no code implementations • 21 Nov 2023 • Shufa Wei, Xiaolong Xu, Xianbiao Qi, Xi Yin, Jun Xia, Jingyi Ren, Peijun Tang, Yuxiang Zhong, Yihao Chen, Xiaoqin Ren, Yuxin Liang, Liankai Huang, Kai Xie, Weikang Gui, Wei Tan, Shuanglong Sun, Yongquan Hu, Qinxian Liu, Nanjin Li, Chihao Dai, Lihua Wang, Xiaohui Liu, Lei Zhang, Yutao Xie
Our training corpus mainly consists of academic papers, thesis, content from some academic domain, high-quality Chinese data and others.
On CoreML, FMViT outperforms MobileOne by 2. 6% in top-1 accuracy on the ImageNet dataset, with inference latency comparable to MobileOne (78. 5% vs. 75. 9%).
Active learning, a widely adopted technique for enhancing machine learning models in text and image classification tasks with limited annotation resources, has received relatively little attention in the domain of Named Entity Recognition (NER).
Named entity recognition (NER), a task that identifies and categorizes named entities such as persons or organizations from text, is traditionally framed as a multi-class classification problem.
Research demonstrates learners engaging in the process of producing explanations to support their reasoning, can have a positive impact on learning.
We note that many prior studies on classifying educational DAs employ cross entropy (CE) loss to optimize DA classifiers on low-resource data with imbalanced DA distribution.
Then, the study investigates how the AL methods can select informative samples to support DA classifiers in the AL sampling process.
To the best of our knowledge, this is the first work that brings AUC maximization to the NER setting.
This paper aims to numerically study the pre-fatigue, low-velocity impact (LVI) and fatigue progressive damage behaviours of a full-scale composite helicopter tail structure under multipoint coordinated loading spectrum.
We convert the ELR framework to estimate the increase in (strictly proper) scores like log probability or negative mean square error, which we call Bayesian Estimate of Mean Proper Scores (BEMPS).
Current MF implementations are either optimized for a single machine or with a need of a large computer cluster but still are insufficient.
The practice in an elderly-care company shows that the FPQM can reduce the number of attributes by 90. 56% with a prediction accuracy of 98. 39%.
To provide a theory-based quantification of the architecture's advantages, we introduce a memory capacity measure, the mean recurrent length, which is more suitable for RNNs with long skip connections than existing measures.
Ranked #24 on Sequential Image Classification on Sequential MNIST
Matrix factorization (MF) is employed by many popular algorithms, e. g., collaborative filtering.
Distributed, Parallel, and Cluster Computing Performance
To the best of our knowledge, the present work is the first attempt that applies sentiment analysis to the domain of TCM on Sina Weibo (a twitter-like microblogging service in China).