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Found 206 papers, 83 papers with code
H2KGAT: Hierarchical Hyperbolic Knowledge Graph Attention Network
Existing knowledge graph embedding approaches concentrate on modeling symmetry/asymmetry, inversion, and composition typed relations but overlook the hierarchical nature of relations.
Gradient Imitation Reinforcement Learning for Low Resource Relation Extraction
Low-resource Relation Extraction (LRE) aims to extract relation facts from limited labeled corpora when human annotation is scarce.
Hyper Meta-Path Contrastive Learning for Multi-Behavior Recommendation
They utilize simple and fixed schemes, like neighborhood information aggregation or mathematical calculation of vectors, to fuse the embeddings of different user behaviors to obtain a unified embedding to represent a user's behavioral patterns which will be used in downstream recommendation tasks.
DSKReG: Differentiable Sampling on Knowledge Graph for Recommendation with Relational GNN
In the information explosion era, recommender systems (RSs) are widely studied and applied to discover user-preferred information.
Contrastive Self-supervised Sequential Recommendation with Robust Augmentation
In this paper, we investigate the application of contrastive Self-Supervised Learning (SSL) to the sequential recommendation, as a way to alleviate some of these issues.
Continuous-Time Sequential Recommendation with Temporal Graph Collaborative Transformer
Therefore, we propose to unify sequential patterns and temporal collaborative signals to improve the quality of recommendation, which is rather challenging.
A Comprehensive Survey on Schema-based Event Extraction with Deep Learning
Numerous methods, datasets, and evaluation metrics have been proposed in the literature, raising the need for a comprehensive and updated survey.
Dual Adversarial Variational Embedding for Robust Recommendation
First, the noise injection based methods often draw the noise from a fixed noise distribution given in advance, while in real world, the noise distributions of different users and items may differ from each other due to personal behaviors and item usage patterns.
Reinforcement Learning-based Dialogue Guided Event Extraction to Exploit Argument Relations
Our approach leverages knowledge of the already extracted arguments of the same sentence to determine the role of arguments that would be difficult to decide individually.
From Canonical Correlation Analysis to Self-supervised Graph Neural Networks
We introduce a conceptually simple yet effective model for self-supervised representation learning with graph data.
Modeling Sequences as Distributions with Uncertainty for Sequential Recommendation
We use Elliptical Gaussian distributions to describe items and sequences with uncertainty.
Sketch-Based Streaming Anomaly Detection in Dynamic Graphs
This higher-order sketch has the useful property of preserving the dense subgraph structure (dense subgraphs in the input turn into dense submatrices in the data structure).
Are Pretrained Transformers Robust in Intent Classification? A Missing Ingredient in Evaluation of Out-of-Scope Intent Detection
Pretrained Transformer-based models were reported to be robust in intent classification.
Attend and Select: A Segment Attention based Selection Mechanism for Microblog Hashtag Generation
The code and datasets are available at \url{https://github. com/OpenSUM/HashtagGen}.
A Comprehensive Survey on Community Detection with Deep Learning
A community reveals the features and connections of its members that are different from those in other communities in a network.
A Robust and Generalized Framework for Adversarial Graph Embedding
With the prevalence of graph data in real-world applications, many methods have been proposed in recent years to learn high-quality graph embedding vectors various types of graphs.
Graph Learning based Recommender Systems: A Review
Recent years have witnessed the fast development of the emerging topic of Graph Learning based Recommender Systems (GLRS).
Error-Robust Multi-View Clustering: Progress, Challenges and Opportunities
Due to inevitable sensor failures, data in each view may contain error.
Graph Entropy Guided Node Embedding Dimension Selection for Graph Neural Networks
Based on them, we design MinGE to directly calculate the ideal node embedding dimension for any graph.
Augmenting Sequential Recommendation with Pseudo-Prior Items via Reversely Pre-training Transformer
We firstly pre-train a transformer with sequences in a reverse direction to predict prior items.
User Preference-aware Fake News Detection
The majority of existing fake news detection algorithms focus on mining news content and/or the surrounding exogenous context for discovering deceptive signals; while the endogenous preference of a user when he/she decides to spread a piece of fake news or not is ignored.
Ranked #1 on
Graph Classification
on UPFD-POL
Higher-Order Attribute-Enhancing Heterogeneous Graph Neural Networks
Furthermore, they cannot fully capture the content-based correlations between nodes, as they either do not use the self-attention mechanism or only use it to consider the immediate neighbors of each node, ignoring the higher-order neighbors.
