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Found 49 papers, 26 papers with code
基于有向异构图的发票明细税收分类方法(Tax Classification of Invoice Details Based on Directed Heterogeneous Graph)
税收是国家赖以生存的物质基础。为加快税收现代化, 方便纳税人便捷、规范开具增值税发票, 国税总局规定纳税人在税控系统开票前选择发票明细对应的税收分类才可正常开具发票。提高税收分类的准确度, 是构建税收风险指标和分析纳税人行为特征的重要基础。基于此, 本文提出了一种基于有向异构图的短文本分类模型(Heterogeneous Directed Graph Attenton Network, HDGAT), 利用发票明细间的有向信息建模, 引入外部知识, 显著地提高了发票明细的税收分类准确度。
Learning to Rematch Mismatched Pairs for Robust Cross-Modal Retrieval
To achieve this, we propose L2RM, a general framework based on Optimal Transport (OT) that learns to rematch mismatched pairs.
Teaching MLP More Graph Information: A Three-stage Multitask Knowledge Distillation Framework
To the best of our knowledge, it is the first work to include hidden layer distillation for student MLP on graphs and to combine graph Positional Encoding with MLP.
Disentangled Representation Learning with Transmitted Information Bottleneck
Encoding only the task-related information from the raw data, \ie, disentangled representation learning, can greatly contribute to the robustness and generalizability of models.
A Diffusion Weighted Graph Framework for New Intent Discovery
New Intent Discovery (NID) aims to recognize both new and known intents from unlabeled data with the aid of limited labeled data containing only known intents.
DNA: Denoised Neighborhood Aggregation for Fine-grained Category Discovery
Specifically, we retrieve k-nearest neighbors of a query as its positive keys to capture semantic similarities between data and then aggregate information from the neighbors to learn compact cluster representations, which can make fine-grained categories more separatable.
Tile Classification Based Viewport Prediction with Multi-modal Fusion Transformer
Viewport prediction is a crucial aspect of tile-based 360 video streaming system.
Clustering based Point Cloud Representation Learning for 3D Analysis
The mined patterns are, in turn, used to repaint the embedding space, so as to respect the underlying distribution of the entire training dataset and improve the robustness to the variations.
LMBot: Distilling Graph Knowledge into Language Model for Graph-less Deployment in Twitter Bot Detection
For datasets without graph structure, we simply replace the GNN with an MLP, which has also shown strong performance.
A Low-rank Matching Attention based Cross-modal Feature Fusion Method for Conversational Emotion Recognition
By setting a matching weight and calculating attention scores between modal features row by row, LMAM contains fewer parameters than the self-attention method.
SFP: Spurious Feature-targeted Pruning for Out-of-Distribution Generalization
Based on the idea that in-distribution (ID) data with spurious features may have a lower experience risk, in this paper, we propose a novel Spurious Feature-targeted model Pruning framework, dubbed SFP, to automatically explore invariant substructures without referring to the above drawbacks.
Detecting Spoilers in Movie Reviews with External Movie Knowledge and User Networks
We then propose MVSD, a novel Multi-View Spoiler Detection framework that takes into account the external knowledge about movies and user activities on movie review platforms.
Noisy Correspondence Learning with Meta Similarity Correction
Despite the success of multimodal learning in cross-modal retrieval task, the remarkable progress relies on the correct correspondence among multimedia data.
Towards Fairer and More Efficient Federated Learning via Multidimensional Personalized Edge Models
Federated learning (FL) is an emerging technique that trains massive and geographically distributed edge data while maintaining privacy.
Disentangled Generation with Information Bottleneck for Few-Shot Learning
To these challenges, we propose a novel Information Bottleneck (IB) based Disentangled Generation Framework for FSL, termed as DisGenIB, that can simultaneously guarantee the discrimination and diversity of generated samples.
Generalized Category Discovery with Decoupled Prototypical Network
Furthermore, the coupled training approach prevents these models transferring category-specific knowledge explicitly from labeled data to unlabeled data, which can lose high-level semantic information and impair model performance.
Power Efficient Video Super-Resolution on Mobile NPUs with Deep Learning, Mobile AI & AIM 2022 challenge: Report
While numerous solutions have been proposed for this problem, they are usually quite computationally demanding, demonstrating low FPS rates and power efficiency on mobile devices.
PAR: Political Actor Representation Learning with Social Context and Expert Knowledge
Extensive experiments demonstrate that PAR is better at augmenting political text understanding and successfully advances the state-of-the-art in political perspective detection and roll call vote prediction.
Fine-grained Category Discovery under Coarse-grained supervision with Hierarchical Weighted Self-contrastive Learning
Novel category discovery aims at adapting models trained on known categories to novel categories.
GraTO: Graph Neural Network Framework Tackling Over-smoothing with Neural Architecture Search
Further experiments bear out the quality of node representations learned with GraTO and the effectiveness of model architecture.
BIC: Twitter Bot Detection with Text-Graph Interaction and Semantic Consistency
In addition, given the stealing behavior of novel Twitter bots, BIC proposes to model semantic consistency in tweets based on attention weights while using it to augment the decision process.
AHEAD: A Triple Attention Based Heterogeneous Graph Anomaly Detection Approach
In light of this problem, we propose AHEAD: a heterogeneity-aware unsupervised graph anomaly detection approach based on the encoder-decoder framework.
