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Found 67 papers, 35 papers with code
Noise-injected Consistency Training and Entropy-constrained Pseudo Labeling for Semi-supervised Extractive Summarization
Labeling large amounts of extractive summarization data is often prohibitive expensive due to time, financial, and expertise constraints, which poses great challenges to incorporating summarization system in practical applications.
Pseudo-Label Guided Unsupervised Domain Adaptation of Contextual Embeddings
In unsupervised domain adaptation, we aim to train a model that works well on a target domain when provided with labeled source samples and unlabeled target samples.
Variational Multi-Modal Hypergraph Attention Network for Multi-Modal Relation Extraction
Existing methods are limited by their neglect of the multiple entity pairs in one sentence sharing very similar contextual information (ie, the same text and image), resulting in increased difficulty in the MMRE task.
Uncertainty-Aware Relational Graph Neural Network for Few-Shot Knowledge Graph Completion
Uncertainty representation is first designed for estimating the uncertainty scope of the entity pairs after transferring feature representations into a Gaussian distribution.
MIKO: Multimodal Intention Knowledge Distillation from Large Language Models for Social-Media Commonsense Discovery
However, understanding the intention behind social media posts remains challenging due to the implicitness of intentions in social media posts, the need for cross-modality understanding of both text and images, and the presence of noisy information such as hashtags, misspelled words, and complicated abbreviations.
Building Flexible Machine Learning Models for Scientific Computing at Scale
Foundation models have revolutionized knowledge acquisition across domains, and our study introduces OmniArch, a paradigm-shifting approach designed for building foundation models in multi-physics scientific computing.
FedCQA: Answering Complex Queries on Multi-Source Knowledge Graphs via Federated Learning
An entity can be involved in various knowledge graphs and reasoning on multiple KGs and answering complex queries on multi-source KGs is important in discovering knowledge cross graphs.
ASGEA: Exploiting Logic Rules from Align-Subgraphs for Entity Alignment
Entity alignment (EA) aims to identify entities across different knowledge graphs that represent the same real-world objects.
HyCubE: Efficient Knowledge Hypergraph 3D Circular Convolutional Embedding
Existing knowledge hypergraph embedding methods mainly focused on improving model performance, but their model structures are becoming more complex and redundant.
Dynamic Graph Information Bottleneck
Leveraged by the Information Bottleneck (IB) principle, we first propose the expected optimal representations should satisfy the Minimal-Sufficient-Consensual (MSC) Condition.
PhoGAD: Graph-based Anomaly Behavior Detection with Persistent Homology Optimization
The analysis of persistent homology demonstrates its effectiveness in capturing the topological structure formed by normal edge features.
Hide Your Model: A Parameter Transmission-free Federated Recommender System
Existing FedRecs generally adhere to a learning protocol in which a central server shares a global recommendation model with clients, and participants achieve collaborative learning by frequently communicating the model's public parameters.
Environment-Aware Dynamic Graph Learning for Out-of-Distribution Generalization
To the best of our knowledge, we are the first to study OOD generalization on dynamic graphs from the environment learning perspective.
Bipartite Graph Pre-training for Unsupervised Extractive Summarization with Graph Convolutional Auto-Encoders
Pre-trained sentence representations are crucial for identifying significant sentences in unsupervised document extractive summarization.
Unleashing the potential of GNNs via Bi-directional Knowledge Transfer
On this basis, BiKT not only allows us to acquire knowledge from both the GNN and its derived model but promotes each other by injecting the knowledge into the other.
Does Graph Distillation See Like Vision Dataset Counterpart?
We validate the proposed SGDD across 9 datasets and achieve state-of-the-art results on all of them: for example, on the YelpChi dataset, our approach maintains 98. 6% test accuracy of training on the original graph dataset with 1, 000 times saving on the scale of the graph.
Multi-Modal Knowledge Graph Transformer Framework for Multi-Modal Entity Alignment
To address these challenges, we propose a novel MMEA transformer, called MoAlign, that hierarchically introduces neighbor features, multi-modal attributes, and entity types to enhance the alignment task.
