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Continuous Normalizing Flows (CNFs) have emerged as promising deep generative models for a wide range of tasks thanks to their invertibility and exact likelihood estimation.
A stochastic estimator of the trace of the influence matrix for laplacian smoothing splines (corr: V19 p433-450)
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Variational inference with normalizing flows
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Infogan: Interpretable representation learning by information maximizing generative adversarial nets
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Adaptive computation time for recurrent neural networks
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Noisy activation functions
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Y. Jernite, E. Grave, A. Joulin, and T. Mikolov · 2016
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Multi-level residual networks from dynamical systems view
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W. Grathwohl, R. T. Chen, J. Betterncourt, I. Sutskever, and D. Duvenaud · 2018
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Don’t decay the learning rate, increase the batch size
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Enresnet: Resnet ensemble via the feynman-kac formalism
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Skipnet: Learning dynamic routing in convolutional networks
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Analyzing inverse problems with invertible neural networks
L. Ardizzone, J. Kruse, C. Rother, and U. Köthe · 2019
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Conditional adversarial generative flow for controllable image synthesis
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Hybrid models with deep and invertible features
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