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complex_parameters [2018/03/21 17:23]
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complex_parameters [2018/09/28 21:01] (current)
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 The data points are interpreted as particles moving along a flow defined by the vector field which intuitively represents the desired movement to enable classification. The architecture moves the data points from their original configuration to anew one following the streamlines of the vector field with the objective of achieving a final configuration where classes are separable. An optimization problem is solved through gradient descent to learn this vector field. The data points are interpreted as particles moving along a flow defined by the vector field which intuitively represents the desired movement to enable classification. The architecture moves the data points from their original configuration to anew one following the streamlines of the vector field with the objective of achieving a final configuration where classes are separable. An optimization problem is solved through gradient descent to learn this vector field.
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 +https://​arxiv.org/​abs/​1803.04386v2 Flipout: Efficient Pseudo-Independent Weight Perturbations on Mini-Batches
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 +We introduce flipout, an efficient method for decorrelating the gradients within a mini-batch by implicitly sampling pseudo-independent weight perturbations for each example. Empirically,​ flipout achieves the ideal linear variance reduction for fully connected networks, convolutional networks, and RNNs. We find significant speedups in training neural networks with multiplicative Gaussian perturbations.
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 +https://​eng.uber.com/​differentiable-plasticity/​
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 +https://​arxiv.org/​abs/​1711.01297v1 Implicit Weight Uncertainty in Neural Networks
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 +http://​mdolab.engin.umich.edu/​sites/​default/​files/​Martins2003CSD.pdf The Complex-Step Derivative Approximation
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 +https://​github.com/​facebookresearch/​QuaterNet QuaterNet: A Quaternion-based Recurrent Model for Human Motion
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 +https://​openreview.net/​forum?​id=ByMHvs0cFQ Quaternion Recurrent Neural Networks ​
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