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Found 101 papers, 47 papers with code
Adversarial Attack against Cross-lingual Knowledge Graph Alignment
Recent literatures have shown that knowledge graph (KG) learning models are highly vulnerable to adversarial attacks.
Timeline Summarization based on Event Graph Compression via Time-Aware Optimal Transport
Timeline Summarization identifies major events from a news collection and describes them following temporal order, with key dates tagged.
KGTrust: Evaluating Trustworthiness of SIoT via Knowledge Enhanced Graph Neural Networks
Graph neural networks for trust evaluation typically adopt a straightforward way such as one-hot or node2vec to comprehend node characteristics, which ignores the valuable semantic knowledge attached to nodes.
Pruning Before Training May Improve Generalization, Provably
It is shown that as long as the pruning fraction is below a certain threshold, gradient descent can drive the training loss toward zero and the network exhibits good generalization performance.
T2-GNN: Graph Neural Networks for Graphs with Incomplete Features and Structure via Teacher-Student Distillation
A key premise for the remarkable performance of GNNs relies on complete and trustworthy initial graph descriptions (i. e., node features and graph structure), which is often not satisfied since real-world graphs are often incomplete due to various unavoidable factors.
Human-instructed Deep Hierarchical Generative Learning for Automated Urban Planning
The third stage is to leverage multi-attentions to model the zone-zone peer dependencies of the functionality projections to generate grid-level land-use configurations.
HigeNet: A Highly Efficient Modeling for Long Sequence Time Series Prediction in AIOps
Modern IT system operation demands the integration of system software and hardware metrics.
Coarse-to-fine Knowledge Graph Domain Adaptation based on Distantly-supervised Iterative Training
In this framework, we apply distant-supervision on cross-domain knowledge graph adaptation.
Automatic Scene-based Topic Channel Construction System for E-Commerce
To conduct scene marketing for e-commerce platforms, this work presents a novel product form, scene-based topic channel which typically consists of a list of diverse products belonging to the same usage scenario and a topic title that describes the scenario with marketing words.
Knowledge Distillation based Contextual Relevance Matching for E-commerce Product Search
In this paper, we design an efficient knowledge distillation framework for e-commerce relevance matching to integrate the respective advantages of Transformer-style models and classical relevance matching models.
Automatic Generation of Product-Image Sequence in E-commerce
For a platform with billions of products, it is extremely time-costly and labor-expensive to manually pick and organize qualified images.
Input-agnostic Certified Group Fairness via Gaussian Parameter Smoothing
Theoretical analysis is conducted to derive that the Nemytskii operator is smooth and induces a Frechet differentiable smooth manifold.
Automatic Controllable Product Copywriting for E-Commerce
Automatic product description generation for e-commerce has witnessed significant advancement in the past decade.
TeKo: Text-Rich Graph Neural Networks with External Knowledge
Graph Neural Networks (GNNs) have gained great popularity in tackling various analytical tasks on graph-structured data (i. e., networks).
Robust Meta-learning with Sampling Noise and Label Noise via Eigen-Reptile
Moreover, when handling the data with noisy labels, the meta-learner could be extremely sensitive to label noise on a corrupted dataset.
TrustGNN: Graph Neural Network based Trust Evaluation via Learnable Propagative and Composable Nature
In this work, we propose a new GNN based trust evaluation method named TrustGNN, which integrates smartly the propagative and composable nature of trust graphs into a GNN framework for better trust evaluation.
Meta Policy Learning for Cold-Start Conversational Recommendation
We propose to learn a meta policy and adapt it to new users with only a few trials of conversational recommendations.
Scenario-based Multi-product Advertising Copywriting Generation for E-Commerce
In this paper, we proposed an automatic Scenario-based Multi-product Advertising Copywriting Generation system (SMPACG) for E-Commerce, which has been deployed on a leading Chinese e-commerce platform.
Improving Long Tailed Document-Level Relation Extraction via Easy Relation Augmentation and Contrastive Learning
Towards real-world information extraction scenario, research of relation extraction is advancing to document-level relation extraction(DocRE).
Ranked #24 on
Relation Extraction
on DocRED
Heterogeneous Graph Neural Networks using Self-supervised Reciprocally Contrastive Learning
However, the existing contrastive learning methods are inadequate for heterogeneous graphs because they construct contrastive views only based on data perturbation or pre-defined structural properties (e. g., meta-path) in graph data while ignore the noises that may exist in both node attributes and graph topologies.
