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Found 383 papers, 159 papers with code
r-Instance Learning for Missing People Tweets Identification
The number of missing people (i. e., people who get lost) greatly increases in recent years.
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-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.
Learning from Multi-View Multi-Way Data via Structural Factorization Machines
Real-world relations among entities can often be observed and determined by different perspectives/views.
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.
Learning Multiple Tasks with Multilinear Relationship Networks
Deep networks trained on large-scale data can learn transferable features to promote learning multiple tasks.
Bringing Semantic Structures to User Intent Detection in Online Medical Queries
The healthcare status, complex medical information needs of patients are expressed diversely and implicitly in their medical text queries.
HitFraud: A Broad Learning Approach for Collective Fraud Detection in Heterogeneous Information Networks
On electronic game platforms, different payment transactions have different levels of risk.
Multi-view Graph Embedding with Hub Detection for Brain Network Analysis
Specifically, we propose an auto-weighted framework of Multi-view Graph Embedding with Hub Detection (MVGE-HD) for brain network analysis.
SEVEN: Deep Semi-supervised Verification Networks
The model consists of two complementary components.
Supervised Complementary Entity Recognition with Augmented Key-value Pairs of Knowledge
Extracting opinion targets is an important task in sentiment analysis on product reviews and complementary entities (products) are one important type of opinion targets that may work together with the reviewed product.
Multi-view Unsupervised Feature Selection by Cross-diffused Matrix Alignment
In this paper, we study unsupervised feature selection for multi-view data, as class labels are usually expensive to obtain.
Correlation Hashing Network for Efficient Cross-Modal Retrieval
This paper presents a Correlation Hashing Network (CHN) approach to cross-modal hashing, which jointly learns good data representation tailored to hash coding and formally controls the quantization error.
Mining Compatible/Incompatible Entities from Question and Answering via Yes/No Answer Classification using Distant Label Expansion
In this paper, we address the problem of extracting compatible and incompatible products from yes/no questions in PCQA.
CER: Complementary Entity Recognition via Knowledge Expansion on Large Unlabeled Product Reviews
One important product feature is the complementary entity (products) that may potentially work together with the reviewed product.
Online Multi-view Clustering with Incomplete Views
We model the multi-view clustering problem as a joint weighted nonnegative matrix factorization problem and process the multi-view data chunk by chunk to reduce the memory requirement.
Online Unsupervised Multi-view Feature Selection
Third, how to leverage the consistent and complementary information from different views to improve the feature selection in the situation when the data are too big or come in as streams?
Multi-source Hierarchical Prediction Consolidation
We propose a novel multi-source hierarchical prediction consolidation method to effectively exploits the complicated hierarchical label structures to resolve the noisy and conflicting information that inherently originates from multiple imperfect sources.
Clustering on Multiple Incomplete Datasets via Collective Kernel Learning
The idea is to collectively completes the kernel matrices of incomplete datasets by optimizing the alignment of the shared instances of the datasets.
Multi-Source Multi-View Clustering via Discrepancy Penalty
In many real-world applications, information can be gathered from multiple sources, while each source can contain multiple views, which are more cohesive for learning.
Bicycle-Sharing System Analysis and Trip Prediction
In bicycle-sharing systems, people can borrow and return bikes at any stations in the service region very conveniently.
Mining Brain Networks using Multiple Side Views for Neurological Disorder Identification
In this paper, we study the problem of discriminative subgraph selection using multiple side views and propose a novel solution to find an optimal set of subgraph features for graph classification by exploring a plurality of side views.
A review of heterogeneous data mining for brain disorders
Brain disorder data poses many unique challenges for data mining research.
DuSK: A Dual Structure-preserving Kernel for Supervised Tensor Learning with Applications to Neuroimages
The dual-tensorial mapping function can map each tensor instance in the input space to another tensor in the feature space while preserving the tensorial structure.
Large-Scale Multi-Label Learning with Incomplete Label Assignments
Unlike prior works, our method can effectively and efficiently consider missing labels and label correlations simultaneously, and is very scalable, that has linear time complexities over the size of the data.
Multilabel Consensus Classification
Nonetheless, data nowadays are usually multilabeled, such that more than one label have to be predicted at the same time.
