Neural Information Processing

Name: Neural Information Processing | 25th International Conference, ICONIP 2018, Siem Reap, Cambodia, December 13-16, 2018, Proceedings, Part III
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25th International Conference, ICONIP 2018, Siem Reap, Cambodia, December 13-16, 2018, Proceedings, Part III

Long Cheng Andrew Chi Sing Leung Seiichi Ozawa(Editor)

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Published on 3. December 2018

XXI, 687 pages

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Content

Intro
Preface
ICONIP 2018 Organization
Contents - Part III
Embedding Learning
fMRI Semantic Category Decoding Using Linguistic Encoding of Word Embeddings
1 Introduction
2 Motivation for Using Word-Embeddings
3 Proposed Approach
4 Experimental Results and Observations
4.1 FMRI Dataset Description
4.2 Architecture Used and Training Strategy
4.3 Statistical Analysis of Predicted fMRI Images
4.4 Mapping Semantics onto the Brain
4.5 Statistical Analysis Across Subjects
5 Conclusion
References
Named Entity Disambiguation via Probabilistic Graphical Model with Embedding Features
1 Introduction
2 Related Work
3 Methodology
3.1 Model Representation
3.2 Features
3.3 Inference and Learning
4 Experiment
5 Conclusions
References
Category-Embodied Knowledge Embedding
1 Introduction
2 Related Work
3 Methology
3.1 Preliminaries
3.2 Semantic Category Embedding
3.3 Joint Embedding
3.4 Learning Model
4 Inference
5 Experiments and Analysis
5.1 Link Prediction
5.2 Triple Classification
6 Conclusion
References
Unsupervised Ensemble Learning Based on Graph Embedding for Image Clustering
1 Introduction
2 Unsupervised Ensemble Learning Based on Graph Embedding
2.1 ULGE
2.2 Diversity in UEL-GE
2.3 Combination Rule in UEL-GE
3 Experimental Results
3.1 Data Sets
3.2 Parameter Setting
3.3 Evaluation Metric
3.4 Results Analysis
3.5 Parameter Analysis
4 Conclusions
References
Potential Probability of Negative Triples in Knowledge Graph Embedding
Abstract
1 Introduction
2 Related Work
3 Our Method
3.1 Training
3.2 Generating Negative Triples with Potential Probability
3.3 Optimization and Implementation Details
4 Experiment and Analysis
4.1 Datasets
4.2 Experimental Setting
4.3 Link Prediction
5 Conclusion and Future Work
References
Event Causality Identification by Modeling Events and Relation Embedding
Abstract
1 Introduction
2 Related Work
2.1 Siamese Network
2.2 Causality Relation Identification
3 Proposed Model
3.1 Structure of Proposed Model
3.2 Event Representation Generation
3.3 Training Relation Embedding
4 Experimental Result
4.1 Experiment Dataset
4.2 Event Causality Identification
4.3 Event Recognition
5 Discussion and Conclusion
References
Topic-Bigram Enhanced Word Embedding Model
1 Introduction
2 Related Work
2.1 Neural Language Models
2.2 Integrating Auxiliary Knowledge
2.3 Topic and Semantic Language Models
3 Our Models
3.1 CBOW and Skip-Gram
3.2 TBWE-1
3.3 TBWE-2
3.4 Optimization and Parameter Estimation
4 Experiments
4.1 Experimental Setup
4.2 Qualitative Analysis
4.3 Syntactic Word Analogy Task
4.4 Word Similarity Task
4.5 Compliexity Analysis
5 Conclusion and Future Work
References
Hybridized Character-Word Embedding for Korean Traditional Document Translation
Abstract
1 Introduction
2 Related Works
2.1 Long Short-Term Memory (LSTM)
2.2 Sequence-to-Sequence Model
3 Proposed Model
3.1 Hybrid of Word and Character Embedding
3.2 Attention Mechanism
4 Experiments
4.1 Dataset
4.2 Implementation Details
4.3 Results and Discussion
5 Conclusion
Acknowledgments
References
Word Embedding Based on Low-Rank Doubly Stochastic Matrix Decomposition
1 Introduction