QRelScore: Better Evaluating Generated Questions with Deeper Understanding of Context-aware Relevance
Existing metrics for assessing question generation not only require costly human reference but also fail to take into account the input context of generation, rendering the lack of deep understanding of the relevance between the generated questions and input contexts.
Knowledge-aware Document Summarization: A Survey of Knowledge, Embedding Methods and Architectures
Knowledge-aware methods have boosted a range of natural language processing applications over the last decades.
Feeding What You Need by Understanding What You Learned
In this paper, we argue that a deep understanding of model capabilities and data properties can help us feed a model with appropriate training data based on its learning status.
Sequential Search with Off-Policy Reinforcement Learning
Recent years have seen a significant amount of interests in Sequential Recommendation (SR), which aims to understand and model the sequential user behaviors and the interactions between users and items over time.
Compact Graph Structure Learning via Mutual Information Compression
Furthermore, we maintain the performance of estimated views and the final view and reduce the mutual information of every two views.
Adversarial Attacks against Windows PE Malware Detection: A Survey of the State-of-the-Art
Then, we conduct a comprehensive and systematic review to categorize the state-of-the-art adversarial attacks against PE malware detection, as well as corresponding defenses to increase the robustness of Windows PE malware detection.
Multiple Choice Questions based Multi-Interest Policy Learning for Conversational Recommendation
As a result, we first propose a more realistic CRS learning setting, namely Multi-Interest Multi-round Conversational Recommendation, where users may have multiple interests in attribute instance combinations and accept multiple items with partially overlapped combinations of attribute instances.
Intelligent Online Selling Point Extraction for E-Commerce Recommendation
In addition, this kind of product description should be eye-catching to the readers.
Automatic Product Copywriting for E-Commerce
It consists of two main components: 1) natural language generation, which is built from a transformer-pointer network and a pre-trained sequence-to-sequence model based on millions of training data from our in-house platform; and 2) copywriting quality control, which is based on both automatic evaluation and human screening.
Validating the Lottery Ticket Hypothesis with Inertial Manifold Theory
Despite achieving remarkable efficiency, traditional network pruning techniques often follow manually-crafted heuristics to generate pruned sparse networks.
Learning to Generate Visual Questions with Noisy Supervision
The task of visual question generation (VQG) aims to generate human-like neural questions from an image and potentially other side information (e. g., answer type or the answer itself).
Graph-augmented Learning to Rank for Querying Large-scale Knowledge Graph
Finally, we apply an answer selection model on the full KSG and the top-ranked sub-KSGs respectively to validate the effectiveness of our proposed graph-augmented learning to rank method.
Triples-to-Text Generation with Reinforcement Learning Based Graph-augmented Neural Networks
Most previous methods solve this task using a sequence-to-sequence model or using a graph-based model to encode RDF triples and to generate a text sequence.
Graph Learning Augmented Heterogeneous Graph Neural Network for Social Recommendation
In this work, we propose an end-to-end heterogeneous global graph learning framework, namely Graph Learning Augmented Heterogeneous Graph Neural Network (GL-HGNN) for social recommendation.
Multi-behavior Graph Contextual Aware Network for Session-based Recommendation
Based on the global graph, MGCNet attaches the global interest representation to final item representation based on local contextual intention to address the limitation (iii).
Bridging the Gap between Spatial and Spectral Domains: A Unified Framework for Graph Neural Networks
Deep learning's performance has been extensively recognized recently.
Heterogeneous Global Graph Neural Networks for Personalized Session-based Recommendation
Additionally, existing personalized session-based recommenders capture user preference only based on the sessions of the current user, but ignore the useful item-transition patterns from other user's historical sessions.
Graph Neural Networks for Natural Language Processing: A Survey
Deep learning has become the dominant approach in coping with various tasks in Natural LanguageProcessing (NLP).
Deep Learning on Graphs for Natural Language Processing
Due to its great power in modeling non-Euclidean data like graphs or manifolds, deep learning on graph techniques (i. e., Graph Neural Networks (GNNs)) have opened a new door to solving challenging graph-related NLP problems.
Constructing Contrastive samples via Summarization for Text Classification with limited annotations
Unlike vision tasks, the data augmentation method for contrastive learning has not been investigated sufficiently in language tasks.
HAConvGNN: Hierarchical Attention Based Convolutional Graph Neural Network for Code Documentation Generation in Jupyter Notebooks
Jupyter notebook allows data scientists to write machine learning code together with its documentation in cells.
