Fetching the paper…
Reading the bibliography…
The electric vehicle (EV) battery supply chain's vulnerability to disruptions necessitates advanced predictive analytics.
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 · 1901
Earlier work this paper cites.
Roberta: A robustly optimized bert pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019 · 1907
Earlier work this paper cites.
A brief statement of schema theory
Selby H Evans. 1967 · 1967
Earlier work this paper cites.
A general learning theory and its application to schema abstraction1
John R Anderson, Paul J Kline, and Charles M Beasley Jr. 1979 · 1979
Earlier work this paper cites.
Leveraging deep learning and language models in revolutionizing water resource management, research, and policy making: A case for chatgpt
Partha Pratim Ray. 2023 · 1986
Earlier work this paper cites.
Theory of linear and integer programming
Alexander Schrijver. 1998 · 1998
Earlier work this paper cites.
The stages of event extraction
David Ahn. 2006 · 2006
Earlier work this paper cites.
Application of machine learning techniques for supply chain demand forecasting
Real Carbonneau, Kevin Laframboise, and Rustam Vahidov. 2008 · 2008
Earlier work this paper cites.
Supply chain modelling using a multi-agent system
Raúl Pino, Isabel Fernández, David de la Fuente, José Parreño, and Paolo Priore. 2010 · 2010
Earlier work this paper cites.
A fuzzy inference system for supply chain risk management
Hülya Behret, Başar Öztayşi, and Cengiz Kahraman. 2012 · 2011
Earlier work this paper cites.
A multi-agent based framework for supply chain risk management
Mihalis Giannakis and Michalis Louis. 2011 · 2011
Earlier work this paper cites.
Sutime: A library for recognizing and normalizing time expressions
Angel X Chang and Christopher D Manning. 2012 · 2012
Earlier work this paper cites.
Tempeval-3: Evaluating events, time expressions, and temporal relations
Naushad UzZaman, Hector Llorens, James Allen, Leon Derczynski, Marc Verhagen, and James Pustejovsky. 2012 · 2012
Earlier work this paper cites.
Smatch: an evaluation metric for semantic feature structures
Shu Cai and Kevin Knight. 2013 · 2013
Earlier work this paper cites.
Towards robust linguistic analysis using ontonotes
Sameer Pradhan, Alessandro Moschitti, Nianwen Xue, Hwee Tou Ng, Anders Björkelund, Olga Uryupina, Yuchen Zhang, and Zhi Zhong. 2013 · 2013
Earlier work this paper cites.
Multilingual and cross-domain temporal tagging
Jannik Strötgen and Michael Gertz. 2013 · 2013
Earlier work this paper cites.
Application of an agent-based supply chain to mitigate supply chain disruptions
Maurício F Blos, Robson M Da Silva, and Paulo E Miyagi. 2015 · 2015
Earlier work this paper cites.
Event extraction via dynamic multi-pooling convolutional neural networks
Yubo Chen, Liheng Xu, Kang Liu, Daojian Zeng, and Jun Zhao. 2015 · 2015
Cited alongside, same era.
An analytical framework for supply network risk propagation: A bayesian network approach
Myles D Garvey, Steven Carnovale, and Sengun Yeniyurt. 2015 · 2015
Cited alongside, same era.
Supplier selection for managing supply risks in supply chain: a fuzzy approach
Sanjoy Kumar Paul. 2015 · 2015
Cited alongside, same era.
A multi-agent based system with big data processing for enhanced supply chain agility
Mihalis Giannakis and Michalis Louis. 2016 · 2016
Cited alongside, same era.
Joint event extraction via recurrent neural networks
Thien Huu Nguyen, Kyunghyun Cho, and Ralph Grishman. 2016 · 2016
Cited alongside, same era.
A quantitative model for disruption mitigation in a supply chain
Scaling language models: Methods, analysis & insights from training gopher
Jack W Rae, Sebastian Borgeaud, Trevor Cai, Katie Millican, Jordan Hoffmann, Francis Song, John Aslanides, Sarah Henderson, Roman Ring, Susannah Young, et al. 2021 · 2021
Later among the works it cites.
Query and extract: Refining event extraction as type-oriented binary decoding
Sijia Wang, Mo Yu, Shiyu Chang, Lichao Sun, and Lifu Huang. 2021 · 2021
Later among the works it cites.
Finetuned language models are zero-shot learners
Jason Wei, Maarten Bosma, Vincent Y Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M Dai, and Quoc V Le. 2021 · 2021
Later among the works it cites.