2 Brief Review of Previous Word Embedding Methods
3 Low-Rank Doubly Stochastic Matrix Decomposition
4 Optimization
5 Experiments
6 Conclusion
References
Meta-path Based Heterogeneous Graph Embedding for Music Recommendation
1 Introduction
2 Related Work
3 Preliminaries
4 Proposed Model
4.1 Generating Node Sequences over Heterogenous Music Graph
4.2 Learning User Preferences Through Heterogeneous Embedding
5 Experimental Results
5.1 Experimental Settings
5.2 Recommendation Performance
5.3 Performance on Cold-Start Users
5.4 Impact of Parameter G
6 Conclusion
References
Knowledge Graph Embedding via Entities' Type Mapping Matrix
1 Introduction
2 Related Work
2.1 Unstructured Model (UM)
2.2 Structure Embedding (SE)
2.3 TransE, TransH, and TransR/CTransR
2.4 Other Models
3 Our Method
4 Training
5 Experiment
5.1 Datasets
5.2 Link Prediction
5.3 Triple Classification
6 Conclusion
References
A Sentence Similarity Model Based on Word Embeddings and Dependency Syntax-Tree
Abstract
1 Introduction
2 Related Work
2.1 The Dependency Syntax Tree
2.2 Word Embeddings
3 Sentence-Level Similarity Model
4 The Similarity Model Based on Word Embeddings and Dependency Syntax-Tree
4.1 The Proposed Approach
4.2 Arc-Based Transformation Syntax Tree
4.3 Syntactic Block and Passive Transformation
4.4 The Embeddings of Syntactic Block
4.5 Splicing Syntactic Blocks
5 Experiments and Results Analysis
6 Conclusion
Acknowledgments
References
Transfer Learning
Semi-coupled Transform Learning
Abstract
1 Introduction
2 Literature Review
2.1 Transform Learning
2.2 Coupled Dictionary Learning
3 Proposed Semi-coupled Transform Learning
4 Experimental Results
4.1 Image Super-Resolution
4.2 Cross Lingual Document Retrieval
5 Conclusion
References
A Refined Spatial Transformer Network
1 Introduction
2 Related Work
2.1 Spatial Transformer Networks
2.2 Parametric Warping
3 Refined Spatial Transformer Network
3.1 Proposed Method
3.2 Mathematical Representation of Refined-STN Pipeline
4 Experiments
4.1 Implemental Details
4.2 Cluttered MNIST Classification
4.3 Planar Face Alignment
5 Conclusion
References
Convolutional Transform Learning
1 Introduction
2 Background
2.1 Dictionary Learning
2.2 Transform Learning
3 Proposed Approach
3.1 Convolutional Transform Learning
3.2 Optimization Algorithm
3.3 Update of T
3.4 Update of X
4 Numerical Results
4.1 Classification Accuracy
4.2 Computational Time
4.3 Analysis of the Learned Kernels
5 Conclusion
References
Delving into Diversity in Substitute Ensembles and Transferability of Adversarial Examples
Abstract
1 Introduction
2 Ensemble-Based Black-Box Attack Strategy
2.1 Iterative Cascade Ensemble Strategy
2.2 Stack Parallel Ensemble Strategy
2.3 Strategy Analysis
3 Experiments
3.1 Setup
3.2 Results
4 Conclusion
Acknowledgement
References
Transfer Learning Using Progressive Neural Networks and NMT for Classification Tasks in NLP
1 Introduction
2 Related Work
3 Datasets
4 Direct Transfer Learning
5 Progressive Neural Networks
5.1 Implementation
6 Neural Machine Translator
6.1 PNN with NMT
7 Conclusions
8 Future Work
References
Deep Transfer Learning via Minimum Enclosing Balls
1 Introduction
2 Deep Learners
3 MTL Framework
4 MEB-based Transfer Learning
5 Experimental Results
5.1 With Versus Without TL
5.2 Independent Versus Dependent TL
5.3 Neural Network Structure
6 Conclusion and Future Work
References
Cross-domain Recommendation with Probabilistic Knowledge Transfer
1 Introduction
2 Preliminary and Problem Formation
2.1 Cross-domain Recommendation by Tri-Factorization
2.2 Problem Formulation
3 Cross-domain Recommendation with Probabilistic Knowledge Transfer
3.1 The Method Description