Predicting Social Links for New Users across Aligned Heterogeneous Social Networks
We propose a link prediction method called SCAN-PS (Supervised Cross Aligned Networks link prediction with Personalized Sampling), to solve the link prediction problem for new users with information transferred from both the existing active users in the target network and other source networks through aligned accounts.
HeteSim: A General Framework for Relevance Measure in Heterogeneous Networks
Similarity search is an important function in many applications, which usually focuses on measuring the similarity between objects with the same type.
On the Feature Discovery for App Usage Prediction in Smartphones
By analyzing real App usage log data, we discover two kinds of features: The Explicit Feature (EF) from sensing readings of built-in sensors, and the Implicit Feature (IF) from App usage relations.
Meta Path-Based Collective Classification in Heterogeneous Information Networks
Moreover, by considering different linkage paths in the network, one can capture the subtlety of different types of dependencies among objects.
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.
BL-MNE: Emerging Heterogeneous Social Network Embedding through Broad Learning with Aligned Autoencoder
The closeness among users in the networks are defined as the meta proximity scores, which will be fed into DIME to learn the embedding vectors of users in the emerging network.
Social and Information Networks Databases
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.
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 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.
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.
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.
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 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.
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.
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.
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.
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.
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
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 #5 on
Dialogue Act Classification
on Switchboard corpus
Multi-Task Learning of Generalizable Representations for Video Action Recognition
Rev2Net is shown to be effective on the classic action recognition task.
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.
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.
Ranked #6 on
Video Prediction
on Human3.6M
Kernelized Support Tensor Machines
In the context of supervised tensor learning, preserving the structural information and exploiting the discriminative nonlinear relationships of tensor data are crucial for improving the performance of learning tasks.
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.
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.
Transfer Sparse Coding for Robust Image Representation
In this paper, we propose a Transfer Sparse Coding (TSC) approach to construct robust sparse representations for classifying cross-distribution images accurately.
Transfer Joint Matching for Unsupervised Domain Adaptation
Visual domain adaptation, which learns an accurate classifier for a new domain using labeled images from an old domain, has shown promising value in computer vision yet still been a challenging problem.
Multi-Way Multi-Level Kernel Modeling for Neuroimaging Classification
Owing to prominence as a diagnostic tool for probing the neural correlates of cognition, neuroimaging tensor data has been the focus of intense investigation.
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.
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.
Spatiotemporal Pyramid Network for Video Action Recognition
From the technical perspective, we introduce the spatiotemporal compact bilinear operator into video analysis tasks.
Multi-Hot Compact Network Embedding
Specifically, we propose a MCNE model to learn compact embeddings from pre-learned node features.
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-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.
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.
Adversarial Defense Framework for Graph Neural Network
How to address the vulnerabilities and defense GNN against the adversarial attacks?
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.
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.
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.
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
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.
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.
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.
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.
Heterogeneous Graph Matching Networks
Information systems have widely been the target of malware attacks.
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.
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.
Med2Meta: Learning Representations of Medical Concepts with Meta-Embeddings
Distributed representations of medical concepts have been used to support downstream clinical tasks recently.
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.
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 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.
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
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.
Contaminant Removal for Android Malware Detection Systems
The results indicate that once we remove contaminants from the datasets, we can significantly improve both malware detection rate and detection accuracy
Cryptography and Security
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).
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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 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.
Heterogeneous Similarity Graph Neural Network on Electronic Health Records
EHRs contain multiple entities and relations and can be viewed as a heterogeneous graph.
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.
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.
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.
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.
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.
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.
Membership Inference Attacks on Knowledge Graphs
Membership inference attacks (MIAs) infer whether a specific data record is used for target model training.
Error-Robust Multi-View Clustering: Progress, Challenges and Opportunities
Due to inevitable sensor failures, data in each view may contain error.
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.
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.
A Survey on Deep Learning Event Extraction: Approaches and Applications
Numerous methods, datasets, and evaluation metrics have been proposed in the literature, raising the need for a comprehensive and updated survey.
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.