Relation-aware Graph Attention Model With Adaptive Self-adversarial Training
Furthermore, the expressivity of the learned representation depends on the quality of negative samples used during training.
Low-skilled Occupations Face the Highest Re-skilling Pressure
Substantial scholarship has estimated the susceptibility of jobs to automation, but little has examined how job contents evolve in the information age as new technologies substitute for tasks, shifting required skills rather than eliminating entire jobs.
A Neural Question Answering System for Basic Questions about Subroutines
In this paper, we take initial steps to bringing state-of-the-art neural QA technologies to Software Engineering applications by designing a context-based QA system for basic questions about subroutines.
Robust Meta-learning with Noise via Eigen-Reptile
In particular, we first cast the meta-overfitting problem (overfitting on sampling and label noise) as a gradient noise problem since few available samples cause meta-learner to overfit on existing examples (clean or corrupted) of an individual task at every gradient step.
Differentiable Graph Optimization for Neural Architecture Search
To this end, we learn a differentiable graph neural network as a surrogate model to rank candidate architectures, which enable us to obtain gradient w. r. t the input architectures.
Ask Question with Double Hints: Visual Question Generation with Answer-awareness and Region-reference
The task of visual question generation~(VQG) aims to generate human-like questions from an image and potentially other side information (e. g. answer type or the answer itself).
Multi-level Graph Matching Networks for Deep and Robust Graph Similarity Learning
The proposed MGMN model consists of a node-graph matching network for effectively learning cross-level interactions between nodes of a graph and the other whole graph, and a siamese graph neural network to learn global-level interactions between two graphs.
Exploiting Heterogeneous Graph Neural Networks with Latent Worker/Task Correlation Information for Label Aggregation in Crowdsourcing
Besides, we exploit the unknown latent interaction between the same type of nodes (workers or tasks) by adding a homogeneous attention layer in the graph neural networks.
Deep Graph Matching and Searching for Semantic Code Retrieval
To this end, we first represent both natural language query texts and programming language code snippets with the unified graph-structured data, and then use the proposed graph matching and searching model to retrieve the best matching code snippet.
Stronger Transformers for Neural Multi-Hop Question Generation
In this work, we introduce a series of strong transformer models for multi-hop question generation, including a graph-augmented transformer that leverages relations between entities in the text.
Technical Question Answering across Tasks and Domains
In this paper, we propose a novel framework of deep transfer learning to effectively address technical QA across tasks and domains.
Unsupervised Reference-Free Summary Quality Evaluation via Contrastive Learning
Experiments on Newsroom and CNN/Daily Mail demonstrate that our new evaluation method outperforms other metrics even without reference summaries.
A Technical Question Answering System with Transfer Learning
In recent years, the need for community technical question-answering sites has increased significantly.
Multilevel Graph Matching Networks for Deep Graph Similarity Learning
In particular, the proposed MGMN consists of a node-graph matching network for effectively learning cross-level interactions between each node of one graph and the other whole graph, and a siamese graph neural network to learn global-level interactions between two input graphs.
A Joint Neural Model for Information Extraction with Global Features
OneIE performs end-to-end IE in four stages: (1) Encoding a given sentence as contextualized word representations; (2) Identifying entity mentions and event triggers as nodes; (3) Computing label scores for all nodes and their pairwise links using local classifiers; (4) Searching for the globally optimal graph with a beam decoder.
Iterative Deep Graph Learning for Graph Neural Networks: Better and Robust Node Embeddings
In this paper, we propose an end-to-end graph learning framework, namely Iterative Deep Graph Learning (IDGL), for jointly and iteratively learning graph structure and graph embedding.
Interpretable Deep Graph Generation with Node-Edge Co-Disentanglement
Disentangled representation learning has recently attracted a significant amount of attention, particularly in the field of image representation learning.
A Multi-Perspective Architecture for Semantic Code Search
The ability to match pieces of code to their corresponding natural language descriptions and vice versa is fundamental for natural language search interfaces to software repositories.
Crossing Variational Autoencoders for Answer Retrieval
Existing methods learned semantic representations with dual encoders or dual variational auto-encoders.
Knowledge Graph-Augmented Abstractive Summarization with Semantic-Driven Cloze Reward
Sequence-to-sequence models for abstractive summarization have been studied extensively, yet the generated summaries commonly suffer from fabricated content, and are often found to be near-extractive.