Reinforced Neighborhood Selection Guided Multi-Relational Graph Neural Networks
A reinforced relation-aware neighbor selection mechanism is developed to choose the most similar neighbors of a targeting node within a relation before aggregating all neighborhood information from different relations to obtain the eventual node embedding.
FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks
FedGraphNN is built on a unified formulation of graph FL and contains a wide range of datasets from different domains, popular GNN models, and FL algorithms, with secure and efficient system support.
HTCInfoMax: A Global Model for Hierarchical Text Classification via Information Maximization
The second one encourages the structure encoder to learn better representations with desired characteristics for all labels which can better handle label imbalance in hierarchical text classification.
Hyperbolic Variational Graph Neural Network for Modeling Dynamic Graphs
To model the uncertainty, we devise a hyperbolic graph variational autoencoder built upon the proposed TGNN to generate stochastic node representations of hyperbolic normal distributions.
Streaming Social Event Detection and Evolution Discovery in Heterogeneous Information Networks
Third, we propose a streaming social event detection and evolution discovery framework for HINs based on meta-path similarity search, historical information about meta-paths, and heterogeneous DBSCAN clustering method.
I-ODA, Real-World Multi-modal Longitudinal Data for OphthalmicApplications
In this paper, we present a new multi-modal longitudinal ophthalmic imaging dataset, the Illinois Ophthalmic Database Atlas (I-ODA), with the goal of advancing state-of-the-art computer vision applications in ophthalmology, and improving upon the translatable capacity of AI based applications across different clinical settings.
An Introduction to Robust Graph Convolutional Networks
In this paper, we propose a novel Robust Graph Convolutional Neural Networks for possible erroneous single-view or multi-view data where data may come from multiple sources.
PredRNN: A Recurrent Neural Network for Spatiotemporal Predictive Learning
This paper models these structures by presenting PredRNN, a new recurrent network, in which a pair of memory cells are explicitly decoupled, operate in nearly independent transition manners, and finally form unified representations of the complex environment.
Ranked #1 on
Video Prediction
on KTH
BCFNet: A Balanced Collaborative Filtering Network with Attention Mechanism
Representation learning tries to learn a common low dimensional space for the representations of users and items.
Understanding WeChat User Preferences and "Wow" Diffusion
"Top Stories" is a novel friend-enhanced recommendation engine in WeChat, in which users can read articles based on preferences of both their own and their friends.
Graph Representation Learning
Social and Information Networks
Graph Self-Supervised Learning: A Survey
Deep learning on graphs has attracted significant interests recently.
Enriching Non-Autoregressive Transformer with Syntactic and SemanticStructures for Neural Machine Translation
In this paper, we claim that the syntactic and semantic structures among natural language are critical for non-autoregressive machine translation and can further improve the performance.
Knowledge-Preserving Incremental Social Event Detection via Heterogeneous GNNs
The complexity and streaming nature of social messages make it appealing to address social event detection in an incremental learning setting, where acquiring, preserving, and extending knowledge are major concerns.
Dynamic Bicycle Dispatching of Dockless Public Bicycle-sharing Systems using Multi-objective Reinforcement Learning
We model the DL-PBS system from the perspective of CPS and use deep learning to predict the layout of bicycle parking spots and the dynamic demand of bicycle dispatching.
Dynamic Planning of Bicycle Stations in Dockless Public Bicycle-sharing System Using Gated Graph Neural Network
The BSDP system contains four modules: bicycle drop-off location clustering, bicycle-station graph modeling, bicycle-station location prediction, and bicycle-station layout recommendation.
Heterogeneous Similarity Graph Neural Network on Electronic Health Records
EHRs contain multiple entities and relations and can be viewed as a heterogeneous graph.
Dynamic Graph Collaborative Filtering
Here we propose Dynamic Graph Collaborative Filtering (DGCF), a novel framework leveraging dynamic graphs to capture collaborative and sequential relations of both items and users at the same time.
A Survey of Community Detection Approaches: From Statistical Modeling to Deep Learning
We conclude with discussions of the challenges of the field and suggestions of possible directions for future research.
Privacy and Robustness in Federated Learning: Attacks and Defenses
Besides training powerful global models, it is of paramount importance to design FL systems that have privacy guarantees and are resistant to different types of adversaries.
A Survey on Heterogeneous Graph Embedding: Methods, Techniques, Applications and Sources
Heterogeneous graphs (HGs) also known as heterogeneous information networks have become ubiquitous in real-world scenarios; therefore, HG embedding, which aims to learn representations in a lower-dimension space while preserving the heterogeneous structures and semantics for downstream tasks (e. g., node/graph classification, node clustering, link prediction), has drawn considerable attentions in recent years.