Deep Fraud Detection on Non-attributed Graph
The successes of most previous methods heavily rely on rich node features and high-fidelity labels.
ESCo: Towards Provably Effective and Scalable Contrastive Representation Learning
In this paper, we propose ESCo (Effective and Scalable Contrastive), a new contrastive framework which is essentially an instantiation of the Information Bottleneck principle under self-supervised learning settings.
Self-supervised Learning for Sequential Recommendation with Model Augmentation
However, existing methods all construct views by adopting augmentation from data perspectives, while we argue that 1) optimal data augmentation methods are hard to devise, 2) data augmentation methods destroy sequential correlations, and 3) data augmentation fails to incorporate comprehensive self-supervised signals.
Omni-Training: Bridging Pre-Training and Meta-Training for Few-Shot Learning
While pre-training and meta-training can create deep models powerful for few-shot generalization, we find that pre-training and meta-training focuses respectively on cross-domain transferability and cross-task transferability, which restricts their data efficiency in the entangled settings of domain shift and task shift.
CvS: Classification via Segmentation For Small Datasets
The success of deep learning methods relies heavily on the availability of a large amount of data.
PDALN: Progressive Domain Adaptation over a Pre-trained Model for Low-Resource Cross-Domain Named Entity Recognition
Cross-domain Named Entity Recognition (NER) transfers the NER knowledge from high-resource domains to the low-resource target domain.
Pre-training Graph Neural Network for Cross Domain Recommendation
Then, we transfer the pre-trained graph encoder to initialize the node embeddings on the target domain, which benefits the fine-tuning of the single domain recommender system on the target domain.
Federated Social Recommendation with Graph Neural Network
However, they all require centralized storage of the social links and item interactions of users, which leads to privacy concerns.
Reinforcement Learning based Path Exploration for Sequential Explainable Recommendation
Recent advances in path-based explainable recommendation systems have attracted increasing attention thanks to the rich information provided by knowledge graphs.
Multi-Grained Entity Proposal Network for Named Entity Recognition
In this paper, we focus on a new Named Entity Recognition (NER) task, i. e., the Multi-grained NER task.
SynonymNet: Multi-context Bilateral Matching for Entity Synonyms
Being able to automatically discover synonymous entities from a large free-text corpus has transformative effects on structured knowledge discovery.
Near-Zero-Cost Differentially Private Deep Learning with Teacher Ensembles
Ensuring the privacy of sensitive data used to train modern machine learning models is of paramount importance in many areas of practice.
Spatio-Temporal Joint Graph Convolutional Networks for Traffic Forecasting
However, this approach failed to explicitly reflect the correlations between different nodes at different time steps, thus limiting the learning capability of graph neural networks.
Pre-training Recommender Systems via Reinforced Attentive Multi-relational Graph Neural Network
In this paper, we propose a novel Reinforced Attentive Multi-relational Graph Neural Network (RAM-GNN) to the pre-train user and item embeddings on the user and item graph prior to the recommendation step.
US-Rule: Discovering Utility-driven Sequential Rules
High utility sequential pattern mining (HUSPM) is one kind of utility-driven mining.
A Self-supervised Mixed-curvature Graph Neural Network
Instead of working on one single constant-curvature space, we construct a mixed-curvature space via the Cartesian product of multiple Riemannian component spaces and design hierarchical attention mechanisms for learning and fusing the representations across these component spaces.
An Adaptive Graph Pre-training Framework for Localized Collaborative Filtering
Thus, graph pre-training has the great potential to alleviate data sparsity in GNN-based recommendations.
Interpretable and Effective Reinforcement Learning for Attacking against Graph-based Rumor Detection
To discover subtle vulnerabilities, we design a powerful attacking algorithm to camouflage rumors in social networks based on reinforcement learning that can interact with and attack any black-box detectors.
Multi-Sparse-Domain Collaborative Recommendation via Enhanced Comprehensive Aspect Preference Learning
Meanwhile, we propose a Multi-Domain Adaptation Network (MDAN) for MSDCR to capture a user's domain-invariant aspect preference.
