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Found 9 papers, 3 papers with code
基于SoftLexicon和注意力机制的中文因果关系抽取(Chinese Causality Extraction Based on SoftLexicon and Attention Mechanism)
“针对现有中文因果关系抽取方法对因果事件边界难以识别和文本特征表示不充分的问题, 提出了一种基于外部词汇信息和注意力机制的中文因果关系抽取模型BiLSTM-TWAM+CRF。该模型首次使用SoftLexicon方法引入外部词汇信息构建词集, 解决了因果事件边界难以识别的问题。通过构建的双路关注模块TWAM(Two Way Attention Module), 实现了从局部和全局两个角度充分刻画文本特征。实验结果表明, 与当前中文因果关系抽取模型相比较, 本文方法表现出更优的抽取效果。”
DyTSCL: Dynamic graph representation via tempo-structural contrastive learning
In this paper, we propose a novel Dynamic graph representation framework via Tempo-Structural Contrastive Learning, DyTSCL, which trains the model by identifying three different subgraphs as a task, named Tempo-Structural subgraph, Non-Temporal subgraph and Non-Structural subgraph.
ConCur: Self-supervised graph representation based on contrastive learning with curriculum negative sampling
In the Curriculum Contrastive Training, we first utilize a triplet network to learn node representations by receiving original graph and different augmented views as input.
Scattering-induced entropy boost for highly-compressed optical sensing and encryption
In the proposed framework of single-pixel detection, the optical field from a target is first scattered by an optical diffuser and then two-dimensionally modulated by a spatial light modulator.
Distribution-Aware Graph Representation Learning for Transient Stability Assessment of Power System
As the topology of the power system is in the form of graph structure, graph neural network based representation learning is naturally suitable for learning the status of the power system.
Synthetic Active Distribution System Generation via Unbalanced Graph Generative Adversarial Network
The basic idea is to learn the distribution of random walks both over a real-world system and across each phase of line segments, capturing the underlying local properties of an individual real-world distribution network and generating specific synthetic networks accordingly.
Data-Driven Transient Stability Boundary Generation for Online Security Monitoring
The purpose of this work is to establish a data-driven framework to generate sufficient critical samples close to the boundary within a limited time, covering all critical scenarios in current OP.
NeuNetS: An Automated Synthesis Engine for Neural Network Design
Application of neural networks to a vast variety of practical applications is transforming the way AI is applied in practice.
Social Influence in Social Advertising: Evidence from Field Experiments
This approach can increase ad efficacy for two main reasons: peers' affiliations reflect unobserved consumer characteristics, which are correlated along the social network; and the inclusion of social cues (i. e., peers' association with a brand) alongside ads affect responses via social influence processes.
Social and Information Networks Physics and Society Applications J.4; H.1.2
