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When training data is scarce, the incorporation of additional prior knowledge can assist the learning process.
Logical versus analogical or symbolic versus connectionist or neat versus scruffy
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Handwritten digit recognition via deformable prototypes
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Transfer learning
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Using fourier descriptors and spatial models for traffic sign recognition
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The German Traffic Sign Recognition Benchmark: A multi-class classification competition
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Foundations of knowledge acquisition: Cognitive models of complex learning , volume 194
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Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
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How transferable are features in deep neural networks?
J. Yosinski, J. Clune, Y. Bengio, and H. Lipson · 2014
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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
K. He, X. Zhang, S. Ren, and J. Sun · 2015
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A theoretical analysis of optimization by gaussian continuation
H. Mobahi and J. Fisher III · 2015
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This looks like that: Deep learning for interpretable image recognition
C. Chen, O. Li, D. Tao, A. Barnett, C. Rudin, and J. K. Su · 2019
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Rethinking imagenet pre-training
K. He, R. Girshick, and P. Dollár · 2019
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Benchmarking neural network robustness to common corruptions and perturbations
D. Hendrycks and T. Dietterich · 2019
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Using pre-training can improve model robustness and uncertainty
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Variational prototyping-encoder: One-shot learning with prototypical images
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Generation of natural traffic sign images using domain translation with cycle-consistent generative adversarial networks
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Prototypical networks for few-shot learning
J. Snell, K. Swersky, and R. Zemel · 2017
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Label-free supervision of neural networks with physics and domain knowledge
R. Stewart and S. Ermon · 2017
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Relational Inductive Biases, Deep Learning, and Graph Networks
P. W. Battaglia, J. B. Hamrick, V. Bapst, A. Sanchez-Gonzalez, V. Zambaldi, M. Malinowski, A. Tacchetti, D. Raposo, A. Santoro, R. Faulkner, et al · 2018
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O. Li, H. Liu, C. Chen, and C. Rudin · 2018
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Deep double descent: Where bigger models and more data hurt
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What is being transferred in transfer learning?
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Stillleben: Realistic scene synthesis for deep learning in robotics
M. Schwarz and S. Behnke · 2020
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Generalizing from a few examples: A survey on few-shot learning
Y. Wang, Q. Yao, J. T. Kwok, and L. M. Ni · 2020
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Trends in atmospheric carbon dioxide, global monthly mean co2, gml.noaa.gov/ccgg/trends/, 2021
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Knowledge enhanced machine learning pipeline against diverse adversarial attacks
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Physics-informed machine learning
G. E. Karniadakis, I. G. Kevrekidis, L. Lu, P. Perdikaris, S. Wang, and L. Yang · 2021
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Informed machine learning - a taxonomy and survey of integrating prior knowledge into learning systems
L. Von Rueden, S. Mayer, K. Beckh, B. Georgiev, S. Giesselbach, R. Heese, B. Kirsch, M. Walczak, J. Pfrommer, A. Pick, R. Ramamurthy, J. Garcke, C. Bauckhage, and J. Schuecker · 2021
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