Learning from Atypical Behavior: Temporary Interest Aware Recommendation Based on Reinforcement Learning
To address this challenges, we propose a novel model called Temporary Interest Aware Recommendation (TIARec), which can distinguish atypical interactions from normal ones without supervision and capture the temporary interest as well as the general preference of users.
Dual Space Graph Contrastive Learning
In addition, we conduct extensive experiments to analyze the impact of different graph encoders on DSGC, giving insights about how to better leverage the advantages of contrastive learning between different spaces.
Link Prediction with Contextualized Self-Supervision
To generate informative node embeddings for link prediction, structural context prediction is leveraged as a self-supervised learning task to boost the link prediction performance.
Deep learning for drug repurposing: methods, databases, and applications
Drug development is time-consuming and expensive.
Graph Neural Networks for Graphs with Heterophily: A Survey
In the end, we point out the potential directions to advance and stimulate more future research and applications on heterophilic graph learning with GNNs.
TaSPM: Targeted Sequential Pattern Mining
What's more, to improve the efficiency of TaSPM on large-scale datasets and multiple-items-based sequence datasets, we propose several pruning strategies to reduce meaningless operations in mining processes.
Towards Revenue Maximization with Popular and Profitable Products
To fulfill this gap, in this paper, we first propose a general profit-oriented framework to address the problem of revenue maximization based on economic behavior, and compute the 0n-shelf Popular and most Profitable Products (OPPPs) for the targeted marketing.
G$^3$SR: Global Graph Guided Session-based Recommendation
G$^3$SR decomposes the session-based recommendation workflow into two steps.
Deep reinforcement learning guided graph neural networks for brain network analysis
Modern neuroimaging techniques, such as diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI), enable us to model the human brain as a brain network or connectome.
Multifaceted Improvements for Conversational Open-Domain Question Answering
Recently, conversational OpenQA is proposed to address these issues with the abundant contextual information in the conversation.
XLTime: A Cross-Lingual Knowledge Transfer Framework for Temporal Expression Extraction
To date, work in this area has mostly focused on English as there is a scarcity of labeled data for other languages.
Evidential Temporal-aware Graph-based Social Event Detection via Dempster-Shafer Theory
To incorporate temporal information into the message passing scheme, we introduce a novel temporal-aware aggregator which assigns weights to neighbours according to an adaptive time exponential decay formula.
A Multi-level Supervised Contrastive Learning Framework for Low-Resource Natural Language Inference
Natural Language Inference (NLI) is a growingly essential task in natural language understanding, which requires inferring the relationship between the sentence pairs (premise and hypothesis).
Towards Target Sequential Rules
In this paper, we provide the relevant definitions of target sequential rule and formulate the problem of targeted sequential rule mining.
Collaborative Knowledge Graph Fusion by Exploiting the Open Corpus
To alleviate the challenges of building Knowledge Graphs (KG) from scratch, a more general task is to enrich a KG using triples from an open corpus, where the obtained triples contain noisy entities and relations.
A Generic Algorithm for Top-K On-Shelf Utility Mining
Compared with traditional utility mining, OSUM can find more practical and meaningful patterns in real-life applications.
A Self-supervised Riemannian GNN with Time Varying Curvature for Temporal Graph Learning
To bridge this gap, we make the first attempt to study the problem of self-supervised temporal graph representation learning in the general Riemannian space, supporting the time-varying curvature to shift among hyperspherical, Euclidean and hyperbolic spaces.
Scene Graph Modification as Incremental Structure Expanding
A scene graph is a semantic representation that expresses the objects, attributes, and relationships between objects in a scene.
Contrast Pattern Mining: A Survey
Contrast pattern mining (CPM) is an important and popular subfield of data mining.
Totally-ordered Sequential Rules for Utility Maximization
TotalSR creates a utility table that can efficiently calculate antecedent support and a utility prefix sum list that can compute the remaining utility in O(1) time for a sequence.
Deep Clustering: A Comprehensive Survey
Finally, we discuss the open challenges and potential future opportunities in different fields of deep clustering.
Variational Graph Generator for Multi-View Graph Clustering
The critical point of MGC is to better utilize the view-specific and view-common information in features and graphs of multiple views.
