Fetching the paper…
Reading the bibliography…
Foundation models (FMs) are able to leverage large volumes of unlabeled data to demonstrate superior performance across a wide range of tasks.
Approximation by superpositions of a sigmoidal function
George Cybenko · 1989
Earlier work this paper cites.
Approximation capabilities of multilayer feedforward networks
Kurt Hornik · 1991
Earlier work this paper cites.
Convex optimization
Stephen P Boyd and Lieven Vandenberghe · 2004
Earlier work this paper cites.
A new approach to cross-modal multimedia retrieval
Nikhil Rasiwasia, Jose Costa Pereira, Emanuele Coviello, Gabriel Doyle, Gert RG Lanckriet, Roger Levy, and Nuno Vasconcelos · 2010
Earlier work this paper cites.
Linear algebra and its applications
Gilbert Strang · 2012
Earlier work this paper cites.
Translating embeddings for modeling multi-relational data
Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, and Oksana Yakhnenko · 2013
Earlier work this paper cites.
Knowledge graph embedding by translating on hyperplanes
Zhen Wang, Jianwen Zhang, Jianlin Feng, and Zheng Chen · 2014
Earlier work this paper cites.
Knowledge graph embedding via dynamic mapping matrix
Guoliang Ji, Shizhu He, Liheng Xu, Kang Liu, and Jun Zhao · 2015
Earlier work this paper cites.
Learning entity and relation embeddings for knowledge graph completion
Yankai Lin, Zhiyuan Liu, Maosong Sun, Yang Liu, and Xuan Zhu · 2015
Earlier work this paper cites.
Zinc 15–ligand discovery for everyone
Teague Sterling and John J Irwin · 2015
Earlier work this paper cites.
Embedding entities and relations for learning and inference in knowledge bases
Bishan Yang, Scott Wen-tau Yih, Xiaodong He, Jianfeng Gao, and Li Deng · 2015
Earlier work this paper cites.
Complex embeddings for simple link prediction
Théo Trouillon, Johannes Welbl, Sebastian Riedel, Éric Gaussier, and Guillaume Bouchard · 2016
Earlier work this paper cites.
Mouse genome informatics (mgi): resources for mining mouse genetic, genomic, and biological data in support of primary and translational research
Janan T Eppig, Cynthia L Smith, Judith A Blake, Martin Ringwald, James A Kadin, Joel E Richardson, and Carol J Bult · 2017
Earlier work this paper cites.
Finite-dimensional vector spaces
Paul R Halmos · 2017
Earlier work this paper cites.
OpenKE: An open toolkit for knowledge embedding
Xu Han, Shulin Cao, Lv Xin, Yankai Lin, Zhiyuan Liu, Maosong Sun, and Juanzi Li · 2018
Earlier work this paper cites.
A multimodal translation-based approach for knowledge graph representation learning
Hatem Mousselly-Sergieh, Teresa Botschen, Iryna Gurevych, and Stefan Roth · 2018
Earlier work this paper cites.
Representation learning with contrastive predictive coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals · 2018
Earlier work this paper cites.
Multifaceted protein–protein interaction prediction based on siamese residual rcnn
Muhao Chen, Chelsea J-T Ju, Guangyu Zhou, Xuelu Chen, Tianran Zhang, Kai-Wei Chang, Carlo Zaniolo, and Wei Wang · 2019
Earlier work this paper cites.
A course in functional analysis , volume 96
John B Conway · 2019
Cited alongside, same era.
Rotate: Knowledge graph embedding by relational rotation in complex space
Zhiqing Sun, Zhi-Hong Deng, Jian-Yun Nie, and Jian Tang · 2019
Cited alongside, same era.
Integrating image-based and knowledge-based representation learning
Ruobing Xie, Stefan Heinrich, Zhiyuan Liu, Cornelius Weber, Yuan Yao, Stefan Wermter, and Maosong Sun · 2019
Cited alongside, same era.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
Cited alongside, same era.
Molecular representation learning with language models and domain-relevant auxiliary tasks
Benedek Fabian, Thomas Edlich, Héléna Gaspar, Marwin Segler, Joshua Meyers, Marco Fiscato, and Mohamed Ahmed · 2020
Cited alongside, same era.
Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences
Alexander Rives, Joshua Meier, Tom Sercu, Siddharth Goyal, Zeming Lin, Jason Liu, Demi Guo, Myle Ott, C Lawrence Zitnick, Jerry Ma, et al · 2021
Later among the works it cites.
Translation between molecules and natural language
Carl Edwards, Tuan Lai, Kevin Ros, Garrett Honke, Kyunghyun Cho, and Heng Ji · 2022
Later among the works it cites.