Deoscillated Graph Collaborative Filtering
Stacking multiple cross-hop propagation layers and locality layers constitutes the DGCF model, which models high-order CF signals adaptively to the locality of nodes and layers.
Hierarchical Bi-Directional Self-Attention Networks for Paper Review Rating Recommendation
Review rating prediction of text reviews is a rapidly growing technology with a wide range of applications in natural language processing.
Understanding Pre-trained BERT for Aspect-based Sentiment Analysis
Most features in the representation of an aspect are dedicated to the fine-grained semantics of the domain (or product category) and the aspect itself, instead of carrying summarized opinions from its context.
Discriminative Nearest Neighbor Few-Shot Intent Detection by Transferring Natural Language Inference
Intent detection is one of the core components of goal-oriented dialog systems, and detecting out-of-scope (OOS) intents is also a practically important skill.
Basket Recommendation with Multi-Intent Translation Graph Neural Network
The problem of basket recommendation~(BR) is to recommend a ranking list of items to the current basket.
Cross-Supervised Joint-Event-Extraction with Heterogeneous Information Networks
To verify the effectiveness of our proposed method, we conduct extensive experiments on four real-world datasets as well as compare our method with state-of-the-art methods.
Dynamic Semantic Matching and Aggregation Network for Few-shot Intent Detection
Although recent works demonstrate that multi-level matching plays an important role in transferring learned knowledge from seen training classes to novel testing classes, they rely on a static similarity measure and overly fine-grained matching components.
Semi-supervised Relation Extraction via Incremental Meta Self-Training
To alleviate human efforts from obtaining large-scale annotations, Semi-Supervised Relation Extraction methods aim to leverage unlabeled data in addition to learning from limited samples.
Mixup-Transformer: Dynamic Data Augmentation for NLP Tasks
Our studies show that mixup is a domain-independent data augmentation technique to pre-trained language models, resulting in significant performance improvement for transformer-based models.
Addressing Class Imbalance in Scene Graph Parsing by Learning to Contrast and Score
Motivated by the fact that detecting these rare relations can be critical in real-world applications, this paper introduces a novel integrated framework of classification and ranking to resolve the class imbalance problem in scene graph parsing.
KG-BART: Knowledge Graph-Augmented BART for Generative Commonsense Reasoning
To promote the ability of commonsense reasoning for text generation, we propose a novel knowledge graph augmented pre-trained language generation model KG-BART, which encompasses the complex relations of concepts through the knowledge graph and produces more logical and natural sentences as output.
Fairness in Semi-supervised Learning: Unlabeled Data Help to Reduce Discrimination
A set of experiments on real-world and synthetic datasets show that our method is able to use unlabeled data to achieve a better trade-off between accuracy and discrimination.
Fairness Constraints in Semi-supervised Learning
Extensive experiments show that our method is able to achieve fair semi-supervised learning, and reach a better trade-off between accuracy and fairness than fair supervised learning.
Pairwise Learning for Name Disambiguation in Large-Scale Heterogeneous Academic Networks
Although these attributes may change, an author's co-authors and research topics do not change frequently with time, which means that papers within a period have similar text and relation information in the academic network.
Multi-view Graph Learning by Joint Modeling of Consistency and Inconsistency
To overcome this limitation, we propose a new multi-view graph learning framework, which for the first time simultaneously and explicitly models multi-view consistency and multi-view inconsistency in a unified objective function, through which the consistent and inconsistent parts of each single-view graph as well as the unified graph that fuses the consistent parts can be iteratively learned.
Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters
Finally, the selected neighbors across different relations are aggregated together.
Differentially Private Multi-Agent Planning for Logistic-like Problems
To the best of our knowledge, this paper is the first to apply differential privacy to the field of multi-agent planning as a means of preserving the privacy of agents for logistic-like problems.
Lifelong Property Price Prediction: A Case Study for the Toronto Real Estate Market
As a departure from prior work, Luce organizes the house data in a heterogeneous information network (HIN) where graph nodes are house entities and attributes that are important for house price valuation.
Interpretable Multi-Step Reasoning with Knowledge Extraction on Complex Healthcare Question Answering
Unlike most other QA tasks that focus on linguistic understanding, HeadQA requires deeper reasoning involving not only knowledge extraction, but also complex reasoning with healthcare knowledge.
More Than Privacy: Applying Differential Privacy in Key Areas of Artificial Intelligence
Artificial Intelligence (AI) has attracted a great deal of attention in recent years.
A Survey on Text Classification: From Shallow to Deep Learning
The last decade has seen a surge of research in this area due to the unprecedented success of deep learning.