Toward Subgraph Guided Knowledge Graph Question Generation with Graph Neural Networks
In this work, we focus on a more realistic setting where we aim to generate questions from a KG subgraph and target answers.
Ranked #3 on
KG-to-Text Generation
on WebQuestions
Improved Automatic Summarization of Subroutines via Attention to File Context
In this paper, we present an approach that models the file context of subroutines (i. e. other subroutines in the same file) and uses an attention mechanism to find words and concepts to use in summaries.
Software Engineering
Graph-to-Tree Neural Networks for Learning Structured Input-Output Translation with Applications to Semantic Parsing and Math Word Problem
In particular, we investigated our model for solving two problems, neural semantic parsing and math word problem.
Improved Code Summarization via a Graph Neural Network
The first approaches to use structural information flattened the AST into a sequence.
Bridging the Gap between Spatial and Spectral Domains: A Survey on Graph Neural Networks
Deep learning's success has been widely recognized in a variety of machine learning tasks, including image classification, audio recognition, and natural language processing.
Deep Iterative and Adaptive Learning for Graph Neural Networks
In this paper, we propose an end-to-end graph learning framework, namely Deep Iterative and Adaptive Learning for Graph Neural Networks (DIAL-GNN), for jointly learning the graph structure and graph embeddings simultaneously.
Scalable Global Alignment Graph Kernel Using Random Features: From Node Embedding to Graph Embedding
In particular, RGE is shown to achieve \emph{(quasi-)linear scalability} with respect to the number and the size of the graphs.
KerGM: Kernelized Graph Matching
Graph matching plays a central role in such fields as computer vision, pattern recognition, and bioinformatics.
Efficient Global String Kernel with Random Features: Beyond Counting Substructures
In this paper, we present a new class of global string kernels that aims to (i) discover global properties hidden in the strings through global alignments, (ii) maintain positive-definiteness of the kernel, without introducing a diagonal dominant kernel matrix, and (iii) have a training cost linear with respect to not only the length of the string but also the number of training string samples.
Improving Graph Neural Network Representations of Logical Formulae with Subgraph Pooling
Recent advances in the integration of deep learning with automated theorem proving have centered around the representation of logical formulae as inputs to deep learning systems.
Ranked #1 on
Automated Theorem Proving
on HolStep (Conditional)
Graph Enhanced Cross-Domain Text-to-SQL Generation
Semantic parsing is a fundamental problem in natural language understanding, as it involves the mapping of natural language to structured forms such as executable queries or logic-like knowledge representations.
Natural Question Generation with Reinforcement Learning Based Graph-to-Sequence Model
Natural question generation (QG) aims to generate questions from a passage and an answer.
MUTLA: A Large-Scale Dataset for Multimodal Teaching and Learning Analytics
Automatic analysis of teacher and student interactions could be very important to improve the quality of teaching and student engagement.
Iterative Deep Graph Learning for Graph Neural Networks
In this paper, we propose an end-to-end graph learning framework, namely Iterative Deep Graph Learning (IDGL), for jointly learning graph structure and graph embedding simultaneously.
Hierarchical Graph Matching Networks for Deep Graph Similarity Learning
The proposed HGMN model consists of a multi-perspective node-graph matching network for effectively learning cross-level interactions between parts of a graph and a whole graph, and a siamese graph neural network for learning global-level interactions between two graphs.
Multi-stage Deep Classifier Cascades for Open World Recognition
At present, object recognition studies are mostly conducted in a closed lab setting with classes in test phase typically in training phase.
DynGraph2Seq: Dynamic-Graph-to-Sequence Interpretable Learning for Health Stage Prediction in Online Health Forums
In this paper, we first formulate the transition of user activities as a dynamic graph with multi-attributed nodes, then formalize the health stage inference task as a dynamic graph-to-sequence learning problem, and hence propose a novel dynamic graph-to-sequence neural networks architecture (DynGraph2Seq) to address all the challenges.
Reinforcement Learning Based Graph-to-Sequence Model for Natural Question Generation
Natural question generation (QG) aims to generate questions from a passage and an answer.
GraphFlow: Exploiting Conversation Flow with Graph Neural Networks for Conversational Machine Comprehension
The proposed GraphFlow model can effectively capture conversational flow in a dialog, and shows competitive performance compared to existing state-of-the-art methods on CoQA, QuAC and DoQA benchmarks.
