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Full-Text Articles in Artificial Intelligence and Robotics
Grammatical Error Correction: A Survey Of The State Of The Art, Christopher Bryant, Zheng Yuan, Muhammad Reza Qorib, Hannan Cao, Hwee Tou Ng, Ted Briscoe
Grammatical Error Correction: A Survey Of The State Of The Art, Christopher Bryant, Zheng Yuan, Muhammad Reza Qorib, Hannan Cao, Hwee Tou Ng, Ted Briscoe
Natural Language Processing Faculty Publications
Grammatical Error Correction (GEC) is the task of automatically detecting and correcting errors in text. The task not only includes the correction of grammatical errors, such as missing prepositions and mismatched subject–verb agreement, but also orthographic and semantic errors, such as misspellings and word choice errors, respectively. The field has seen significant progress in the last decade, motivated in part by a series of five shared tasks, which drove the development of rule-based methods, statistical classifiers, statistical machine translation, and finally neural machine translation systems, which represent the current dominant state of the art. In this survey paper, we condense …
Class-Independent Regularization For Learning With Noisy Labels, Rumeng Yi, Dayan Guan, Yaping Huang, Shijian Lu
Class-Independent Regularization For Learning With Noisy Labels, Rumeng Yi, Dayan Guan, Yaping Huang, Shijian Lu
Computer Vision Faculty Publications
Training deep neural networks (DNNs) with noisy labels often leads to poorly generalized models as DNNs tend to memorize the noisy labels in training. Various strategies have been developed for improving sample selection precision and mitigating the noisy label memorization issue. However, most existing works adopt a class-dependent softmax classifier that is vulnerable to noisy labels by entangling the classification of multi-class features. This paper presents a class-independent regularization (CIR) method that can effectively alleviate the negative impact of noisy labels in DNN training. CIR regularizes the class-dependent softmax classifier by introducing multi-binary classifiers each of which takes care of …
Uncertaintyfusenet: Robust Uncertainty-Aware Hierarchical Feature Fusion Model With Ensemble Monte Carlo Dropout For Covid-19 Detection, Moloud Abdar, Soorena Salari, Sina Qahremani, Hak-Keung Lam, Fakhreddine (Fakhri) Karray, Sadiq Hussain, Abbas Khosravi, U. Rajendra Acharya, Vladimir Makarenkov, Saeid Nahavandi
Uncertaintyfusenet: Robust Uncertainty-Aware Hierarchical Feature Fusion Model With Ensemble Monte Carlo Dropout For Covid-19 Detection, Moloud Abdar, Soorena Salari, Sina Qahremani, Hak-Keung Lam, Fakhreddine (Fakhri) Karray, Sadiq Hussain, Abbas Khosravi, U. Rajendra Acharya, Vladimir Makarenkov, Saeid Nahavandi
Machine Learning Faculty Publications
The COVID-19 (Coronavirus disease 2019) pandemic has become a major global threat to human health and well-being Thus, the development of computer-aided detection (CAD) systems that are capable to accurately distinguish COVID-19 from other diseases using chest computed tomography (CT) and X-ray data is of immediate priority Such automatic systems are usually based on traditional machine learning or deep learning methods Differently from most of existing studies, which used either CT scan or X-ray images in COVID-19-case classification, we present a simple but efficient deep learning feature fusion model, called UncertaintyFuseNet, which is able to classify accurately large datasets of …