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There is a recent surge of interest in designing deep architectures based on the update steps in traditional algorithms, or learning neural networks to improve and replace traditional algorithms.
Optimal stopping of controlled jump diffusion processes: a viscosity solution approach
Pham, H · 1998
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A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics
Martin, D., Fowlkes, C., Tal, D., and Malik, J · 2001
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Mixed optimal stopping and stochastic control problems with semicontinuous final reward for diffusion processes
Ceci, C. and Bassan, B · 2004
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Image denoising by sparse 3-d transform-domain collaborative filtering
Dabov, K., Foi, A., Katkovnik, V., and Egiazarian, K · 2007
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Optimal stopping rules , volume 8
Shiryaev, A. N · 2007
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Iterative thresholding for sparse approximations
Blumensath, T. and Davies, M. E · 2008
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A fast iterative shrinkage-thresholding algorithm for linear inverse problems
Beck, A. and Teboulle, M · 2009
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Optimal response initiation: Why recent experience matters
Jones, M., Kinoshita, S., and Mozer, M. C · 2009
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Contour detection and hierarchical image segmentation
Arbelaez, P., Maire, M., Fowlkes, C., and Malik, J · 2010
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Learning fast approximations of sparse coding
Gregor, K. and LeCun, Y · 2010
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Parameter learning with truncated message-passing
Domke, J · 2011
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One shot learning of simple visual concepts
Lake, B., Salakhutdinov, R., Gross, J., and Tenenbaum, J · 2011
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Auto-encoding variational bayes
Kingma, D. P. and Welling, M · 2013
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Bilevel sparse models for polyphonic music transcription
Yakar, T. B., Litman, R., Sprechmann, P., Bronstein, A. M., and Sapiro, G · 2013
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Weighted nuclear norm minimization with application to image denoising
Gu, S., Zhang, L., Zuo, W., and Feng, X · 2014
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Very deep convolutional networks for large-scale image recognition
Simonyan, K. and Zisserman, A · 2014
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Learning to learn by gradient descent by gradient descent
Andrychowicz, M., Denil, M., Gomez, S., Hoffman, M. W., Pfau, D., Schaul, T., Shillingford, B., and De Freitas, N · 2016
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Li, K. and Malik, J · 2016
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f-gan: Training generative neural samplers using variational divergence minimization
Nowozin, S., Cseke, B., and Tomioka, R · 2016
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Deep admm-net for compressive sensing mri
Sun, J., Li, H., Xu, Z., et al · 2016
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Branchynet: Fast inference via early exiting from deep neural networks
Teerapittayanon, S., McDanel, B., and Kung, H.-T · 2016
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End-to-end learning for structured prediction energy networks
Multi-scale dense networks for resource efficient image classification
Huang, G., Chen, D., Li, T., Wu, F., van der Maaten, L., and Weinberger, K · 2018
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Auto-encoding sequential monte carlo
Le, T. A., Igl, M., Rainforth, T., Jin, T., and Wood, F · 2018
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Gradient-based meta-learning with learned layerwise metric and subspace
Lee, Y. and Choi, S · 2018
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Universal denoising networks: a novel cnn architecture for image denoising
Lefkimmiatis, S · 2018
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Tadam: Task dependent adaptive metric for improved few-shot learning
Oreshkin, B., López, P. R., and Lacoste, A · 2018
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Few-shot image recognition by predicting parameters from activations
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Belanger, D., Yang, B., and McCallum, A · 2017
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Amp-inspired deep networks for sparse linear inverse problems
Borgerding, M., Schniter, P., and Rangan, S · 2017
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Model-agnostic meta-learning for fast adaptation of deep networks
Finn, C., Abbeel, P., and Levine, S · 2017
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beta-VAE: Learning basic visual concepts with a constrained variational framework
Higgins, I., Matthey, L., Pal, A., Burgess, C., Glorot, X., Botvinick, M., Mohamed, S., and Lerchner, A · 2017
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Meta-sgd: Learning to learn quickly for few-shot learning
Li, Z., Zhou, F., Chen, F., and Li, H · 2017
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Learned d-amp: Principled neural network based compressive image recovery
Metzler, C., Mousavi, A., and Baraniuk, R · 2017
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Optimization as a model for few-shot learning
Ravi, S. and Larochelle, H · 2017
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Qiao, S., Liu, C., Shen, W., and Yuille, A. L · 2018
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Ista-net: Interpretable optimization-inspired deep network for image compressive sensing
Zhang, J. and Ghanem, B · 2018
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Deep optimal stopping
Becker, S., Cheridito, P., and Jentzen, A · 2019
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Particle flow bayes’ rule
Chen, X., Dai, H., and Song, L · 2019
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Learning protein structure with a differentiable simulator
Ingraham, J., Riesselman, A., Sander, C., and Marks, D · 2019
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Shallow-deep networks: Understanding and mitigating network overthinking
Kaya, Y., Hong, S., and Dumitras, T · 2019
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ALISTA: Analytic weights are as good as learned weights in LISTA
Liu, J., Chen, X., Wang, Z., and Yin, W · 2019
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Dynamically unfolding recurrent restorer: A moving endpoint control method for image restoration
Zhang, X., Lu, Y., Liu, J., and Dong, B · 2019
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RNA secondary structure prediction by learning unrolled algorithms
Chen, X., Li, Y., Umarov, R., Gao, X., and Song, L · 2020
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Learning to balance: Bayesian meta-learning for imbalanced and out-of-distribution tasks
Na, D., Lee, H. B., Lee, H., Kim, S., Park, M., Yang, E., and Hwang, S. J · 2020
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GLAD: Learning sparse graph recovery
Shrivastava, H., Chen, X., Chen, B., Lan, G., Aluru, S., Liu, H., and Song, L · 2020
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