Entity-to-Text based Data Augmentation for various Named Entity Recognition Tasks
Data augmentation techniques have been used to alleviate the problem of scarce labeled data in various NER tasks (flat, nested, and discontinuous NER tasks).
Can Current Explainability Help Provide References in Clinical Notes to Support Humans Annotate Medical Codes?
The medical codes prediction problem from clinical notes has received substantial interest in the NLP community, and several recent studies have shown the state-of-the-art (SOTA) code prediction results of full-fledged deep learning-based methods.
Gradient Imitation Reinforcement Learning for General Low-Resource Information Extraction
Low-Resource Information Extraction (LRIE) strives to use unsupervised data, reducing the required resources and human annotation.
Self-Supervised Continual Graph Learning in Adaptive Riemannian Spaces
On the one hand, existing methods work with the zero-curvature Euclidean space, and largely ignore the fact that curvature varies over the coming graph sequence.
Localized Contrastive Learning on Graphs
Contrastive learning methods based on InfoNCE loss are popular in node representation learning tasks on graph-structured data.
Towards Sequence Utility Maximization under Utility Occupancy Measure
An algorithm called Sequence Utility Maximization with Utility occupancy measure (SUMU) is proposed.
Self-organization Preserved Graph Structure Learning with Principle of Relevant Information
Most Graph Neural Networks follow the message-passing paradigm, assuming the observed structure depicts the ground-truth node relationships.
Time-aware Hyperbolic Graph Attention Network for Session-based Recommendation
TA-HGAT is built in a hyperbolic space to learn the hierarchical structure of session graphs.
Provable Unrestricted Adversarial Training without Compromise with Generalizability
Second, the existing AT methods often achieve adversarial robustness at the expense of standard generalizability (i. e., the accuracy on natural examples) because they make a tradeoff between them.
OrthoReg: Improving Graph-regularized MLPs via Orthogonality Regularization
Graph Neural Networks (GNNs) are currently dominating in modeling graph-structure data, while their high reliance on graph structure for inference significantly impedes them from widespread applications.
Deep Learning and Medical Imaging for COVID-19 Diagnosis: A Comprehensive Survey
In this survey, we investigate the main contributions of deep learning applications using medical images in fighting against COVID-19 from the aspects of image classification, lesion localization, and severity quantification, and review different deep learning architectures and some image preprocessing techniques for achieving a preciser diagnosis.
A Comprehensive Survey on Pretrained Foundation Models: A History from BERT to ChatGPT
This study provides a comprehensive review of recent research advancements, challenges, and opportunities for PFMs in text, image, graph, as well as other data modalities.
Contrastive Graph Clustering in Curvature Spaces
On the other hand, contrastive learning boosts the deep graph clustering but usually struggles in either graph augmentation or hard sample mining.
Zero-shot Item-based Recommendation via Multi-task Product Knowledge Graph Pre-Training
This paper presents a novel paradigm for the Zero-Shot Item-based Recommendation (ZSIR) task, which pre-trains a model on product knowledge graph (PKG) to refine the item features from PLMs.
Gaussian Prior Reinforcement Learning for Nested Named Entity Recognition
Existing works for nested NER ignore the recognition order and boundary position relation of nested entities.
Towards Complex Dynamic Physics System Simulation with Graph Neural ODEs
To better comprehend the complex physical laws, this paper proposes a novel learning based simulation model- Graph Networks with Spatial-Temporal neural Ordinary Equations (GNSTODE)- that characterizes the varying spatial and temporal dependencies in particle systems using a united end-to-end framework.
Multimodal Relation Extraction with Cross-Modal Retrieval and Synthesis
Multimodal relation extraction (MRE) is the task of identifying the semantic relationships between two entities based on the context of the sentence image pair.
Give Me More Details: Improving Fact-Checking with Latent Retrieval
Evidence plays a crucial role in automated fact-checking.
GDA: Generative Data Augmentation Techniques for Relation Extraction Tasks
These techniques neither preserve the semantic consistency of the original sentences when rule-based augmentations are adopted, nor preserve the syntax structure of sentences when expressing relations using seq2seq models, resulting in less diverse augmentations.
