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pro vyhledávání: '"Yepes, Antonio"'
Chunking information is a key step in Retrieval Augmented Generation (RAG). Current research primarily centers on paragraph-level chunking. This approach treats all texts as equal and neglects the information contained in the structure of documents.
Externí odkaz:
http://arxiv.org/abs/2402.05131
Autor:
Liu, Yuxi, Zhang, Zhenhao, Qin, Shaowen, Salim, Flora D., Yepes, Antonio Jimeno, Shen, Jun, Bian, Jiang
The Intensive Care Unit (ICU) is one of the most important parts of a hospital, which admits critically ill patients and provides continuous monitoring and treatment. Various patient outcome prediction methods have been attempted to assist healthcare
Externí odkaz:
http://arxiv.org/abs/2308.12575
Predicting the risk of in-hospital mortality from electronic health records (EHRs) has received considerable attention. Such predictions will provide early warning of a patient's health condition to healthcare professionals so that timely interventio
Externí odkaz:
http://arxiv.org/abs/2308.09896
Predicting the health risks of patients using Electronic Health Records (EHR) has attracted considerable attention in recent years, especially with the development of deep learning techniques. Health risk refers to the probability of the occurrence o
Externí odkaz:
http://arxiv.org/abs/2211.06045
Building models for health prediction based on Electronic Health Records (EHR) has become an active research area. EHR patient journey data consists of patient time-ordered clinical events/visits from patients. Most existing studies focus on modeling
Externí odkaz:
http://arxiv.org/abs/2207.06414
Autor:
Chen, Qingyu, Allot, Alexis, Leaman, Robert, Doğan, Rezarta Islamaj, Du, Jingcheng, Fang, Li, Wang, Kai, Xu, Shuo, Zhang, Yuefu, Bagherzadeh, Parsa, Bergler, Sabine, Bhatnagar, Aakash, Bhavsar, Nidhir, Chang, Yung-Chun, Lin, Sheng-Jie, Tang, Wentai, Zhang, Hongtong, Tavchioski, Ilija, Pollak, Senja, Tian, Shubo, Zhang, Jinfeng, Otmakhova, Yulia, Yepes, Antonio Jimeno, Dong, Hang, Wu, Honghan, Dufour, Richard, Labrak, Yanis, Chatterjee, Niladri, Tandon, Kushagri, Laleye, Fréjus, Rakotoson, Loïc, Chersoni, Emmanuele, Gu, Jinghang, Friedrich, Annemarie, Pujari, Subhash Chandra, Chizhikova, Mariia, Sivadasan, Naveen, Lu, Zhiyong
The COVID-19 pandemic has been severely impacting global society since December 2019. Massive research has been undertaken to understand the characteristics of the virus and design vaccines and drugs. The related findings have been reported in biomed
Externí odkaz:
http://arxiv.org/abs/2204.09781
Autor:
Yepes, Antonio Jimeno
Deep learning methods minimise the empirical risk using loss functions such as the cross entropy loss. When minimising the empirical risk, the generalisation of the learnt function still depends on the performance on the training data, the Vapnik-Che
Externí odkaz:
http://arxiv.org/abs/2201.05799
Publikováno v:
ICDAR, 2021
Scientific literature contain important information related to cutting-edge innovations in diverse domains. Advances in natural language processing have been driving the fast development in automated information extraction from scientific literature.
Externí odkaz:
http://arxiv.org/abs/2106.14616
Autor:
Šuster, Simon, Verspoor, Karin, Baldwin, Timothy, Lau, Jey Han, Yepes, Antonio Jimeno, Martinez, David, Otmakhova, Yulia
The COVID-19 pandemic has driven ever-greater demand for tools which enable efficient exploration of biomedical literature. Although semi-structured information resulting from concept recognition and detection of the defining elements of clinical tri
Externí odkaz:
http://arxiv.org/abs/2105.12261
We introduce a grey-box adversarial attack and defence framework for sentiment classification. We address the issues of differentiability, label preservation and input reconstruction for adversarial attack and defence in one unified framework. Our re
Externí odkaz:
http://arxiv.org/abs/2103.11576