3.2 The Method Learning
4 Experiments
4.1 Datasets and Evaluation Metrics
4.2 Baselines and Experimental Settings
4.3 Results
4.4 Parameter Analysis and Complexity Analysis
5 Conclusion
References
Transfer Learning with Active Queries for Relational Data Modeling Across Multiple Information Networks
1 Introduction
2 Related Work
3 The Proposed Method
3.1 Problem Formulation
3.2 The TAQIL Framework
4 Experiment
4.1 Settings
4.2 Comparison Methods
4.3 Experimental Results
5 Conclusion
References
Semi-supervised Transfer Metric Learning with Relative Constraints
1 Introduction
2 Background and Related Work
3 Proposed Work
3.1 Similarity Between Data Points Using Gaussian Kernel
3.2 Relative Distance Constraints Ceq and Cneq
3.3 Development to a Kernel Space
3.4 The Bregman Projection and It's Log Determinant Divergence in Kernel Learning
4 Experimental Results
4.1 Handwritten Letter Classification
4.2 USPS Digit Classification
4.3 Discussion of Results
5 Conclusion
References
Fuzzy Domain Adaptation Using Unlabeled Target Data
Abstract
1 Introduction
2 Preliminary
2.1 Transfer Learning
2.2 Fuzzy Rule-Based Model
3 Methodology
4 Experiments
4.1 Experiments on Synthetic Datasets
4.2 Experiments on Real-World Datasets
5 Conclusions and Future Work
Acknowledgment
References
Reinforcement Learning
Reinforcement Learning Policy with Proportional-Integral Control
1 Introduction
2 Related Works
3 Approach
3.1 Background
3.2 Architecture
4 Experiment
4.1 RL Environments
4.2 Experimental Setup
4.3 Results
4.4 Ablation Experiments
5 Conclusion
References
Data-Efficient Reinforcement Learning Using Active Exploration Method
1 Introduction
2 The PILCO Framework
3 Active Exploration PILCO
3.1 Entropy-Based Sample Description
3.2 Policy Evaluation
3.3 Policy Improvement
3.4 Dynamic Model Efficiency Experiment
3.5 Parameter Selection Experiment
4 Conclusion
References
Averaged-A3C for Asynchronous Deep Reinforcement Learning
1 Introduction
2 Related Work
3 Background
3.1 Reinforcement Learning
3.2 Asynchronous Advantage Actor-Critic
4 Averaged-A3C
5 Experiment
5.1 Experimental Environment and Setup
5.2 Results of Atari
5.3 Results of MuJoCo
6 Conclusion
References
Deep Reinforcement Learning for Multi-resource Cloud Job Scheduling
Abstract
1 Introduction
2 Background
3 Model
3.1 State Space
3.2 Action Space
3.3 Objectives and Reward Functions
3.4 Network Structure
4 Training Process
5 Experiments and Analysis
5.1 Experimental Parameters
5.2 Result Analysis
6 Conclusions and Prospects
Acknowledgements
References
Accelerating Spatio-Temporal Deep Reinforcement Learning Model for Game Strategy
Abstract
1 Introduction
2 Related Work
3 Game Environment
4 Method
4.1 Depthwise Separable Convolutional Neural Network
4.2 Convolutional Long Short-Term Memory Network
5 Game Performance
6 Conclusion
Acknowledgements
References
ASD: A Framework for Generation of Task Hierarchies for Transfer in Reinforcement Learning
1 Introduction
2 Background and Related Work
3 Component Hierarchies
4 Generating Component Hierarchies
5 Evaluation of ASD in Standard RL Domains
6 Observation and Conclusion
References
Driving Control with Deep and Reinforcement Learning in The Open Racing Car Simulator
1 Introduction
2 Problem Description in TORCS
3 Learn Driving Controller by Modified PILCO
3.1 Gaussian Process Model
3.2 Return Evaluation
3.3 Policy Gradient Search
4 RL Experiment Results
5 Perceive Driving Data from Images by DL
6 Combination of RL and DL in TORCS
7 Conclusion
References
An Adaptive Box-Normalization Stock Index Trading Strategy Based on Reinforcement Learning
1 Introduction
2 Related Work
2.1 Stock Box Theory