Data science and AI in FinTech: An overview
Financial technology (FinTech) has been playing an increasingly critical role in driving modern economies, society, technology, and many other areas.
Network Embedding with Completely-imbalanced Labels
Network embedding, aiming to project a network into a low-dimensional space, is increasingly becoming a focus of network research.
GCN for HIN via Implicit Utilization of Attention and Meta-paths
Graph neural networks for HIN embeddings typically adopt a hierarchical attention (including node-level and meta-path-level attentions) to capture the information from meta-path-based neighbors.
A Survey on Applications of Artificial Intelligence in Fighting Against COVID-19
The COVID-19 pandemic caused by the SARS-CoV-2 virus has spread rapidly worldwide, leading to a global outbreak.
Attentional Graph Convolutional Networks for Knowledge Concept Recommendation in MOOCs in a Heterogeneous View
To address this issue, we leverage both content information and context information to learn the representation of entities via graph convolution network.
Heuristic Semi-Supervised Learning for Graph Generation Inspired by Electoral College
Recently, graph-based algorithms have drawn much attention because of their impressive success in semi-supervised setups.
Robust Spammer Detection by Nash Reinforcement Learning
We experiment on three large review datasets using various state-of-the-art spamming and detection strategies and show that the optimization algorithm can reliably find an equilibrial detector that can robustly and effectively prevent spammers with any mixed spamming strategies from attaining their practical goal.
User Memory Reasoning for Conversational Recommendation
We study a conversational recommendation model which dynamically manages users' past (offline) preferences and current (online) requests through a structured and cumulative user memory knowledge graph, to allow for natural interactions and accurate recommendations.
Joint Training Capsule Network for Cold Start Recommendation
This paper proposes a novel neural network, joint training capsule network (JTCN), for the cold start recommendation task.
Deep Learning for Community Detection: Progress, Challenges and Opportunities
As communities represent similar opinions, similar functions, similar purposes, etc., community detection is an important and extremely useful tool in both scientific inquiry and data analytics.
Commonsense Evidence Generation and Injection in Reading Comprehension
Human tackle reading comprehension not only based on the given context itself but often rely on the commonsense beyond.
Alleviating the Inconsistency Problem of Applying Graph Neural Network to Fraud Detection
In this paper, we introduce these inconsistencies and design a new GNN framework, $\mathsf{GraphConsis}$, to tackle the inconsistency problem: (1) for the context inconsistency, we propose to combine the context embeddings with node features, (2) for the feature inconsistency, we design a consistency score to filter the inconsistent neighbors and generate corresponding sampling probability, and (3) for the relation inconsistency, we learn a relation attention weights associated with the sampled nodes.
DomBERT: Domain-oriented Language Model for Aspect-based Sentiment Analysis
This paper focuses on learning domain-oriented language models driven by end tasks, which aims to combine the worlds of both general-purpose language models (such as ELMo and BERT) and domain-specific language understanding.
Graph Learning Approaches to Recommender Systems: A Review
Recent years have witnessed the fast development of the emerging topic of Graph Learning based Recommender Systems (GLRS).
SelfORE: Self-supervised Relational Feature Learning for Open Relation Extraction
Open relation extraction is the task of extracting open-domain relation facts from natural language sentences.
Differentially Private Deep Learning with Smooth Sensitivity
Ensuring the privacy of sensitive data used to train modern machine learning models is of paramount importance in many areas of practice.
Optimizing Item and Subgroup Configurations for Social-Aware VR Shopping
Shopping in VR malls has been regarded as a paradigm shift for E-commerce, but most of the conventional VR shopping platforms are designed for a single user.
Data Structures and Algorithms
A Survey on Knowledge Graphs: Representation, Acquisition and Applications
In this survey, we provide a comprehensive review of knowledge graph covering overall research topics about 1) knowledge graph representation learning, 2) knowledge acquisition and completion, 3) temporal knowledge graph, and 4) knowledge-aware applications, and summarize recent breakthroughs and perspective directions to facilitate future research.
Hybrid Deep Embedding for Recommendations with Dynamic Aspect-Level Explanations
Particularly, as the aspect preference/quality of users/items is learned automatically, HDE is able to capture the impact of aspects that are not mentioned in reviews of a user or an item.
Deep Collaborative Embedding for information cascade prediction
First, the existing works often assume an underlying information diffusion model, which is impractical in real world due to the complexity of information diffusion.
BasConv: Aggregating Heterogeneous Interactions for Basket Recommendation with Graph Convolutional Neural Network
By defining a basket entity to represent the basket intent, we can model this problem as a basket-item link prediction task in the User-Basket-Item~(UBI) graph.
