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Pre-trained machine learning (ML) models have shown great performance for a wide range of applications, in particular in natural language processing (NLP) and computer vision (CV).
Learning and transferring mid-level image representations using convolutional neural networks
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Language models are few-shot learners
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Heavy-tailed Universality predicts trends in test accuracies for very large pre-trained deep neural networks
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Exploring the limits of transfer learning with a unified text-to-text transformer
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Ai for science: Report on the department of energy (doe) town halls on artificial intelligence (ai) for science
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Model reduction and neural networks for parametric pdes
Kaushik Bhattacharya, Bamdad Hosseini, Nikola B Kovachki, and Andrew M Stuart · 2021
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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
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One-shot transfer learning of physics-informed neural networks
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Deep transfer operator learning for partial differential equations under conditional shift
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Analysis of three-dimensional potential problems in non-homogeneous media with physics-informed deep collocation method using material transfer learning and sensitivity analysis
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An empirical analysis of compute-optimal large language model training
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Fourier neural operator with learned deformations for pdes on general geometries
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Reliable extrapolation of deep neural operators informed by physics or sparse observations
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Learning elliptic partial differential equations with randomized linear algebra
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