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Found 10 papers, 5 papers with code
面向人工智能伦理计算的中文道德词典构建方法研究(Construction of a Chinese Moral Dictionary for Artificial Intelligence Ethical Computing)
道德词典资源的建设是人工智能伦理计算的一个研究重点。由于道德行为复杂多样, 现有的英文道德词典分类体系并不完善, 而中文方面目前尚未有相关的词典资源, 理论体系和构建方法仍待探究。针对以上问题, 该文提出了面向人工智能伦理计算的中文道德词典构建任务, 设计了四类标签和四种类型, 得到包含25, 012个词的中文道德词典资源。实验结果表明, 该词典资源不仅能够使机器学会道德知识, 判断词的道德标签和类型, 而且能够为句子级别的道德文本分析提供数据支持。
Transfer learning for improved generalizability in causal physics-informed neural networks for beam simulations
This paper introduces a novel methodology for simulating the dynamics of beams on elastic foundations.
Neural oscillators for generalization of physics-informed machine learning
A primary challenge of physics-informed machine learning (PIML) is its generalization beyond the training domain, especially when dealing with complex physical problems represented by partial differential equations (PDEs).
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.
PCCA-Model: an attention module for medical image segmentation
Based on the idea that the human visual cortex differs in terms of the size of the receptive field and can sense the stimulus location, we propose the pyramid channel coordinate attention (PCCA) module to fuse multiscale features in the channel direction, aggregate local and global channel information, combine them with the location information in the spatial direction, and then integrate them into the existing semantic segmentation network.
Physics-informed machine learning for moving load problems
This paper presents a new approach to simulate forward and inverse problems of moving loads using physics-informed machine learning (PIML).
Physics-informed neural networks for solving forward and inverse problems in complex beam systems
This paper proposes a new framework using physics-informed neural networks (PINNs) to simulate complex structural systems that consist of single and double beams based on Euler-Bernoulli and Timoshenko theory, where the double beams are connected with a Winkler foundation.
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.
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.
MA-Net: A Multi-Scale Attention Network for Liver and Tumor Segmentation
In recent years, a large number of variants of U-Net based on Multi-scale feature fusion are proposed to improve the segmentation performance for medical image segmentation.