Attacking Graph Convolutional Networks via Rewiring
Graph Neural Networks (GNNs) have boosted the performance of many graph related tasks such as node classification and graph classification.
Reinforcement Learning Based Text Style Transfer without Parallel Training Corpus
Our generator employs an attention-based encoder-decoder to transfer a sentence from the source style to the target style.
Bidirectional Attentive Memory Networks for Question Answering over Knowledge Bases
When answering natural language questions over knowledge bases (KBs), different question components and KB aspects play different roles.
High-Fidelity Vector Space Models of Structured Data
Machine learning systems regularly deal with structured data in real-world applications.
From Node Embedding to Graph Embedding: Scalable Global Graph Kernel via Random Features
Graph kernels are one of the most important methods for graph data analysis and have been successfully applied in diverse applications.
Discrete Adversarial Attacks and Submodular Optimization with Applications to Text Classification
In this paper we formulate the attacks with discrete input on a set function as an optimization task.
The Cinderella Complex: Word Embeddings Reveal ender Stereotypes in Movies and Books
Our analysis of thousands of movies and books reveals how these cultural products weave stereotypical gender roles into morality tales and perpetuate gender inequality through storytelling.
Word Mover's Embedding: From Word2Vec to Document Embedding
While the celebrated Word2Vec technique yields semantically rich representations for individual words, there has been relatively less success in extending to generate unsupervised sentences or documents embeddings.
Random Warping Series: A Random Features Method for Time-Series Embedding
The proposed kernel does not suffer from the issue of diagonal dominance while naturally enjoys a \emph{Random Features} (RF) approximation, which reduces the computational complexity of existing DTW-based techniques from quadratic to linear in terms of both the number and the length of time-series.
Revisiting Random Binning Features: Fast Convergence and Strong Parallelizability
We thus propose the first analysis of RB from the perspective of optimization, which by interpreting RB as a Randomized Block Coordinate Descent in the infinite-dimensional space, gives a faster convergence rate compared to that of other random features.
SQL-to-Text Generation with Graph-to-Sequence Model
Previous work approaches the SQL-to-text generation task using vanilla Seq2Seq models, which may not fully capture the inherent graph-structured information in SQL query.
Exploiting Rich Syntactic Information for Semantic Parsing with Graph-to-Sequence Model
Existing neural semantic parsers mainly utilize a sequence encoder, i. e., a sequential LSTM, to extract word order features while neglecting other valuable syntactic information such as dependency graph or constituent trees.
Quantized Densely Connected U-Nets for Efficient Landmark Localization
Finally, to reduce the memory consumption and high precision operations both in training and testing, we further quantize weights, inputs, and gradients of our localization network to low bit-width numbers.
Ranked #17 on
Pose Estimation
on MPII Human Pose
Fast Incremental von Neumann Graph Entropy Computation: Theory, Algorithm, and Applications
The von Neumann graph entropy (VNGE) facilitates measurement of information divergence and distance between graphs in a graph sequence.
Deep Graph Translation
To achieve this, we propose a novel Graph-Translation-Generative Adversarial Networks (GT-GAN) which will generate a graph translator from input to target graphs.
Scalable Spectral Clustering Using Random Binning Features
Moreover, our method exhibits linear scalability in both the number of data samples and the number of RB features.
Ranked #5 on
Image/Document Clustering
on pendigits
Graph2Seq: Graph to Sequence Learning with Attention-based Neural Networks
Our method first generates the node and graph embeddings using an improved graph-based neural network with a novel aggregation strategy to incorporate edge direction information in the node embeddings.
Ranked #1 on
SQL-to-Text
on WikiSQL
D2KE: From Distance to Kernel and Embedding
For many machine learning problem settings, particularly with structured inputs such as sequences or sets of objects, a distance measure between inputs can be specified more naturally than a feature representation.
Revisiting Spectral Graph Clustering with Generative Community Models
The presented method, SGC-GEN, not only considers the detection error caused by the corresponding model mismatch to a given graph, but also yields a theoretical guarantee on community detectability by analyzing Spectral Graph Clustering (SGC) under GENerative community models (GCMs).
Large Teams Have Developed Science and Technology; Small Teams Have Disrupted It
Teams dominate the production of high-impact science and technology.
Physics and Society Digital Libraries Social and Information Networks
Similarity Preserving Representation Learning for Time Series Clustering
A considerable amount of clustering algorithms take instance-feature matrices as their inputs.