Decentralized Federated Learning: A Survey and Perspective
Federated learning (FL) has been gaining attention for its ability to share knowledge while maintaining user data, protecting privacy, increasing learning efficiency, and reducing communication overhead.
Machine Unlearning: A Survey
Machine learning has attracted widespread attention and evolved into an enabling technology for a wide range of highly successful applications, such as intelligent computer vision, speech recognition, medical diagnosis, and more.
Addressing the Rank Degeneration in Sequential Recommendation via Singular Spectrum Smoothing
We also establish a correlation between the ranks of sequence and item embeddings and the rank of the user-item preference prediction matrix, which can affect recommendation diversity.
Privacy and Fairness in Federated Learning: on the Perspective of Trade-off
Federated learning (FL) has been a hot topic in recent years.
Click-Conversion Multi-Task Model with Position Bias Mitigation for Sponsored Search in eCommerce
Position bias, the phenomenon whereby users tend to focus on higher-ranked items of the search result list regardless of the actual relevance to queries, is prevailing in many ranking systems.
Group Identification via Transitional Hypergraph Convolution with Cross-view Self-supervised Learning
With the proliferation of social media, a growing number of users search for and join group activities in their daily life.
All Labels Together: Low-shot Intent Detection with an Efficient Label Semantic Encoding Paradigm
In intent detection tasks, leveraging meaningful semantic information from intent labels can be particularly beneficial for few-shot scenarios.
Localize, Retrieve and Fuse: A Generalized Framework for Free-Form Question Answering over Tables
Furthermore, TAG-QA outperforms the end-to-end model T5 by 16% and 12% on BLEU-4 and PARENT F-score, respectively.
Discovering Utility-driven Interval Rules
For artificial intelligence, high-utility sequential rule mining (HUSRM) is a knowledge discovery method that can reveal the associations between events in the sequences.
Graph Neural Architecture Search with GPT-4
In this paper, we integrate GPT-4 into GNAS and propose a new GPT-4 based Graph Neural Architecture Search method (GPT4GNAS for short).
Do Large Language Models Know about Facts?
Large language models (LLMs) have recently driven striking performance improvements across a range of natural language processing tasks.
AE-smnsMLC: Multi-Label Classification with Semantic Matching and Negative Label Sampling for Product Attribute Value Extraction
In this paper, we reformulate this task as a multi-label classification task that can be applied for real-world scenario in which only annotation of attribute values is available to train models (i. e., annotation of positional information of attribute values is not available).
Collaborative Semantic Alignment in Recommendation Systems
However, PLMs often overlook the vital collaborative filtering signals, leading to challenges in merging collaborative and semantic representation spaces and fine-tuning semantic representations for better alignment with warm-start conditions.
Towards Graph Foundation Models: A Survey and Beyond
Foundation models have emerged as critical components in a variety of artificial intelligence applications, and showcase significant success in natural language processing and several other domains.
Uncertainty-guided Boundary Learning for Imbalanced Social Event Detection
While in our observation, compared to the rarity of classes, the calibrated uncertainty estimated from well-trained evidential deep learning networks better reflects model performance.
DIVKNOWQA: Assessing the Reasoning Ability of LLMs via Open-Domain Question Answering over Knowledge Base and Text
Moreover, a significant gap in the current landscape is the absence of a realistic benchmark for evaluating the effectiveness of grounding LLMs on heterogeneous knowledge sources (e. g., knowledge base and text).
Bayes-enhanced Multi-view Attention Networks for Robust POI Recommendation
To this end, we investigate a novel problem of robust POI recommendation by considering the uncertainty factors of the user check-ins, and proposes a Bayes-enhanced Multi-view Attention Network.
Probing Explicit and Implicit Gender Bias through LLM Conditional Text Generation
Large Language Models (LLMs) can generate biased and toxic responses.
Joint Learning of Local and Global Features for Aspect-based Sentiment Classification
Furthermore, we propose a dual-level graph attention network as a global encoder by fully employing dependency tag information to capture long-distance information effectively.
Aspect-based Meeting Transcript Summarization: A Two-Stage Approach with Weak Supervision on Sentence Classification
Then we merge the sentences selected for a specific aspect as the input for the summarizer to produce the aspect-based summary.