Leveraging Semi-Supervised Learning for Fairness using Neural Networks
There has been a growing concern about the fairness of decision-making systems based on machine learning.
Deep Graph Similarity Learning: A Survey
In many domains where data are represented as graphs, learning a similarity metric among graphs is considered a key problem, which can further facilitate various learning tasks, such as classification, clustering, and similarity search.
VideoDG: Generalizing Temporal Relations in Videos to Novel Domains
This paper introduces video domain generalization where most video classification networks degenerate due to the lack of exposure to the target domains of divergent distributions.
Med2Meta: Learning Representations of Medical Concepts with Meta-Embeddings
Distributed representations of medical concepts have been used to support downstream clinical tasks recently.
Generative Temporal Link Prediction via Self-tokenized Sequence Modeling
We formalize networks with evolving structures as temporal networks and propose a generative link prediction model, Generative Link Sequence Modeling (GLSM), to predict future links for temporal networks.
A Domain Adaptive Density Clustering Algorithm for Data with Varying Density Distribution
However, clustering algorithms based on density peak have limited clustering effect on data with varying density distribution (VDD), equilibrium distribution (ED), and multiple domain-density maximums (MDDM), leading to the problems of sparse cluster loss and cluster fragmentation.
Community-preserving Graph Convolutions for Structural and Functional Joint Embedding of Brain Networks
Specifically, we use graph convolutions to learn the structural and functional joint embedding, where the graph structure is defined with structural connectivity and node features are from the functional connectivity.
A Failure of Aspect Sentiment Classifiers and an Adaptive Re-weighting Solution
Aspect-based sentiment classification (ASC) is an important task in fine-grained sentiment analysis.~Deep supervised ASC approaches typically model this task as a pair-wise classification task that takes an aspect and a sentence containing the aspect and outputs the polarity of the aspect in that sentence.
JSCN: Joint Spectral Convolutional Network for Cross Domain Recommendation
JSCN will simultaneously operate multi-layer spectral convolutions on different graphs, and jointly learn a domain-invariant user representation with a domain adaptive user mapping module.
Heterogeneous Graph Matching Networks
Information systems have widely been the target of malware attacks.
Hierarchical Semantic Correspondence Learning for Post-Discharge Patient Mortality Prediction
Predicting patient mortality is an important and challenging problem in the healthcare domain, especially for intensive care unit (ICU) patients.
Mixed Pooling Multi-View Attention Autoencoder for Representation Learning in Healthcare
Distributed representations have been used to support downstream tasks in healthcare recently.
Find or Classify? Dual Strategy for Slot-Value Predictions on Multi-Domain Dialog State Tracking
Dialog state tracking (DST) is a core component in task-oriented dialog systems.
Ranked #4 on
Multi-domain Dialogue State Tracking
on MULTIWOZ 2.0
Deep Collaborative Filtering with Multi-Aspect Information in Heterogeneous Networks
However, the characteristics of users and the properties of items may stem from different aspects, e. g., the brand-aspect and category-aspect of items.
Temporal Network Embedding with Micro- and Macro-dynamics
The micro-dynamics describe the formation process of network structures in a detailed manner, while the macro-dynamics refer to the evolution pattern of the network scale.
Generative Question Refinement with Deep Reinforcement Learning in Retrieval-based QA System
To improve the quality and retrieval performance of the generated questions, we make two major improvements: 1) To better encode the semantics of ill-formed questions, we enrich the representation of questions with character embedding and the recent proposed contextual word embedding such as BERT, besides the traditional context-free word embeddings; 2) To make it capable to generate desired questions, we train the model with deep reinforcement learning techniques that considers an appropriate wording of the generation as an immediate reward and the correlation between generated question and answer as time-delayed long-term rewards.
Competitive Multi-Agent Deep Reinforcement Learning with Counterfactual Thinking
During the iterations, our model updates the parallel policies and the corresponding scenario-based regrets for agents simultaneously.
Uncovering Download Fraud Activities in Mobile App Markets
To the best of our knowledge, this is the first work that investigates the download fraud problem in mobile App markets.
Deep Learning for Spatio-Temporal Data Mining: A Survey
Next we classify existing literatures based on the types of ST data, the data mining tasks, and the deep learning models, followed by the applications of deep learning for STDM in different domains including transportation, climate science, human mobility, location based social network, crime analysis, and neuroscience.
Hierarchical Taxonomy-Aware and Attentional Graph Capsule RCNNs for Large-Scale Multi-Label Text Classification
In this paper, we propose a novel hierarchical taxonomy-aware and attentional graph capsule recurrent CNNs framework for large-scale multi-label text classification.
