Beyond Signal Propagation: Is Feature Diversity Necessary in Deep Neural Network Initialization?

• URL: http://arxiv.org/abs/2007.01038v1
• Date: Thu, 2 Jul 2020 11:49:17 GMT
• Title: Beyond Signal Propagation: Is Feature Diversity Necessary in Deep Neural Network Initialization?
• Authors: Yaniv Blumenfeld, Dar Gilboa, Daniel Soudry
• Abstract summary: We construct a deep convolutional network with identical features by initializing almost all the weights to $0$. The architecture also enables perfect signal propagation and stable gradients, and high accuracy on standard benchmarks.
• Score: 31.122757815108884
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep neural networks are typically initialized with random weights, with variances chosen to facilitate signal propagation and stable gradients. It is also believed that diversity of features is an important property of these initializations. We construct a deep convolutional network with identical features by initializing almost all the weights to $0$. The architecture also enables perfect signal propagation and stable gradients, and achieves high accuracy on standard benchmarks. This indicates that random, diverse initializations are \textit{not} necessary for training neural networks. An essential element in training this network is a mechanism of symmetry breaking; we study this phenomenon and find that standard GPU operations, which are non-deterministic, can serve as a sufficient source of symmetry breaking to enable training.

Related papers

• Stabilizing RNN Gradients through Pre-training [3.335932527835653]
  Theory of learning proposes to prevent the gradient from exponential growth with depth or time, to stabilize and improve training. We extend known stability theories to encompass a broader family of deep recurrent networks, requiring minimal assumptions on data and parameter distribution. We propose a new approach to mitigate this issue, that consists on giving a weight of a half to the time and depth contributions to the gradient.
  arXiv  Detail & Related papers  (2023-08-23T11:48:35Z)
• Gradient Descent in Neural Networks as Sequential Learning in RKBS [63.011641517977644]
  We construct an exact power-series representation of the neural network in a finite neighborhood of the initial weights. We prove that, regardless of width, the training sequence produced by gradient descent can be exactly replicated by regularized sequential learning.
  arXiv  Detail & Related papers  (2023-02-01T03:18:07Z)
• Simple initialization and parametrization of sinusoidal networks via their kernel bandwidth [92.25666446274188]
  sinusoidal neural networks with activations have been proposed as an alternative to networks with traditional activation functions. We first propose a simplified version of such sinusoidal neural networks, which allows both for easier practical implementation and simpler theoretical analysis. We then analyze the behavior of these networks from the neural tangent kernel perspective and demonstrate that their kernel approximates a low-pass filter with an adjustable bandwidth.
  arXiv  Detail & Related papers  (2022-11-26T07:41:48Z)
• Dynamical Isometry for Residual Networks [8.21292084298669]
  We show that RISOTTO achieves perfect dynamical isometry for residual networks with ReLU activation functions even for finite depth and width. In experiments, we demonstrate that our approach outperforms schemes proposed to make Batch Normalization obsolete, including Fixup and SkipInit.
  arXiv  Detail & Related papers  (2022-10-05T17:33:23Z)
• Robust Training and Verification of Implicit Neural Networks: A Non-Euclidean Contractive Approach [64.23331120621118]
  This paper proposes a theoretical and computational framework for training and robustness verification of implicit neural networks. We introduce a related embedded network and show that the embedded network can be used to provide an $ell_infty$-norm box over-approximation of the reachable sets of the original network. We apply our algorithms to train implicit neural networks on the MNIST dataset and compare the robustness of our models with the models trained via existing approaches in the literature.
  arXiv  Detail & Related papers  (2022-08-08T03:13:24Z)
• Robust Learning of Parsimonious Deep Neural Networks [0.0]
  We propose a simultaneous learning and pruning algorithm capable of identifying and eliminating irrelevant structures in a neural network. We derive a novel hyper-prior distribution over the prior parameters that is crucial for their optimal selection. We evaluate the proposed algorithm on the MNIST data set and commonly used fully connected and convolutional LeNet architectures.
  arXiv  Detail & Related papers  (2022-05-10T03:38:55Z)
• Subquadratic Overparameterization for Shallow Neural Networks [60.721751363271146]
  We provide an analytical framework that allows us to adopt standard neural training strategies. We achieve the desiderata viaak-Lojasiewicz, smoothness, and standard assumptions.
  arXiv  Detail & Related papers  (2021-11-02T20:24:01Z)
• ZerO Initialization: Initializing Residual Networks with only Zeros and Ones [44.66636787050788]
  Deep neural networks are usually with random weights, with adequately selected initial variance to ensure stable signal propagation during training. There is no consensus on how to select the variance, and this becomes challenging as the number of layers grows. In this work, we replace the widely used random weight initialization with a fully deterministic initialization scheme ZerO, which initializes residual networks with only zeros and ones. Surprisingly, we find that ZerO achieves state-of-the-art performance over various image classification datasets, including ImageNet.
  arXiv  Detail & Related papers  (2021-10-25T06:17:33Z)
• Universality of Gradient Descent Neural Network Training [0.0]
  We discuss the question if it is always possible to redesign a neural network so that it trains well with gradient descent. The construction is not intended for practical computations, but it provides some orientation on the possibilities of meta-learning and related approaches.
  arXiv  Detail & Related papers  (2020-07-27T16:17:19Z)
• Revisiting Initialization of Neural Networks [72.24615341588846]
  We propose a rigorous estimation of the global curvature of weights across layers by approximating and controlling the norm of their Hessian matrix. Our experiments on Word2Vec and the MNIST/CIFAR image classification tasks confirm that tracking the Hessian norm is a useful diagnostic tool.
  arXiv  Detail & Related papers  (2020-04-20T18:12:56Z)
• MSE-Optimal Neural Network Initialization via Layer Fusion [68.72356718879428]
  Deep neural networks achieve state-of-the-art performance for a range of classification and inference tasks. The use of gradient combined nonvolutionity renders learning susceptible to novel problems. We propose fusing neighboring layers of deeper networks that are trained with random variables.
  arXiv  Detail & Related papers  (2020-01-28T18:25:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.