2.2 Stock Trading Strategy Based on RL
3 Architecture of ABN Framework
3.1 Predict MRR and MFR with SVR
3.2 Optimize Input Feature with GA
3.3 Construct Trading Strategy Based on RL
4 Experiment
4.1 Experiments with Constant Trigger Threshold of Box
4.2 Experiments with Adaptive Trigger
5 Conclusion
References
Heterogeneous Multi-task Learning of Evaluation Functions for Chess and Shogi
1 Introduction
2 Related Work
2.1 Neural Networks and Board Games
2.2 Uniformity Regularization
2.3 Multi-task Learning
3 Learning Framework
3.1 Data Representation
3.2 Network Architecture
3.3 Learning Objectives
4 Experiments
4.1 Datasets
4.2 Training Configurations
4.3 Evaluation Metrics
4.4 Models
4.5 Results
5 Conclusion
References
Reinforcement Learning Based Dialogue Management Strategy
1 Introduction
2 Related Work
2.1 Background
2.2 Motivation
3 Reinforcement Learning for Dialogue Strategy
3.1 An Introduction to Reinforcement Learning
3.2 Dialogue Strategy
4 Proposed Methodology
4.1 Proposed MDP
4.2 Implementation
5 Results and Discussion
6 Conclusions and Future Work
References
Other Learning Approaches
A Family of Maximum Margin Criterion for Adaptive Learning
1 Introduction
2 Direct Maximum Margin Criterion
3 Adaptive Learning of MMC
3.1 Layered MMC and 2D MMC
3.2 MMC Network
4 Experiments
5 Conclusion
References
Multi-task Manifold Learning Using Hierarchical Modeling for Insufficient Samples
1 Introduction
2 Problem Formulation
3 Related Work
4 Proposed Method
4.1 Kernel-Smoother-Based Manifold Modeling (KSMM)
4.2 Hierarchical KSMM (H-KSMM)
5 Experimental Results
5.1 Artificial Datasets
5.2 Face Image Datasets
6 Discussion and Conclusion
References
Enhanced Metric Learning via Dempster-Shafer Evidence Theory
1 Introduction
2 Preliminaries
3 Metric Learning with Competitive-Cost Function
4 Optimization
5 Experimental Results
5.1 UCI Data Sets
5.2 Face Recognition Data Sets
6 Conclusion
References
InsightGAN: Semi-Supervised Feature Learning with Generative Adversarial Network for Drug Abuse Detection
1 Introduction
2 Related Work
2.1 Drug Abuse Detection
2.2 Semi-Supervised Learning with GAN
3 The Proposed Model
3.1 Face Preprocessing
3.2 Model Architecture
3.3 Adversarial Training
3.4 Test Process
4 Experimental Evaluation
4.1 Data Collection
4.2 Experimental Setup
4.3 Comparison to Alternative Detection Methods
4.4 Comparison to Alternative CNN Models
5 Conclusion
References
Sparse Feature Learning Using Ensemble Model for Highly-Correlated High-Dimensional Data
1 Introduction
2 Related Work
3 The Proposed Method SFL-ESVM
3.1 Clustering Co-Expression Networks
3.2 Review of ESVM-RFE
3.3 The Proposed SFL-ESVM Based on the Co-Expression Feature Network
4 Experiments
4.1 Datasets
4.2 Results and Discussion
5 Conclusion
References
Task and Instance Quadratic Ordering for Active Online Multitask Learning
1 Introduction
2 Related Work
2.1 Instance Ordering
2.2 Task Ordering
3 Task and Instance Ordering
4 Proposed Quadratic Ordering
4.1 QR-Decomposition Task Ordering
4.2 Minimal-Loss Task Ordering
4.3 QR-QR Ordering
4.4 QR-ML Ordering
4.5 ML-QR Ordering
4.6 ML-ML Ordering
5 Experimental Results
5.1 Datasets
5.2 Experimental Procedure
5.3 Results
6 Conclusion
References
Learning from Titles to Recommend Keywords for Academic Papers
Abstract
1 Introduction
2 Weighting Strategy for Hyper-edge and Hyper-vertex
2.1 The Weighting Hyper-graph Model
2.2 Weighting Strategy for Hyper-edge
2.3 Weighting Strategy for Hyper-vertex
3 Random Walk Process on Hyper-graph
4 Experimental Performance and Analysis
4.1 Datasets
4.2 Evaluation Metrics
4.3 Experimental Results and Discussion
4.3.1 Parameter Tuning