Fine-grained Event Categorization with Heterogeneous Graph Convolutional Networks
In this paper, we design an event meta-schema to characterize the semantic relatedness of social events and build an event-based heterogeneous information network (HIN) integrating information from external knowledge base, and propose a novel Pair-wise Popularity Graph Convolutional Network (PP-GCN) based fine-grained social event categorization model.
Private Deep Learning with Teacher Ensembles
Privacy-preserving deep learning is crucial for deploying deep neural network based solutions, especially when the model works on data that contains sensitive information.
Self-Activation Influence Maximization
We incorporate self activation into influence propagation and propose the self-activation independent cascade (SAIC) model: nodes may be self activated besides being selected as seeds, and influence propagates from both selected seeds and self activated nodes.
Social and Information Networks
Missing Movie Synergistic Completion across Multiple Isomeric Online Movie Knowledge Libraries
Online knowledge libraries refer to the online data warehouses that systematically organize and categorize the knowledge-based information about different kinds of concepts and entities.
Adversarial Defense Framework for Graph Neural Network
How to address the vulnerabilities and defense GNN against the adversarial attacks?
Distributed Deep Learning Model for Intelligent Video Surveillance Systems with Edge Computing
We implement the proposed DIVS system and address the problems of parallel training, model synchronization, and workload balancing.
BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysis
Since ReviewRC has limited training examples for RRC (and also for aspect-based sentiment analysis), we then explore a novel post-training approach on the popular language model BERT to enhance the performance of fine-tuning of BERT for RRC.
Multi-Modal Generative Adversarial Network for Short Product Title Generation in Mobile E-Commerce
To address this discrepancy, previous studies mainly consider textual information of long product titles and lacks of human-like view during training and evaluation process.
Mutual Clustering on Comparative Texts via Heterogeneous Information Networks
HINT first models multi-sourced texts (e. g. news and tweets) as heterogeneous information networks by introducing the shared ``anchor texts'' to connect the comparative texts.
Multi-Hot Compact Network Embedding
Specifically, we propose a MCNE model to learn compact embeddings from pre-learned node features.
Spatiotemporal Pyramid Network for Video Action Recognition
From the technical perspective, we introduce the spatiotemporal compact bilinear operator into video analysis tasks.
Fused Lasso for Feature Selection using Structural Information
To overcome these issues, we propose a new feature selection method using structural correlation between pairwise samples.
WaveletAE: A Wavelet-enhanced Autoencoder for Wind Turbine Blade Icing Detection
Quick detection of blade ice accretion is crucial for the maintenance of wind farms.
Review Conversational Reading Comprehension
Inspired by conversational reading comprehension (CRC), this paper studies a novel task of leveraging reviews as a source to build an agent that can answer multi-turn questions from potential consumers of online businesses.
DeepCF: A Unified Framework of Representation Learning and Matching Function Learning in Recommender System
To solve this problem, many methods have been studied, which can be generally categorized into two types, i. e., representation learning-based CF methods and matching function learning-based CF methods.
A Comprehensive Survey on Graph Neural Networks
In this survey, we provide a comprehensive overview of graph neural networks (GNNs) in data mining and machine learning fields.
Entity Synonym Discovery via Multipiece Bilateral Context Matching
Being able to automatically discover synonymous entities in an open-world setting benefits various tasks such as entity disambiguation or knowledge graph canonicalization.
Adversarial Attack and Defense on Graph Data: A Survey
Therefore, in this paper, we aim to survey existing adversarial learning strategies on graph data and first provide a unified formulation for adversarial learning on graph data which covers most adversarial learning studies on graph.
Joint Slot Filling and Intent Detection via Capsule Neural Networks
Being able to recognize words as slots and detect the intent of an utterance has been a keen issue in natural language understanding.
Ranked #3 on
Slot Filling
on ATIS
Attentional Heterogeneous Graph Neural Network: Application to Program Reidentification
The key idea is to leverage the representation learning of the heterogeneous program behavior graph to guide the reidentification process.
Multi-Task Learning of Generalizable Representations for Video Action Recognition
Rev2Net is shown to be effective on the classic action recognition task.
Memory In Memory: A Predictive Neural Network for Learning Higher-Order Non-Stationarity from Spatiotemporal Dynamics
Natural spatiotemporal processes can be highly non-stationary in many ways, e. g. the low-level non-stationarity such as spatial correlations or temporal dependencies of local pixel values; and the high-level variations such as the accumulation, deformation or dissipation of radar echoes in precipitation forecasting.
