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Recently, there has been a surge of interest in combining deep learning models with reasoning in order to handle more sophisticated learning tasks.
Sharper bounds for gaussian and empirical processes
Michel Talagrand · 1994
Earlier work this paper cites.
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Vladimir Koltchinskii and Dmitriy Panchenko · 2000
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Stability and generalization
Olivier Bousquet and André Elisseeff · 2002
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Vladimir Koltchinskii, Dmitry Panchenko, et al · 2002
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Rademacher and gaussian complexities: Risk bounds and structural results
Peter L Bartlett and Shahar Mendelson · 2002
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Local rademacher complexities
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Local rademacher complexities and oracle inequalities in risk minimization
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Uniform central limit theorems
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A vector-contraction inequality for rademacher complexities
Andreas Maurer · 2016
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Structured prediction theory based on factor graph complexity
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On large-batch training for deep learning: Generalization gap and sharp minima
Nitish Shirish Keskar, Dheevatsa Mudigere, Jorge Nocedal, Mikhail Smelyanskiy, and Ping Tak Peter Tang · 2016
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Spectrally-normalized margin bounds for neural networks
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Model-agnostic meta-learning for fast adaptation of deep networks
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Learning protein structure with a differentiable simulator
John Ingraham, Adam Riesselman, Chris Sander, and Debora Marks · 2019
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End to end learning and optimization on graphs
Bryan Wilder, Eric Ewing, Bistra Dilkina, and Milind Tambe · 2019
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Backpropagation-friendly eigendecomposition
Wei Wang, Zheng Dang, Yinlin Hu, Pascal Fua, and Mathieu Salzmann · 2019
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End-to-end training of hybrid cnn-crf models for stereo
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Generalization in deep learning
Kenji Kawaguchi, Leslie Pack Kaelbling, and Yoshua Bengio · 2017
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Behnam Neyshabur, Srinadh Bhojanapalli, David McAllester, and Nati Srebro · 2017
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Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising
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Deepproblog: Neural probabilistic logic programming
Robin Manhaeve, Sebastijan Dumancic, Angelika Kimmig, Thomas Demeester, and Luc De Raedt · 2018
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Differentiable dynamic programming for structured prediction and attention
Arthur Mensch and Mathieu Blondel · 2018
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Minshuo Chen, Xingguo Li, and Tuo Zhao · 2019
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Stability and generalization of graph convolutional neural networks
Saurabh Verma and Zhi-Li Zhang · 2019
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Meta-learning with implicit gradients
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Differentiation of blackbox combinatorial solvers
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Deep equilibrium models
Shaojie Bai, J Zico Kolter, and Vladlen Koltun · 2019
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Laurent El Ghaoui, Fangda Gu, Bertrand Travacca, and Armin Askari · 2019
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Rna secondary structure prediction by learning unrolled algorithms
Xinshi Chen, Yu Li, Ramzan Umarov, Xin Gao, and Le Song · 2020
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GLAD: Learning sparse graph recovery
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Generalization and representational limits of graph neural networks
Vikas K Garg, Stefanie Jegelka, and Tommi Jaakkola · 2020
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Deep graph matching via blackbox differentiation of combinatorial solvers
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Learning with differentiable perturbed optimizers
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