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Devlbert: Learning deconfounded visio-linguistic representations
In this paper, we propose to investigate the problem of out-of-domain visio-linguistic
pretraining, where the pretraining data distribution differs from that of downstream data on …
pretraining, where the pretraining data distribution differs from that of downstream data on …
Explainable multi-task convolutional neural network framework for electronic petition tag recommendation
Electronic petition (e-petition) is an electronic government (e-government) service that
allows citizens to file petitions to governments via the internet. The complexity of the e …
allows citizens to file petitions to governments via the internet. The complexity of the e …
Interpretable video tag recommendation with multimedia deep learning framework
Purpose Tags help promote customer engagement on video-sharing platforms. Video tag
recommender systems are artificial intelligence-enabled frameworks that strive for …
recommender systems are artificial intelligence-enabled frameworks that strive for …
An Improved Confounding Effect Model for Software Defect Prediction
Y Yuan, C Li, J Yang - Applied Sciences, 2023 - mdpi.com
Software defect prediction technology can effectively improve software quality. Depending
on the code metrics, machine learning models are built to predict potential defects. Some …
on the code metrics, machine learning models are built to predict potential defects. Some …
Deconfounded and explainable interactive vision-language retrieval of complex scenes
In vision-language retrieval systems, users provide natural language feedback to find target
images. Vision-language explanations in the systems can better guide users to provide …
images. Vision-language explanations in the systems can better guide users to provide …
Bias invariant approaches for improving word embedding fairness
Many public pre-trained word embeddings have been shown to encode different types of
biases. Embeddings are often obtained from training on large pre-existing corpora, and …
biases. Embeddings are often obtained from training on large pre-existing corpora, and …
基于词频效应控制的神经机器翻译用词多样性增**方法 (Improving Word-level Diversity in Neural Machine Translation by Controlling the Effects of Word Frequency)
Abstract “通过最大似然估计优化的神经机器翻译(NMT) 容易出现不可最大化的标记或低频词
精度差等问题, 这会导致生成的翻译缺乏词级别的多样性. 词频在训练数据上的不均衡分布是 …
精度差等问题, 这会导致生成的翻译缺乏词级别的多样性. 词频在训练数据上的不均衡分布是 …