Review of Deep Learning Algorithms and Architectures
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langone, 2015, Kernel spectral clustering and applications, CoRR
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conneau, 2016, Very deep convolutional networks for text classification, CoRR
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lecun, 1998, Convolutional networks for images, speech, and time series, The Handbook of Brain Theory and Neural Networks, 255
salakhutdinov, 2009, Deep Boltzmann machines, Proc 12th Int Conf Artif Intell Statist, 448
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2019, Top Deep Learning Github Repositories
glorot, 2010, Understanding the difficulty of training deep feedforward neural networks, Proc 13th Int Conf Artif Intell Statist, 249
martens, 2010, Deep learning via Hessian-free optimization, Proc Int Conf Int Conf Mach Learn, 735
reed, 2016, Generative adversarial text to image synthesis
goodfellow, 2014, Generative adversarial networks
kingma, 2013, Auto-encoding variational bayes
ioffe, 2015, Batch normalization: Accelerating deep network training by reducing internal covariate shift, CoRR
kingma, 2014, Adam: A method for stochastic optimization, CoRR
duchi, 2011, Adaptive subgradient methods for online learning and stochastic optimization, J Mach Learn Res, 12, 2121
miikkulainen, 2017, Evolving deep neural networks, CoRR
goldberg, 2013, The Design of Innovation Lessons from and for Competent Genetic Algorithms
sastry, 2005, Genetic Algorithms
escalante, 2009, Particle swarm model selection, J Mach Learn Res, 10, 405
nagpal, 2018, Development and validation of a deep learning algorithm for improving Gleason scoring of prostate cancer, CoRR
nevo, 2019, ML for flood forecasting at scale, CoRR
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wu, 2016, Google’s neural machine translation system: Bridging the gap between human and machine translation, CoRR
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ng, 2019, Machine learning yearning: Technical strategy for ai engineers in the era of deep learning
gavin, 2016, The Levenberg-Marquardt method for nonlinear least squares curve-fitting problems
metz, 2019, Turing Award Won by 3 Pioneers in Artificial Intelligence, 3b
goodfellow, 2016, Deep learning, Adaptive Computation and Machine Learning, 775
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chung, 2014, Empirical evaluation of gated recurrent neural networks on sequence modeling