Ranked #2 on
Video Prediction
on Human3.6M
Improving Automatic Source Code Summarization via Deep Reinforcement Learning
To the best of our knowledge, most state-of-the-art approaches follow an encoder-decoder framework which encodes the code into a hidden space and then decode it into natural language space, suffering from two major drawbacks: a) Their encoders only consider the sequential content of code, ignoring the tree structure which is also critical for the task of code summarization, b) Their decoders are typically trained to predict the next word by maximizing the likelihood of next ground-truth word with previous ground-truth word given.
Private Model Compression via Knowledge Distillation
To benefit from the on-device deep learning without the capacity and privacy concerns, we design a private model compression framework RONA.
Improved Dynamic Memory Network for Dialogue Act Classification with Adversarial Training
Dialogue Act (DA) classification is a challenging problem in dialogue interpretation, which aims to attach semantic labels to utterances and characterize the speaker's intention.
Ranked #4 on
Dialogue Act Classification
on Switchboard corpus
Generative Dual Adversarial Network for Generalized Zero-shot Learning
Most previous models try to learn a fixed one-directional mapping between visual and semantic space, while some recently proposed generative methods try to generate image features for unseen classes so that the zero-shot learning problem becomes a traditional fully-supervised classification problem.
Graph Convolutional Neural Networks via Motif-based Attention
Different from previous convolutional neural networks on graphs, we first design a motif-matching guided subgraph normalization method to capture neighborhood information.
Product Title Refinement via Multi-Modal Generative Adversarial Learning
Nowadays, an increasing number of customers are in favor of using E-commerce Apps to browse and purchase products.
Semi-supervised Deep Representation Learning for Multi-View Problems
While neural networks for learning representation of multi-view data have been previously proposed as one of the state-of-the-art multi-view dimension reduction techniques, how to make the representation discriminative with only a small amount of labeled data is not well-studied.
Dimensionality Reduction
Learning Representation Of Multi-View Data
Securing Behavior-based Opinion Spam Detection
In terms of security, DETER is versatile enough to be vaccinated against diverse and unexpected evasions, is agnostic about evasion strategy and can be released without privacy concern.
Similarity Learning with Higher-Order Graph Convolutions for Brain Network Analysis
We propose an end-to-end similarity learning framework called Higher-order Siamese GCN for multi-subject fMRI data analysis.
Data-driven Blockbuster Planning on Online Movie Knowledge Library
After a thorough investigation of an online movie knowledge library, a novel movie planning framework "Blockbuster Planning with Maximized Movie Configuration Acquaintance" (BigMovie) is introduced in this paper.
A Self-Organizing Tensor Architecture for Multi-View Clustering
In many real-world applications, data are often unlabeled and comprised of different representations/views which often provide information complementary to each other.
A Bi-layered Parallel Training Architecture for Large-scale Convolutional Neural Networks
In this paper, we focus on the time-consuming training process of large-scale CNNs and propose a Bi-layered Parallel Training (BPT-CNN) architecture in distributed computing environments.
A Periodicity-based Parallel Time Series Prediction Algorithm in Cloud Computing Environments
In this paper, a Periodicity-based Parallel Time Series Prediction (PPTSP) algorithm for large-scale time-series data is proposed and implemented in the Apache Spark cloud computing environment.
Modeling relation paths for knowledge base completion via joint adversarial training
By treating relations and multi-hop paths as two different input sources, we use a feature extractor, which is shared by two downstream components (i. e. relation classifier and source discriminator), to capture shared/similar information between them.
Joint Embedding of Meta-Path and Meta-Graph for Heterogeneous Information Networks
Meta-graph is currently the most powerful tool for similarity search on heterogeneous information networks, where a meta-graph is a composition of meta-paths that captures the complex structural information.
Layerwise Perturbation-Based Adversarial Training for Hard Drive Health Degree Prediction
Then, it is possible to utilize unlabeled data that have a potential of failure to further improve the performance of the model.
Deep Learning Towards Mobile Applications
In this paper, we provide an overview of the current challenges and representative achievements about pushing deep learning on mobile devices from three aspects: training with mobile data, efficient inference on mobile devices, and applications of mobile deep learning.
Not Just Privacy: Improving Performance of Private Deep Learning in Mobile Cloud
To benefit from the cloud data center without the privacy risk, we design, evaluate, and implement a cloud-based framework ARDEN which partitions the DNN across mobile devices and cloud data centers.
Deep Priority Hashing
Deep hashing enables image retrieval by end-to-end learning of deep representations and hash codes from training data with pairwise similarity information.