Learning Compact Compositional Embeddings via Regularized Pruning for Recommendation
In addition, we innovatively design a regularized pruning mechanism in CERP, such that the two sparsified meta-embedding tables are encouraged to encode information that is mutually complementary.
Dual-Gated Fusion with Prefix-Tuning for Multi-Modal Relation Extraction
Multi-Modal Relation Extraction (MMRE) aims at identifying the relation between two entities in texts that contain visual clues.
Learning Music Sequence Representation from Text Supervision
Music representation learning is notoriously difficult for its complex human-related concepts contained in the sequence of numerical signals.
DisCo: Distilled Student Models Co-training for Semi-supervised Text Mining
Many text mining models are constructed by fine-tuning a large deep pre-trained language model (PLM) in downstream tasks.
Hyperbolic Geometric Graph Representation Learning for Hierarchy-imbalance Node Classification
We find that training labeled nodes with different hierarchical properties have a significant impact on the node classification tasks and confirm it in our experiments.
Attribute-Consistent Knowledge Graph Representation Learning for Multi-Modal Entity Alignment
In this paper, we propose a novel attribute-consistent knowledge graph representation learning framework for MMEA (ACK-MMEA) to compensate the contextual gaps through incorporating consistent alignment knowledge.
SE-GSL: A General and Effective Graph Structure Learning Framework through Structural Entropy Optimization
Graph Neural Networks (GNNs) are de facto solutions to structural data learning.
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.
Unbiased and Efficient Self-Supervised Incremental Contrastive Learning
Contrastive Learning (CL) has been proved to be a powerful self-supervised approach for a wide range of domains, including computer vision and graph representation learning.
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.
Type Information Utilized Event Detection via Multi-Channel GNNs in Electrical Power Systems
To address the above issues, we propose a Multi-Channel graph neural network utilizing Type information for Event Detection in power systems, named MC-TED, leveraging a semantic channel and a topological channel to enrich information interaction from short texts.
Position-aware Structure Learning for Graph Topology-imbalance by Relieving Under-reaching and Over-squashing
Topology-imbalance is a graph-specific imbalance problem caused by the uneven topology positions of labeled nodes, which significantly damages the performance of GNNs.
Task-Specific Expert Pruning for Sparse Mixture-of-Experts
The sparse Mixture-of-Experts (MoE) model is powerful for large-scale pre-training and has achieved promising results due to its model capacity.
THE-X: Privacy-Preserving Transformer Inference with Homomorphic Encryption
As more and more pre-trained language models adopt on-cloud deployment, the privacy issues grow quickly, mainly for the exposure of plain-text user data (e. g., search history, medical record, bank account).
MTTrans: Cross-Domain Object Detection with Mean-Teacher Transformer
To solve this problem, we propose an end-to-end cross-domain detection Transformer based on the mean teacher framework, MTTrans, which can fully exploit unlabeled target domain data in object detection training and transfer knowledge between domains via pseudo labels.
Curvature Graph Generative Adversarial Networks
In this paper, we proposed a novel Curvature Graph Generative Adversarial Networks method, named \textbf{\modelname}, which is the first GAN-based graph representation method in the Riemannian geometric manifold.
Exploring Human Mobility for Multi-Pattern Passenger Prediction: A Graph Learning Framework
To reduce this research gap and learn human mobility knowledge from this fixed travel behaviors, we propose a multi-pattern passenger flow prediction framework, MPGCN, based on Graph Convolutional Network (GCN).
Network resilience in the aging brain
Degeneration and adaptation are two competing sides of the same coin called resilience in the progressive processes of brain aging or diseases.
Bi-CLKT: Bi-Graph Contrastive Learning based Knowledge Tracing
The goal of Knowledge Tracing (KT) is to estimate how well students have mastered a concept based on their historical learning of related exercises.
Temporal Knowledge Graph Completion: A Survey
KGC methods assume a knowledge graph is static, but that may lead to inaccurate prediction results because many facts in the knowledge graphs change over time.
Graph Structure Learning with Variational Information Bottleneck
Graph Neural Networks (GNNs) have shown promising results on a broad spectrum of applications.
