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Temporal knowledge graph (TKG) forecasting benchmarks challenge models to predict future facts using knowledge of past facts.
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.
Judgemental and statistical time series forecasting: a review of the literature
Richard Webby and Marcus O’Connor. 1996 · 1996
Earlier work this paper cites.
Principles of forecasting: a handbook for researchers and practitioners , volume 30
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Earlier work this paper cites.
Automatic time series forecasting: the forecast package for r
Rob J Hyndman and Yeasmin Khandakar. 2008 · 2008
Earlier work this paper cites.
Yago3: A knowledge base from multilingual wikipedias
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Earlier work this paper cites.
Learning sequence encoders for temporal knowledge graph completion
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Earlier work this paper cites.
Deriving validity time in knowledge graph
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. 2019 · 2019
Earlier work this paper cites.
Think globally, act locally: A deep neural network approach to high-dimensional time series forecasting
Rajat Sen, Hsiang-Fu Yu, and Inderjit S Dhillon. 2019 · 2019
Earlier work this paper cites.
Diachronic embedding for temporal knowledge graph completion
Rishab Goel, Seyed Mehran Kazemi, Marcus Brubaker, and Pascal Poupart. 2020 · 2020
Earlier work this paper cites.
Recurrent event network: Autoregressive structure inferenceover temporal knowledge graphs
Woojeong Jin, Meng Qu, Xisen Jin, and Xiang Ren. 2020 · 2020
Earlier work this paper cites.
Tensor decompositions for temporal knowledge base completion
Timothée Lacroix, Guillaume Obozinski, and Nicolas Usunier. 2020 · 2020
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Transformers: State-of-the-art natural language processing
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Learning neural ordinary equations for forecasting future links on temporal knowledge graphs
Zhen Han, Zifeng Ding, Yunpu Ma, Yujia Gu, and Volker Tresp. 2021b · 2021
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Unsolved problems in ml safety
Dan Hendrycks, Nicholas Carlini, John Schulman, and Jacob Steinhardt. 2021 · 2021
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ForecastQA: A question answering challenge for event forecasting with temporal text data
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Palm: Scaling language modeling with pathways
Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, et al. 2022 · 2022
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What can transformers learn in-context? a case study of simple function classes
Shivam Garg, Dimitris Tsipras, Percy S Liang, and Gregory Valiant. 2022 · 2022
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On the evaluation of methods for temporal knowledge graph forecasting
Julia Gastinger, Timo Sztyler, Lokesh Sharma, and Anett Schuelke. 2022 · 2022
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Teaching models to express their uncertainty in words
Stephanie Lin, Jacob Hilton, and Owain Evans. 2022 · 2022
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Tlogic: Temporal logical rules for explainable link forecasting on temporal knowledge graphs
Yushan Liu, Yunpu Ma, Marcel Hildebrandt, Mitchell Joblin, and Volker Tresp. 2022 · 2022
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Temporal knowledge graph reasoning based on evolutional representation learning
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TimeTraveler: Reinforcement learning for temporal knowledge graph forecasting
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Learning from history: Modeling temporal knowledge graphs with sequential copy-generation networks
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Gpt-neox-20b: An open-source autoregressive language model
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Data distributional properties drive emergent in-context learning in transformers
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Rethinking the role of demonstrations: What makes in-context learning work?
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Impact of pretraining term frequencies on few-shot numerical reasoning
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An explanation of in-context learning as implicit bayesian inference
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Forecasting future world events with neural networks
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A theory of emergent in-context learning as implicit structure induction
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Larger language models do in-context learning differently
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