Enhancing Stock Market Prediction with Extended Coupled Hidden Markov Model over Multi-Sourced Data
In this work, we study how to explore multiple data sources to improve the performance of the stock prediction.
Zero-shot User Intent Detection via Capsule Neural Networks
User intent detection plays a critical role in question-answering and dialog systems.
Spectral Collaborative Filtering
Benefiting from the rich information of connectivity existing in the \textit{spectral domain}, SpectralCF is capable of discovering deep connections between users and items and therefore, alleviates the \textit{cold-start} problem for CF.
dpMood: Exploiting Local and Periodic Typing Dynamics for Personalized Mood Prediction
Mood disorders are common and associated with significant morbidity and mortality.
Human-Computer Interaction Computers and Society
Visual Domain Adaptation with Manifold Embedded Distribution Alignment
Existing methods either attempt to align the cross-domain distributions, or perform manifold subspace learning.
Ranked #1 on
Domain Adaptation
on Office-Caltech-10
Multi-View Multi-Graph Embedding for Brain Network Clustering Analysis
Network analysis of human brain connectivity is critically important for understanding brain function and disease states.
TI-CNN: Convolutional Neural Networks for Fake News Detection
By projecting the explicit and latent features into a unified feature space, TI-CNN is trained with both the text and image information simultaneously.
r-Instance Learning for Missing People Tweets Identification
The number of missing people (i. e., people who get lost) greatly increases in recent years.
Lifelong Domain Word Embedding via Meta-Learning
Learning high-quality domain word embeddings is important for achieving good performance in many NLP tasks.
FAKEDETECTOR: Effective Fake News Detection with Deep Diffusive Neural Network
This paper aims at investigating the principles, methodologies and algorithms for detecting fake news articles, creators and subjects from online social networks and evaluating the corresponding performance.
Double Embeddings and CNN-based Sequence Labeling for Aspect Extraction
Unlike other highly sophisticated supervised deep learning models, this paper proposes a novel and yet simple CNN model employing two types of pre-trained embeddings for aspect extraction: general-purpose embeddings and domain-specific embeddings.
PredRNN++: Towards A Resolution of the Deep-in-Time Dilemma in Spatiotemporal Predictive Learning
We present PredRNN++, an improved recurrent network for video predictive learning.
DeepMood: Modeling Mobile Phone Typing Dynamics for Mood Detection
The increasing use of electronic forms of communication presents new opportunities in the study of mental health, including the ability to investigate the manifestations of psychiatric diseases unobtrusively and in the setting of patients' daily lives.
An Introduction to Image Synthesis with Generative Adversarial Nets
There has been a drastic growth of research in Generative Adversarial Nets (GANs) in the past few years.
Multi-Round Influence Maximization (Extended Version)
In this paper, we study the Multi-Round Influence Maximization (MRIM) problem, where influence propagates in multiple rounds independently from possibly different seed sets, and the goal is to select seeds for each round to maximize the expected number of nodes that are activated in at least one round.
Social and Information Networks
Multi-Task Pharmacovigilance Mining from Social Media Posts
Social media has grown to be a crucial information source for pharmacovigilance studies where an increasing number of people post adverse reactions to medical drugs that are previously unreported.
Lifelong Word Embedding via Meta-Learning
We observe that domains are not isolated and a small domain corpus can leverage the learned knowledge from many past domains to augment that corpus in order to generate high-quality embeddings.
Generative Discovery of Relational Medical Entity Pairs
Online healthcare services can provide the general public with ubiquitous access to medical knowledge and reduce the information access cost for both individuals and societies.
Error-Robust Multi-View Clustering
Various types of errors behave differently and inconsistently in each view.
Stratified Transfer Learning for Cross-domain Activity Recognition
The proposed framework, referred to as Stratified Transfer Learning (STL), can dramatically improve the classification accuracy for cross-domain activity recognition.
Product Function Need Recognition via Semi-supervised Attention Network
Functionality is of utmost importance to customers when they purchase products.
Dual Attention Network for Product Compatibility and Function Satisfiability Analysis
Product compatibility and their functionality are of utmost importance to customers when they purchase products, and to sellers and manufacturers when they sell products.
PredRNN: Recurrent Neural Networks for Predictive Learning using Spatiotemporal LSTMs
The core of this network is a new Spatiotemporal LSTM (ST-LSTM) unit that extracts and memorizes spatial and temporal representations simultaneously.
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Heterogeneous Information Network Embedding for Recommendation
In this paper, we propose a novel heterogeneous network embedding based approach for HIN based recommendation, called HERec.
Social and Information Networks