4.3.2 Comparison with Other Approaches
5 Conclusions and Future Work
Acknowledgement
References
Zero-Shot Learning with Superclasses
1 Introduction
2 Related Work
3 The Proposed Framework
3.1 Problem Definition for ZSL
3.2 ZSL with Superclasses
3.3 Model Optimization
3.4 Full ZSL Algorithm
4 Experiments
4.1 Datasets and Settings
4.2 Results Under Standard ZSL Setting
4.3 Results Under Generalized ZSL Setting
5 Conclusion
References
Active Learning Methods with Deep Gaussian Processes
1 Introduction
2 DGPs as Learning Models
2.1 Gaussian Processes
2.2 Deep Gaussian Processes
2.3 Deep Gaussian Process Classification
3 Active Learning Methods with DGPs
3.1 Active Learning Strategies for Binary Classification
3.2 Active Learning Strategies for Multi-class Classification
3.3 Constructing Additional Features Using Unlabeled Data by DGP
4 Experiments
4.1 Data Description
4.2 Experimental Settings
4.3 Results for Educational and Non-educational Text Classification
4.4 Results for Handwritten Digit Recognition
5 Conclusion
References
Transductive Learning with String Kernels for Cross-Domain Text Classification
1 Introduction
2 Related Work
2.1 Cross-Domain Classification
2.2 String Kernels
3 Transductive String Kernels
4 Transductive Kernel Classifier
5 Polarity Classification
6 Conclusion
References
Overcoming Catastrophic Forgetting with Self-adaptive Identifiers
1 Introduction
2 The Proposed Method
2.1 Network Pruning
2.2 Task Indicating
3 Experiment
3.1 Experiment Setting
3.2 Experimental Results and Discussion
4 Conclusions
References
Learning, Storing, and Disentangling Correlated Patterns in Neural Networks
1 Introduction
2 The Model
3 Results
3.1 Learning to Memorize Correlated Patterns
3.2 Disentangling Superposed Memory Patterns
3.3 Learning a CANN
4 Conclusion
References
MultNet: An Efficient Network Representation Learning for Large-Scale Social Relation Extraction
1 Introduction
2 Related Works
3 MultNet
3.1 Architecture
3.2 Training
3.3 Complexity Analysis
4 Experiment
4.1 Data Sets
4.2 Baselines
4.3 Results and Analysis
4.4 Parameter Sensitivity
5 Conclusion and Future Work
References
Geometrical Formulation of the Nonnegative Matrix Factorization
1 Introduction
2 NMF and Topic Model
3 Projection onto an Autoparallel Submanifold
4 Geometrical Projection Algorithm
5 Experiments
5.1 Synthetic Data
5.2 Real Data
6 Conclusion
References
Information Geometric Perspective of Modal Linear Regression
1 Introduction
2 Modal Linear Regression
2.1 Formulation
2.2 EM Algorithm for MLR
3 Information Geometry
4 Information Geometry of MEM Algorithm
4.1 Information Geometric Formulation
5 Information Geometry of MLR Algorithm
5.1 Constructing Manifolds
5.2 Information Geometric Formulation
6 Conclusion
References
Learning from Audience Intelligence: Dynamic Labeled LDA Model for Time-Sync Commented Video Tagging
1 Introduction
2 Related Work
3 Problem Definition
3.1 Properties of Bullet-Screen Comments
3.2 Formal Problem Definition
4 DLLDA Model
4.1 The Training Model
4.2 The Testing Model
4.3 The Generation Process
5 Inference Algorithm
6 Experiments
6.1 Dataset
6.2 Experimental Setup
6.3 Parameter Sensitivity
6.4 Experiment Results
6.5 Case Study
7 Conclusions
References
Accurate Q-Learning
1 Introduction
2 Background
3 Accurate Q-Learning
3.1 A New Form of Q-Learning
3.2 Generalization
3.3 Accurate Q-Learning Algorithm
4 Experiments
4.1 Roulette
4.2 Grid World
5 Conclusion
References
Monotonicity Extraction for Monotonic Bayesian Networks Parameter Learning
1 Introduction
2 Preliminaries
2.1 Bayesian Networks and Its Parameter Learning
2.2 Monotonicity in Bayesian Networks
2.3 Monotonicity Metrics
