De novo peptide sequencing from mass spectrometry data is an important method for protein identification.
Studies of active learning traditionally assume the target and source data stem from a single domain.
We figure out that the background class should be treated differently from the classes of interest during training.
The fifth generation (5G) mobile networks with enhanced connectivity and positioning capabilities play an increasingly important role in the development of automated vehicle-to-everything (V2X) and other advanced industrial Internet of Things (IoT) systems.
In this paper, we dissect the reasoning process of the aforementioned two tasks.
The detection of thoracic abnormalities challenge is organized by the Deepwise AI Lab.
In the context of question answering, we investigate competing hypotheses for the existence of MPPIs, including poor posterior calibration of neural models, lack of pretraining, and "dataset bias" (where a model learns to attend to spurious, non-generalizable cues in the training data).
Progress in cross-lingual modeling depends on challenging, realistic, and diverse evaluation sets.
We propose a hierarchical problem embedding algorithm, called Prob2Vec, that consists of abstraction and embedding steps.
1 code implementation • • Lin Li, Lixin Qin, Zeguo Xu, Youbing Yin, Xin Wang, Bin Kong, Junjie Bai, Yi Lu, Zhenghan Fang, Qi Song, Kunlin Cao, Daliang Liu, Guisheng Wang, Qizhong Xu, Xisheng Fang, Shiqin Zhang, Juan Xia, Jun Xia
Materials and Methods In this retrospective and multi-center study, a deep learning model, COVID-19 detection neural network (COVNet), was developed to extract visual features from volumetric chest CT exams for the detection of COVID-19.
To produce a domain-agnostic question answering model for the Machine Reading Question Answering (MRQA) 2019 Shared Task, we investigate the relative benefits of large pre-trained language models, various data sampling strategies, as well as query and context paraphrases generated by back-translation.
This study describes the model design of the NCUEE system for the MEDIQA challenge at the ACL-BioNLP 2019 workshop.
The proposed method generates well-positioned centerlines, exhibiting lower number of missing branches and is more robust in the presence of minor imperfections of the object segmentation mask.
In order to address these limitations, we present tree-structured ConvLSTM models for tree-structured image analysis tasks which can be trained end-to-end.
The hierarchical attention components of the residual attention subnet force our network to focus on the key components of the X-ray images and generate the final predictions as well as the associated visual supports, which is similar to the assessment procedure of clinicians.
This paper describes the acquisition of a large scale and high quality parallel corpora for English and Chinese.