KRACL: Contrastive Learning with Graph Context Modeling for Sparse Knowledge Graph Completion
Knowledge Graph Embeddings (KGE) aim to map entities and relations to low dimensional spaces and have become the \textit{de-facto} standard for knowledge graph completion.
TwiBot-22: Towards Graph-Based Twitter Bot Detection
Twitter bot detection has become an increasingly important task to combat misinformation, facilitate social media moderation, and preserve the integrity of the online discourse.
Towards Explanation for Unsupervised Graph-Level Representation Learning
Due to the superior performance of Graph Neural Networks (GNNs) in various domains, there is an increasing interest in the GNN explanation problem "\emph{which fraction of the input graph is the most crucial to decide the model's decision?}"
Noise-Tolerant Learning for Audio-Visual Action Recognition
To address this challenge, we use the audio-visual action recognition task as a proxy and propose a noise-tolerant learning framework to find anti-interference model parameters against both noisy labels and noisy correspondence.
CGUA: Context-Guided and Unpaired-Assisted Weakly Supervised Person Search
On the other hand, there are numerous unpaired persons in real-world scene images.
Toward Enhanced Robustness in Unsupervised Graph Representation Learning: A Graph Information Bottleneck Perspective
Our RGIB attempts to learn robust node representations against adversarial perturbations by preserving the original information in the benign graph while eliminating the adversarial information in the adversarial graph.
PPSGCN: A Privacy-Preserving Subgraph Sampling Based Distributed GCN Training Method
In light of these problems, we propose a Privacy-Preserving Subgraph sampling based distributed GCN training method (PPSGCN), which preserves data privacy and significantly cuts back on communication and memory overhead.
Semantics-Guided Contrastive Network for Zero-Shot Object detection
To address these issues, we develop a novel Semantics-Guided Contrastive Network for ZSD, named ContrastZSD, a detection framework that first brings contrastive learning mechanism into the realm of zero-shot detection.
Ranked #4 on
Zero-Shot Object Detection
on MS-COCO
Contrastive Learning
Generalized Zero-Shot Object Detection
+3
Legislator Representation Learning with Social Context and Expert Knowledge
Modeling the ideological perspectives of political actors is an essential task in computational political science with applications in many downstream tasks.
KGAP: Knowledge Graph Augmented Political Perspective Detection in News Media
Specifically, we construct a political knowledge graph to serve as domain-specific external knowledge.
Annotation of Chinese Predicate Heads and Relevant Elements
This annotation guideline emphasizes the role of the predicate as the structural center of a sentence.
Towards Entity Alignment in the Open World: An Unsupervised Approach
These preliminary results are regarded as the pseudo-labeled data and forwarded to the progressive learning framework to generate structural representations, which are integrated with the side information to provide a more comprehensive view for alignment.
A Boundary Regression Model for Nested Named Entity Recognition
Then, a regression operation is introduced to regress boundaries of NEs in a sentence.
XTQA: Span-Level Explanations of the Textbook Question Answering
Textbook Question Answering (TQA) is a task that one should answer a diagram/non-diagram question given a large multi-modal context consisting of abundant essays and diagrams.
Self-Weighted Robust LDA for Multiclass Classification with Edge Classes
In this regard, a novel self-weighted robust LDA with l21-norm based pairwise between-class distance criterion, called SWRLDA, is proposed for multi-class classification especially with edge classes.
DWMD: Dimensional Weighted Orderwise Moment Discrepancy for Domain-specific Hidden Representation Matching
Our metric function takes advantage of a series for high-order moment alignment, and we theoretically prove that our DWMD metric function is error-free, which means that it can strictly reflect the distribution differences between domains and is valid without any feature distribution assumption.
Scalable Attack on Graph Data by Injecting Vicious Nodes
Recent studies have shown that graph convolution networks (GCNs) are vulnerable to carefully designed attacks, which aim to cause misclassification of a specific node on the graph with unnoticeable perturbations.
Self-Supervised Graph Representation Learning via Global Context Prediction
To take full advantage of fast-growing unlabeled networked data, this paper introduces a novel self-supervised strategy for graph representation learning by exploiting natural supervision provided by the data itself.
Graph Representation Learning via Graphical Mutual Information Maximization
The richness in the content of various information networks such as social networks and communication networks provides the unprecedented potential for learning high-quality expressive representations without external supervision.
Enjoy the Untrusted Cloud: A Secure, Scalable and Efficient SQL-like Query Framework for Outsourcing Data
On one hand, such work lacks of supporting scalable access control over multiple data users.
Cryptography and Security Databases Distributed, Parallel, and Cluster Computing
Knowledge forest: a novel model to organize knowledge fragments
Facet trees can organize knowledge fragments with facet hyponymy to alleviate information overload.
Modeling e-Learners' Cognitive and Metacognitive Strategy in Comparative Question Solving
from "The C Programming Language" course and "What are similarities and differences between packet switching and circuit switching?"
Simple to Complex Cross-modal Learning to Rank
The heterogeneity-gap between different modalities brings a significant challenge to multimedia information retrieval.
Attributing Hacks
That is, given properties of sites and the temporal occurrence of attacks, we are able to attribute individual attacks to joint causes and vulnerabilities, as well as estimating the evolution of these vulnerabilities over time.
