract eme...
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Chapter and Conference Paper
A Dynamic Decision-Making Method Based on Ensemble Methods for Complex Unbalanced Data
Class imbalance has been proven to seriously hinder the precision of many standard learning algorithms. To solve this problem, a number of methods have been proposed, for example, the distance-based balancing ...
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Chapter and Conference Paper
Multi-view Spectral Clustering via Multi-view Weighted Consensus and Matrix-Decomposition Based Discretization
In recent years, multi-view clustering has been widely used in many areas. As an important category of multi-view clustering, multi-view spectral clustering has recently shown promising advantages in partition...
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Chapter and Conference Paper
Fine-Grained Segmentation Using Hierarchical Dilated Neural Networks
Image segmentation is a crucial step in many computer-aided medical image analysis tasks, e.g., automated radiation therapy. However, low tissue-contrast and large amounts of artifacts in medical images, i.e., CT...
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Chapter and Conference Paper
Cascaded LSTMs Based Deep Reinforcement Learning for Goal-Driven Dialogue
This paper proposes a deep neural network model for jointly modeling Natural Language Understanding and Dialogue Management in goal-driven dialogue systems. There are three parts in this model. A Long Short-Te...
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Chapter and Conference Paper
Chinese Governmental Named Entity Recognition
Named entity recognition (NER) is a fundamental task in natural language processing and there is a lot of interest on vertical NER such as medical NER, short text NER etc. In this paper, we study the problem o...
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Chapter and Conference Paper
Jointly Modeling Intent Identification and Slot Filling with Contextual and Hierarchical Information
Intent classification and slot filling are two critical subtasks of natural language understanding (NLU) in task-oriented dialogue systems. Previous work has made use of either hierarchical or contextual infor...
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Chapter and Conference Paper
Deep Attentional Features for Prostate Segmentation in Ultrasound
Automatic prostate segmentation in transrectal ultrasound (TRUS) is of essential importance for image-guided prostate biopsy and treatment planning. However, develo** such automatic solutions remains very ch...
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Chapter and Conference Paper
Generalizing Deep Models for Ultrasound Image Segmentation
Deep models are subject to performance drop when encountering appearance discrepancy, even on congeneric corpus in which objects share the similar structure but only differ slightly in appearance. This perform...
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Chapter and Conference Paper
Using Crowdsourcing for Fine-Grained Entity Type Completion in Knowledge Bases
Recent years have witnessed the proliferation of large-scale Knowledge Bases (KBs). However, many entities in KBs have incomplete type information, and some are totally untyped. Even worse, fine-grained types (e....
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Chapter and Conference Paper
Rules for Inducing Hierarchies from Social Tagging Data
Automatic generation of hierarchies from social tags is a challenging task. We identified three rules, set inclusion, graph centrality and information-theoretic condition from the literature and proposed two n...
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Chapter and Conference Paper
Community Detection in Graph Streams by Pruning Zombie Nodes
Detecting communities in graph streams has attracted a large amount of attention recently. Although many algorithms have been developed from different perspectives, there is still a limitation to the existing ...
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Chapter and Conference Paper
Multi-context Deep Convolutional Features and Exemplar-SVMs for Scene Parsing
Scene parsing is a challenging task in computer vision field. The work of scene parsing is labeling every pixel in an image with its semantic category to which it belongs. In this paper, we solve this problem ...
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Chapter and Conference Paper
High Capacity Reversible Data Hiding with Contrast Enhancement
Reversible data hiding aims at recovering exactly the cover image from the marked image after extracting the hidden data. Reversible data hiding with contrast enhancement proposed by Wu et al. achieved a good eff...
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Chapter and Conference Paper
Quality Assessment of Palm Vein Image Using Natural Scene Statistics
Image quality has a great influence on the performance of non-contact biometric identification system. In order to acquire palm vein image with high-quality, an image quality assessment algorithm for palm vein...
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Chapter and Conference Paper
Image Forgery Detection Based on Semantic Image Understanding
Image forensics has been focusing on low-level visual features, paying little attention to high-level semantic information of the image. In this work, we propose the framework for image forgery detection based...
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Chapter and Conference Paper
Relation Classification: CNN or RNN?
Convolutional neural networks (CNN) have delivered competitive performance on relation classification, without tedious feature engineering. A particular shortcoming of CNN, however, is that it is less powerful...
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Chapter and Conference Paper
Deep and Sparse Learning in Speech and Language Processing: An Overview
Large-scale deep neural models, e.g., deep neural networks (DNN) and recurrent neural networks (RNN), have demonstrated significant success in solving various challenging tasks of speech and language processin...