3 The Proposed Method
3.1 Construction of Monotonicity Constraints
3.2 Parameter Learning with Monotonicity Constraints
4 Experiments
4.1 Experiments on UCI Datasets
4.2 Experiments on Standard BNs
5 Conclusions
References
Dynamic Maintenance of Decision Rules for Decision Attribute Values' Changing
1 Introduction
2 Preliminaries
3 Incremental Method for Variation of Decision Attribute Values
3.1 An Incremental Method for Updating Decision Rules on DAVC (IMDAVC)
3.2 An Incremental Method for Updating Decision Rules on DAVR (IMDAVR)
4 Complexity Analysis
4.1 IMDAVC
4.2 IMDAVR
5 Experiments
5.1 Performance of Algorithm1 on DAVC
5.2 Performance of Algorithm2 on DAVR
6 Conclusion
References
Label Distribution Learning Based on Ensemble Neural Networks
1 Introduction
2 Related Work
3 Ensemble Neural Networks Framework
3.1 The Maximum Entropy Model
3.2 Construct Base Learners
4 Experiment
4.1 The Data Set of Label Distribution
4.2 Evaluation Measures
4.3 Experiment Setting
4.4 Experimental Results
5 Conclusion
References
Neural Information Processing in Hierarchical Prototypical Networks
1 Introduction
2 Hierarchical Prototypical Networks
2.1 Prototypical Networks
2.2 Hierarchical Prototypical Networks
2.3 Experimental Results
3 Linking to Attractor Neural Networks
4 Conclusions
References
Regularized Tensor Learning with Adaptive One-Class Support Vector Machines
1 Introduction
2 Related Work
3 Methods
3.1 Tensor Analysis
3.2 Adaptive One-Class Support Vector Machine Based Spatial Distance Algorithm
4 Experimental Results
4.1 Case Study I: Sydney Bridge
4.2 Case Study II: A Reinforced Concrete Jack Arch
5 Conclusions
References
Semi-supervised Multi-label Dimensionality Reduction via Low Rank Representation
1 Introduction
2 Methodology
2.1 Low Rank Representation for High-Order Sample Structure Exploration
2.2 Low Rank Representation for High-Order Label Correlation Exploitation
2.3 The SMLD-LRR Method
3 Experimental Results
3.1 Experimental Setup
3.2 Results on All Datasets
3.3 Parameter Analysis
4 Conclusions and Feature Work
References
Visualization Method of Viewpoints Latent in a Dataset
1 Introduction
2 Problem Formulation
3 Related Works
3.1 Data Integration Methods
3.2 Viewpoint Estimation Methods
4 Algorithm of the Latent Viewpoint Visualization
5 Experimental Results
5.1 Psychological Measures' Aspect Visualization
5.2 Dimension Reduction Techniques' Perspective Visualization
6 Discussion
6.1 Data Integration
6.2 Viewpoint Estimation
7 Conclusion
References
Localized Multiple Sources Self-Organizing Map
1 Introduction
2 State of Art
3 Method
3.1 MSSOM Algorithm
3.2 Discussion
4 Experimentation
4.1 Datasets
4.2 Protocol
4.3 Results
5 Conclusions and Perspectives
References
Heterogeneous Dyadic Multi-task Learning with Implicit Feedback
1 Introduction
2 Feature Extraction Based on Implicit Feedback
3 Learning by Combining the Outputs
4 SGD for Multi-target Heterogeneous Dyadic Learning
4.1 SGD for Multi-output and Heterogeneous Tasks
5 Experiments
6 Conclusions and Future Work
References
Parallel Cooperative Ensemble Learning by Adaptive Data Weighting and Error-Correcting Output Codes
1 Introduction
2 Related Work
2.1 AdaBoost.OC
2.2 Aim of This Work
3 Parallel Cooperative Ensemble Learning (PCEL)
3.1 Parallel Training for Cooperation
3.2 Sample Weight Assignment Function
3.3 Fixing Weak Hypotheses According to Their Accuracy
3.4 PCEL
4 Experimental Results
4.1 The Datasets
4.2 Experimental Conditions
4.3 Results
5 Conclusion
References
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Neural Information Processing

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