Zero-shot on-the-fly event schema induction
Rotem Dror, Haoyu Wang, and Dan Roth. 2022 · 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…
Sanjoy Kumar Paul, Ruhul Sarker, and Daryl Essam. 2017 · 2017
Cited alongside, same era.
Multi-tier sustainable global supplier selection using a fuzzy ahp-vikor based approach
Anjali Awasthi, Kannan Govindan, and Stefan Gold. 2018 · 2018
Cited alongside, same era.
Knowledge-based multi-agent system for manufacturing problem solving process in production plants
Alvaro Camarillo, José Ríos, and Klaus-Dieter Althoff. 2018 · 2018
Cited alongside, same era.
Zero-shot transfer learning for event extraction
Lifu Huang, Heng Ji, Kyunghyun Cho, Ido Dagan, Sebastian Riedel, and Clare R Voss. 2018 · 2018
Cited alongside, same era.
Jointly extracting event triggers and arguments by dependency-bridge rnn and tensor-based argument interaction
Lei Sha, Feng Qian, Baobao Chang, and Zhifang Sui. 2018 · 2018
Cited alongside, same era.
Risk assessment of maintenance activities using fuzzy logic
Maryam Gallab, Hafida Bouloiz, Youssef Lamrani Alaoui, and Mohamed Tkiouat. 2019 · 2019
Cited alongside, same era.
Hmeae: Hierarchical modular event argument extraction
Xiaozhi Wang, Ziqi Wang, Xu Han, Zhiyuan Liu, Juanzi Li, Peng Li, Maosong Sun, Jie Zhou, and Xiang Ren. 2019 · 2019
Cited alongside, same era.
Resin-11: Schema-guided event prediction for 11 newsworthy scenarios
Xinya Du, Zixuan Zhang, Sha Li, Pengfei Yu, Hongwei Wang, Tuan Lai, Xudong Lin, Ziqi Wang, Iris Liu, Ben Zhou, et al. 2022 · 2022
Later among the works it cites.
Future of artificial intelligence and its influence on supply chain risk management–a systematic review
A Deiva Ganesh and P Kalpana. 2022 · 2022
Later among the works it cites.
Can language models learn from explanations in context?
Andrew K Lampinen, Ishita Dasgupta, Stephanie CY Chan, Kory Matthewson, Michael Henry Tessler, Antonia Creswell, James L McClelland, Jane X Wang, and Felix Hill. 2022 · 2022
Later among the works it cites.
Abstract meaning representation guided graph encoding and decoding for joint information extraction
Zixuan Zhang and Heng Ji. 2021 · 2022
Later among the works it cites.
Predictive analytics and machine learning for real-time supply chain risk mitigation and agility
Abeer Aljohani. 2023 · 2023
Later among the works it cites.
Artificial intelligence for supply chain management: Disruptive innovation or innovative disruption?
Christian Hendriksen. 2023 · 2023
Later among the works it cites.
Open-domain hierarchical event schema induction by incremental prompting and verification
Sha Li, Ruining Zhao, Manling Li, Heng Ji, Chris Callison-Burch, and Jiawei Han. 2023 · 2023
Later among the works it cites.
A deep learning approach using natural language processing and time-series forecasting towards enhanced food safety
Georgios Makridis, Philip Mavrepis, and Dimosthenis Kyriazis. 2023 · 2023
Later among the works it cites.
Ishani Mondal, Michelle Yuan, Aparna Garimella, Francis Ferraro, Andrew Blair-Stanek, Benjamin Van Durme, Jordan Boyd-Graber, et al. 2023 · 2023
Later among the works it cites.
Human-in-the-loop schema induction
Tianyi Zhang, Isaac Tham, Zhaoyi Hou, Jiaxuan Ren, Liyang Zhou, Hainiu Xu, Li Zhang, Lara J Martin, Rotem Dror, Sha Li, et al. 2023 · 2023
Later among the works it cites.
Language models can improve event prediction by few-shot abductive reasoning
Xiaoming Shi, Siqiao Xue, Kangrui Wang, Fan Zhou, James Zhang, Jun Zhou, Chenhao Tan, and Hongyuan Mei. 2024 · 2024
Closest in time.
Human-ai interaction in the age of llms
Diyi Yang, Sherry Tongshuang Wu, and Marti A Hearst. 2024 · 2024
Closest in time.
Improving supply chain visibility with artificial neural networks
Nathalie Silva, Luís Miguel DF Ferreira, Cristóvão Silva, Vanessa Magalhães, and Pedro Neto. 2017 · 2090
Closest in time.