POLLA: Enhancing the Local Structure Awareness in Long Sequence Spatial-temporal Modeling
Extensive experiments are conducted on five large-scale datasets, which demonstrate that our method achieves state-of-the-art performance and validates the effectiveness brought by local structure information.
ACE-HGNN: Adaptive Curvature Exploration Hyperbolic Graph Neural Network
Hyperbolic Graph Neural Networks(HGNNs) extend GNNs to hyperbolic space and thus are more effective to capture the hierarchical structures of graphs in node representation learning.
Generating Disentangled Arguments with Prompts: A Simple Event Extraction Framework that Works
Event Extraction bridges the gap between text and event signals.
Sentiment Analysis and Topic Modeling for COVID-19 Vaccine Discussions
The outbreak of the novel Coronavirus Disease 2019 (COVID-19) has lasted for nearly two years and caused unprecedented impacts on people's daily life around the world.
Distributed Optimization of Graph Convolutional Network using Subgraph Variance
We first propose a graph augmentation-based partition (GAD-Partition) that can divide original graph into augmented subgraphs to reduce communication by selecting and storing as few significant nodes of other processors as possible while guaranteeing the accuracy of the training.
Gradient Broadcast Adaptation: Defending against the backdoor attack in pre-trained models
Pre-trained language models (e. g, BERT, GPT-3) have revolutionized the NLP research and fine-tuning becomes the indispensable step of downstream adaptation.
Representation Learning for Short Text Clustering
Effective representation learning is critical for short text clustering due to the sparse, high-dimensional and noise attributes of short text corpus.
Event Extraction by Associating Event Types and Argument Roles
It ignores meaningful associations among event types and argument roles, leading to relatively poor performance for less frequent types/roles.
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.
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.
RoSearch: Search for Robust Student Architectures When Distilling Pre-trained Language Models
In order to overcome these security problems, RoSearch is proposed as a comprehensive framework to search the student models with better adversarial robustness when performing knowledge distillation.
Attend and select: A segment selective transformer for microblog hashtag generation
These tokens or phrases may originate from primary fragmental textual pieces (e. g., segments) in the original text and are separated into different segments.
CNTLS: A Benchmark Dataset for Abstractive or Extractive Chinese Timeline Summarization
We meticulously analyze the corpus using well-known metrics, focusing on the style of the summaries and the complexity of the summarization task.
Noised Consistency Training for Text Summarization
In this paper, we argue that this limitation can be overcome by a semi-supervised approach: consistency training which is to leverage large amounts of unlabeled data to improve the performance of supervised learning over a small corpus.
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.
Differentially Private Federated Knowledge Graphs Embedding
However, for multiple cross-domain knowledge graphs, state-of-the-art embedding models cannot make full use of the data from different knowledge domains while preserving the privacy of exchanged data.
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.
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.
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.
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.
Dynamic Network Embedding Survey
Due to the lack of comprehensive investigation of them, we give a survey of dynamic network embedding in this paper.
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.
SUGAR: Subgraph Neural Network with Reinforcement Pooling and Self-Supervised Mutual Information Mechanism
Graph representation learning has attracted increasing research attention.
Chaotic-to-Fine Clustering for Unlabeled Plant Disease Images
Current annotation for plant disease images depends on manual sorting and handcrafted features by agricultural experts, which is time-consuming and labour-intensive.
Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting
Many real-world applications require the prediction of long sequence time-series, such as electricity consumption planning.
Ranked #1 on
Time Series Forecasting
on ETTh2 (336) Univariate
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.
Adversarial Directed Graph Embedding
To capture the directed edges between nodes, existing methods mostly learn two embedding vectors for each node, source vector and target vector.
RWNE: A Scalable Random-Walk-Based Network Embedding Framework with Personalized Higher-Order Proximity Preserved
However, despite the success of current random-walk-based methods, most of them are usually not expressive enough to preserve the personalized higher-order proximity and lack a straightforward objective to theoretically articulate what and how network proximity is preserved.
A Lightweight Music Texture Transfer System
In this paper, we initiate a novel system for transferring the texture of music, and release it as an open source project.
