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Found 6 papers, 1 papers with code
标签先验知识增强的方面类别情感分析方法研究(Aspect-Category based Sentiment Analysis Enhanced by Label Prior Knowledge)
“当前, 基于方面类别的情感分析研究旨在将方面类别检测和面向类别的情感分类两个任务协同进行。然而, 现有研究未能有效关注情感数据集中存在的噪声标签, 影响了情感分析的质量。基于此, 本文提出一种标签先验知识增强的方面类别情感分析方法(AP-LPK)。首先本文为面向类别的情感分类构建了自回归提示训练方式, 可以激发预训练语言模型的潜力。同时该方式通过自回归生成标签词, 以期获得比非自回归更好的语义一致性。其次, 每个类别的标签分布作为标签先验知识引入, 并通过伯努利分布对其进行进一步精炼, 以用于减轻噪声标签的干扰。然后, AP-LPK将上述两个步骤分别得到的情感类别分布进行融合, 以获得最终的情感类别预测概率。最后, 本文提出的AP-LPK方法在五个数据集上进行评估, 包括SemEval 2015和2016的四个基准数据集和AI Challenger 2018的餐厅领域大规模数据集。实验结果表明, 本文提出的方法在F1指标上优于现有方法。”
MTGA: Multi-view Temporal Granularity aligned Aggregation for Event-based Lip-reading
Then we design a spatio-temporal fusion module based on temporal granularity alignment, where the global spatial features extracted from event frames, together with the local relative spatial and temporal features contained in voxel graph list are effectively aligned and integrated.
A Protein Structure Prediction Approach Leveraging Transformer and CNN Integration
Proteins are essential for life, and their structure determines their function.
Protein Language Model
Protein Secondary Structure Prediction
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Construction and application of artificial intelligence crowdsourcing map based on multi-track GPS data
In recent years, the rapid development of high-precision map technology combined with artificial intelligence has ushered in a new development opportunity in the field of intelligent vehicles.
Sparse Double Descent: Where Network Pruning Aggravates Overfitting
People usually believe that network pruning not only reduces the computational cost of deep networks, but also prevents overfitting by decreasing model capacity.
Can network pruning benefit deep learning under label noise?
Network pruning is a widely-used technique to reduce the computational cost of over-parameterized neural networks.
