195 citations · 325 across the 10 of their papers we have counts for
10 papers
BiPC: Bidirectional Probability Calibration for Unsupervised Domain Adaption
Wenlve Zhou, Zhiheng Zhou, Junyuan Shang +4
Unsupervised Domain Adaptation (UDA) leverages a labeled source domain to solve tasks in an unlabeled target domain. While Transformer-based methods have shown promise in UDA, thei…
X-PuDu at SemEval-2022 Task 7: A Replaced Token Detection Task Pre-trained Model with Pattern-aware Ensembling for Identifying Plausible Clarifications
Junyuan Shang, Shuohuan Wang, Yu Sun +4
This paper describes our winning system on SemEval 2022 Task 7: Identifying Plausible Clarifications of Implicit and Underspecified Phrases in Instructional Texts. A replaced token…
ERNIE 3.0 Titan: Exploring Larger-scale Knowledge Enhanced Pre-training for Language Understanding and Generation
Shuohuan Wang, Yu Sun, Yang Xiang +26
Pre-trained language models have achieved state-of-the-art results in various Natural Language Processing (NLP) tasks. GPT-3 has shown that scaling up pre-trained language models c…
ERNIE 3.0: Large-scale Knowledge Enhanced Pre-training for Language Understanding and Generation
Yu Sun, Shuohuan Wang, Shikun Feng +19
Pre-trained models have achieved state-of-the-art results in various Natural Language Processing (NLP) tasks. Recent works such as T5 and GPT-3 have shown that scaling up pre-train…
ERNIE-Doc: A Retrospective Long-Document Modeling Transformer
Siyu Ding, Junyuan Shang, Shuohuan Wang +4
Transformers are not suited for processing long documents, due to their quadratically increasing memory and time consumption. Simply truncating a long document or applying the spar…
Opportunities and Challenges of Deep Learning Methods for Electrocardiogram Data: A Systematic Review
Shenda Hong, Yuxi Zhou, Junyuan Shang +2
Background:The electrocardiogram (ECG) is one of the most commonly used diagnostic tools in medicine and healthcare. Deep learning methods have achieved promising results on predic…
GENN: Predicting Correlated Drug-drug Interactions with Graph Energy Neural Networks
Tengfei Ma, Junyuan Shang, Cao Xiao +1
Gaining more comprehensive knowledge about drug-drug interactions (DDIs) is one of the most important tasks in drug development and medical practice. Recently graph neural networks…
K-margin-based Residual-Convolution-Recurrent Neural Network for Atrial Fibrillation Detection
Yuxi Zhou, Shenda Hong, Junyuan Shang +4
Atrial Fibrillation (AF) is an abnormal heart rhythm which can trigger cardiac arrest and sudden death. Nevertheless, its interpretation is mostly done by medical experts due to hi…
Pre-training of Graph Augmented Transformers for Medication Recommendation
Junyuan Shang, Tengfei Ma, Cao Xiao +1
Medication recommendation is an important healthcare application. It is commonly formulated as a temporal prediction task. Hence, most existing works only utilize longitudinal elec…
GAMENet: Graph Augmented MEmory Networks for Recommending Medication Combination
Junyuan Shang, Cao Xiao, Tengfei Ma +2
Recent progress in deep learning is revolutionizing the healthcare domain including providing solutions to medication recommendations, especially recommending medication combinatio…