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Foundation models (FMs) have shown prominent success in a wide range of tasks.
Overview of biocreative ii gene mention recognition
Larry Smith, Lorraine K Tanabe, Cheng-Ju Kuo, I Chung, Chun-Nan Hsu, Yu-Shi Lin, Roman Klinger, Christoph M Friedrich, Kuzman Ganchev, Manabu Torii, et al · 2008
Earlier work this paper cites.
LINNAEUS: A species name identification system for biomedical literature
Martin Gerner, Goran Nenadic, and Casey M Bergman · 2010
Earlier work this paper cites.
The eu-adr corpus: annotated drugs, diseases, targets, and their relationships
Erik M Van Mulligen, Annie Fourrier-Reglat, David Gurwitz, Mariam Molokhia, Ainhoa Nieto, Gianluca Trifiro, Jan A Kors, and Laura I Furlong · 2012
Earlier work this paper cites.
Efficient estimation of word representations in vector space
Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean · 2013
Earlier work this paper cites.
The species and organisms resources for fast and accurate identification of taxonomic names in text
Evangelos Pafilis, Sune P Frankild, Lucia Fanini, Sarah Faulwetter, Christina Pavloudi, Aikaterini Vasileiadou, Christos Arvanitidis, and Lars Juhl Jensen · 2013
Earlier work this paper cites.
Ncbi disease corpus: a resource for disease name recognition and concept normalization
Rezarta Islamaj Doğan, Robert Leaman, and Zhiyong Lu · 2014
Earlier work this paper cites.
The chemdner corpus of chemicals and drugs and its annotation principles
Martin Krallinger and et al · 2015
Earlier work this paper cites.
Extraction of relations between genes and diseases from text and large-scale data analysis: implications for translational research
Àlex Bravo, Janet Piñero, Núria Queralt-Rosinach, Michael Rautschka, and Laura I Furlong · 2015
Earlier work this paper cites.
An overview of the bioasq large-scale biomedical semantic indexing and question answering competition
George Tsatsaronis, Georgios Balikas, Prodromos Malakasiotis, Ioannis Partalas, Matthias Zschunke, Michael R Alvers, Dirk Weissenborn, Anastasia Krithara, Sergios Petridis, Dimitris Polychronopoulos, et al · 2015
Earlier work this paper cites.
Biocreative V CDR task corpus: a resource for chemical disease relation extraction
Jiao Li, Yueping Sun, Robin J. Johnson, Daniela Sciaky, Chih-Hsuan Wei, Robert Leaman, Allan Peter Davis, Carolyn J. Mattingly, Thomas C. Wiegers, and Zhiyong Lu · 2016
Earlier work this paper cites.
Communication-efficient learning of deep networks from decentralized data
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas · 2017
Earlier work this paper cites.
Distributed learning of deep neural network over multiple agents
Otkrist Gupta and Ramesh Raskar · 2018
Earlier work this paper cites.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
Earlier work this paper cites.
A discourse-aware attention model for abstractive summarization of long documents
Arman Cohan, Franck Dernoncourt, Doo Soon Kim, Trung Bui, Seokhwan Kim, Walter Chang, and Nazli Goharian · 2018
Cited alongside, same era.
Automatic extraction of gene-disease associations from literature using joint ensemble learning
Balu Bhasuran and Jeyakumar Natarajan · 2018
Cited alongside, same era.
Publicly available clinical bert embeddings
Emily Alsentzer, John R Murphy, Willie Boag, Wei-Hung Weng, Di Jin, Tristan Naumann, and Matthew McDermott · 2019
Cited alongside, same era.
Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter
Victor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf · 2019
Cited alongside, same era.
Federated word2vec: Leveraging federated learning to encourage collaborative representation learning
Daniel Garcia Bernal, Lodovico Giaretta, Sarunas Girdzijauskas, and Magnus Sahlgren · 2021
Later among the works it cites.
Advances and open problems in federated learning
Peter Kairouz, H Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, et al · 2021
Later among the works it cites.
Will we run out of data? an analysis of the limits of scaling datasets in machine learning
Pablo Villalobos, Jaime Sevilla, Lennart Heim, Tamay Besiroglu, Marius Hobbhahn, and Anson Ho · 2022
Later among the works it cites.
On the domain adaptation and generalization of pretrained language models: A survey
Xu Guo and Han Yu · 2022
Later among the works it cites.
Fedbert: When federated learning meets pre-training
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Jaejun Lee, Raphael Tang, and Jimmy Lin · 2019
Cited alongside, same era.
Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al · 2019
Cited alongside, same era.
Don’t stop pretraining: Adapt language models to domains and tasks
Suchin Gururangan, Ana Marasović, Swabha Swayamdipta, Kyle Lo, Iz Beltagy, Doug Downey, and Noah A Smith · 2020
Cited alongside, same era.
Biobert: a pre-trained biomedical language representation model for biomedical text mining
Jinhyuk Lee, Wonjin Yoon, Sungdong Kim, Donghyeon Kim, Sunkyu Kim, Chan Ho So, and Jaewoo Kang · 2020
Cited alongside, same era.
Federated pretraining and fine tuning of bert using clinical notes from multiple silos
Dianbo Liu and Tim Miller · 2020
Cited alongside, same era.
Adversarial and domain-aware bert for cross-domain sentiment analysis
Chunning Du, Haifeng Sun, Jingyu Wang, Qi Qi, and Jianxin Liao · 2020
Cited alongside, same era.
Flower: A friendly federated learning research framework
Daniel J Beutel, Taner Topal, Akhil Mathur, Xinchi Qiu, Javier Fernandez-Marques, Yan Gao, Lorenzo Sani, Kwing Hei Li, Titouan Parcollet, Pedro Porto Buarque de Gusmão, et al · 2020
Cited alongside, same era.
On the opportunities and risks of foundation models
Rishi Bommasani, Drew A Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, et al · 2021
Cited alongside, same era.
Yuanyishu Tian, Yao Wan, Lingjuan Lyu, Dezhong Yao, Hai Jin, and Lichao Sun · 2022
Later among the works it cites.
Autofednlp: An efficient fednlp framework
Dongqi Cai, Yaozong Wu, Shangguang Wang, Felix Xiaozhu Lin, and Mengwei Xu · 2022
Later among the works it cites.
Federated learning from pre-trained models: A contrastive learning approach
Yue Tan, Guodong Long, Jie Ma, Lu Liu, Tianyi Zhou, and Jing Jiang · 2022
Later among the works it cites.
Federated foundation models: Privacy-preserving and collaborative learning for large models
Sixing Yu, J Pablo Muñoz, and Ali Jannesari · 2023
Closest in time.
When foundation model meets federated learning: Motivations, challenges, and future directions
Weiming Zhuang, Chen Chen, and Lingjuan Lyu · 2023
Closest in time.
OpenAI · 2023
Closest in time.
Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al · 2023
Closest in time.
A comprehensive survey on pretrained foundation models: A history from bert to chatgpt
Ce Zhou, Qian Li, Chen Li, Jun Yu, Yixin Liu, Guangjing Wang, Kai Zhang, Cheng Ji, Qiben Yan, Lifang He, et al · 2023
Closest in time.