4 citations · 6 across the 6 of their papers we have counts for
8 papers
Enhancing Clinical Information Extraction with Transferred Contextual Embeddings
Zimin Wan, Chenchen Xu, Hanna Suominen
The Bidirectional Encoder Representations from Transformers (BERT) model has achieved the state-of-the-art performance for many natural language processing (NLP) tasks. Yet, limite…
Analyzing the Granularity and Cost of Annotation in Clinical Sequence Labeling
Haozhan Sun, Chenchen Xu, Hanna Suominen
Well-annotated datasets, as shown in recent top studies, are becoming more important for researchers than ever before in supervised machine learning (ML). However, the dataset anno…
ARVo: Learning All-Range Volumetric Correspondence for Video Deblurring
Dongxu Li, Chenchen Xu, Kaihao Zhang +5
Video deblurring models exploit consecutive frames to remove blurs from camera shakes and object motions. In order to utilize neighboring sharp patches, typical methods rely mainly…
Learning to Continually Learn Rapidly from Few and Noisy Data
Nicholas I-Hsien Kuo, Mehrtash Harandi, Nicolas Fourrier +3
Neural networks suffer from catastrophic forgetting and are unable to sequentially learn new tasks without guaranteed stationarity in data distribution. Continual learning could be…
TSPNet: Hierarchical Feature Learning via Temporal Semantic Pyramid for Sign Language Translation
Dongxu Li, Chenchen Xu, Xin Yu +4
Sign language translation (SLT) aims to interpret sign video sequences into text-based natural language sentences. Sign videos consist of continuous sequences of sign gestures with…
A Token-wise CNN-based Method for Sentence Compression
Weiwei Hou, Hanna Suominen, Piotr Koniusz +2
Sentence compression is a Natural Language Processing (NLP) task aimed at shortening original sentences and preserving their key information. Its applications can benefit many fiel…