Transdti: transformer-based language models for estimating dtis and building a drug recommendation workflow
Yogesh Kalakoti, Shashank Yadav, and Durai Sundar · 2022
Later among the works it cites.
MMKRL: A robust embedding approach for multi-modal knowledge graph representation learning
Xinyu Lu, Lifang Wang, Zejun Jiang, Shichang He, and Shizhong Liu · 2022
Later among the works it cites.
Adapting protein language models for rapid dti prediction
Samuel Sledzieski, Rohit Singh, Lenore Cowen, and Bonnie Berger · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Drkg - drug repurposing knowledge graph for covid-19
Vassilis N. Ioannidis, Xiang Song, Saurav Manchanda, Mufei Li, Xiaoqin Pan, Da Zheng, Xia Ning, Xiangxiang Zeng, and George Karypis · 2020
Cited alongside, same era.
Inductive relation prediction by subgraph reasoning
Komal Teru, Etienne Denis, and Will Hamilton · 2020
Cited alongside, same era.
MolGPT: molecular generation using a transformer-decoder model
Viraj Bagal, Rishal Aggarwal, PK Vinod, and U Deva Priyakumar · 2021
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.
Prottrans: Toward understanding the language of life through self-supervised learning
Ahmed Elnaggar, Michael Heinzinger, Christian Dallago, Ghalia Rehawi, Yu Wang, Llion Jones, Tom Gibbs, Tamas Feher, Christoph Angerer, Martin Steinegger, et al · 2021
Cited alongside, same era.
Structure-based protein function prediction using graph convolutional networks
Vladimir Gligorijević, P Douglas Renfrew, Tomasz Kosciolek, Julia Koehler Leman, Daniel Berenberg, Tommi Vatanen, Chris Chandler, Bryn C Taylor, Ian M Fisk, Hera Vlamakis, et al · 2021
Cited alongside, same era.
Domain-specific language model pretraining for biomedical natural language processing
Yu Gu, Robert Tinn, Hao Cheng, Michael Lucas, Naoto Usuyama, Xiaodong Liu, Tristan Naumann, Jianfeng Gao, and Hoifung Poon · 2021
Cited alongside, same era.
Ross Taylor, Marcin Kardas, Guillem Cucurull, Thomas Scialom, Anthony Hartshorn, Elvis Saravia, Andrew Poulton, Viktor Kerkez, and Robert Stojnic · 2022
Later among the works it cites.
Learning functional properties of proteins with language models
Serbulent Unsal, Heval Atas, Muammer Albayrak, Kemal Turhan, Aybar C Acar, and Tunca Doğan · 2022
Later among the works it cites.
Medclip: Contrastive learning from unpaired medical images and text
Zifeng Wang, Zhenbang Wu, Dinesh Agarwal, and Jimeng Sun · 2022
Later among the works it cites.
Ontoprotein: Protein pretraining with gene ontology embedding
Ningyu Zhang, Zhen Bi, Xiaozhuan Liang, Siyuan Cheng, Haosen Hong, Shumin Deng, Qiang Zhang, Jiazhang Lian, and Huajun Chen · 2022
Later among the works it cites.
Building a knowledge graph to enable precision medicine
Payal Chandak, Kexin Huang, and Marinka Zitnik · 2023
Closest in time.
Imagebind: One embedding space to bind them all
Rohit Girdhar, Alaaeldin El-Nouby, Zhuang Liu, Mannat Singh, Kalyan Vasudev Alwala, Armand Joulin, and Ishan Misra · 2023
Closest in time.
Protein-protein interaction prediction is achievable with large language models
Logan Hallee and Jason P Gleghorn · 2023
Closest in time.
Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C Berg, Wan-Yen Lo, et al · 2023
Closest in time.
Evolutionary-scale prediction of atomic-level protein structure with a language model
Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Nikita Smetanin, Robert Verkuil, Ori Kabeli, Yaniv Shmueli, et al · 2023
Closest in time.
A text-guided protein design framework
Shengchao Liu, Yutao Zhu, Jiarui Lu, Zhao Xu, Weili Nie, Anthony Gitter, Chaowei Xiao, Jian Tang, Hongyu Guo, and Anima Anandkumar · 2023
Closest in time.
Large language models generate functional protein sequences across diverse families
Ali Madani, Ben Krause, Eric R Greene, Subu Subramanian, Benjamin P Mohr, James M Holton, Jose Luis Olmos Jr, Caiming Xiong, Zachary Z Sun, Richard Socher, et al · 2023
Closest in time.
Large ai models in health informatics: Applications, challenges, and the future
Jianing Qiu, Lin Li, Jiankai Sun, Jiachuan Peng, Peilun Shi, Ruiyang Zhang, Yinzhao Dong, Kyle Lam, Frank P-W Lo, Bo Xiao, et al · 2023
Closest in time.