Quantal synaptic dilution enhances sparse encoding and dropout regularisation in deep networks

• URL: http://arxiv.org/abs/2009.13165v1
• Date: Mon, 28 Sep 2020 09:29:49 GMT
• Title: Quantal synaptic dilution enhances sparse encoding and dropout regularisation in deep networks
• Abstract summary: Dropout is a technique that silences the activity of unitsally while training deep networks to reduce overfitting. Here we introduce Quantal Synaptic Dilution (QSD), a biologically plausible model of dropout regularisation based on the quantal properties of neuronal synapses.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Dropout is a technique that silences the activity of units stochastically while training deep networks to reduce overfitting. Here we introduce Quantal Synaptic Dilution (QSD), a biologically plausible model of dropout regularisation based on the quantal properties of neuronal synapses, that incorporates heterogeneities in response magnitudes and release probabilities for vesicular quanta. QSD outperforms standard dropout in ReLU multilayer perceptrons, with enhanced sparse encoding at test time when dropout masks are replaced with identity functions, without shifts in trainable weight or bias distributions. For convolutional networks, the method also improves generalisation in computer vision tasks with and without inclusion of additional forms of regularisation. QSD also outperforms standard dropout in recurrent networks for language modelling and sentiment analysis. An advantage of QSD over many variations of dropout is that it can be implemented generally in all conventional deep networks where standard dropout is applicable.

Related papers

• Learning Orthogonal Multi-Index Models Beyond Small Initialization: Incremental Learning, Competitive Dynamics and Symmetry [52.75288960194919]
  Recent work has identified incremental learning in shallow networks trained on single-index and multi-index models.<n>We introduce a symmetry-based finite-width approximation via symmetrized networks, rather than comparing directly with an infinite-width limit.
  arXiv  Detail & Related papers  (2026-09-09T22:35:31Z)
• Analytic theory of dropout regularization [1.243080988483032]
  Dropout is a regularization technique widely used in training artificial neural networks.<n>We analytically study dropout in two-layer neural networks trained with online gradient descent.
  arXiv  Detail & Related papers  (2025-05-12T17:45:02Z)
• Quantization without Tears [26.5790668319932]
  Quantization without Tears (QwT) is a method that simultaneously achieves quantization speed, accuracy, simplicity, and generality.<n>QwT incorporates a lightweight additional structure into the quantized network to mitigate information loss during quantization.<n> Extensive experiments across various vision, language, and multimodal tasks demonstrate that QwT is both highly effective and versatile.
  arXiv  Detail & Related papers  (2024-11-21T08:13:24Z)
• A Scalable Walsh-Hadamard Regularizer to Overcome the Low-degree Spectral Bias of Neural Networks [79.28094304325116]
  Despite the capacity of neural nets to learn arbitrary functions, models trained through gradient descent often exhibit a bias towards simpler'' functions. We show how this spectral bias towards low-degree frequencies can in fact hurt the neural network's generalization on real-world datasets. We propose a new scalable functional regularization scheme that aids the neural network to learn higher degree frequencies.
  arXiv  Detail & Related papers  (2023-05-16T20:06:01Z)
• Generalization and Estimation Error Bounds for Model-based Neural Networks [78.88759757988761]
  We show that the generalization abilities of model-based networks for sparse recovery outperform those of regular ReLU networks. We derive practical design rules that allow to construct model-based networks with guaranteed high generalization.
  arXiv  Detail & Related papers  (2023-04-19T16:39:44Z)
• Improving the Trainability of Deep Neural Networks through Layerwise Batch-Entropy Regularization [1.3999481573773072]
  We introduce and evaluate the batch-entropy which quantifies the flow of information through each layer of a neural network. We show that we can train a "vanilla" fully connected network and convolutional neural network with 500 layers by simply adding the batch-entropy regularization term to the loss function.
  arXiv  Detail & Related papers  (2022-08-01T20:31:58Z)
• Implicit regularization of dropout [3.42658286826597]
  It is important to understand how dropout, a popular regularization method, aids in achieving a good generalization solution during neural network training. In this work, we present a theoretical derivation of an implicit regularization of dropout, which is validated by a series of experiments. We experimentally find that the training with dropout leads to the neural network with a flatter minimum compared with standard gradient descent training.
  arXiv  Detail & Related papers  (2022-07-13T04:09:14Z)
• Cluster-Promoting Quantization with Bit-Drop for Minimizing Network Quantization Loss [61.26793005355441]
  Cluster-Promoting Quantization (CPQ) finds the optimal quantization grids for neural networks. DropBits is a new bit-drop technique that revises the standard dropout regularization to randomly drop bits instead of neurons. We experimentally validate our method on various benchmark datasets and network architectures.
  arXiv  Detail & Related papers  (2021-09-05T15:15:07Z)
• LocalDrop: A Hybrid Regularization for Deep Neural Networks [98.30782118441158]
  We propose a new approach for the regularization of neural networks by the local Rademacher complexity called LocalDrop. A new regularization function for both fully-connected networks (FCNs) and convolutional neural networks (CNNs) has been developed based on the proposed upper bound of the local Rademacher complexity.
  arXiv  Detail & Related papers  (2021-03-01T03:10:11Z)
• Recurrence of Optimum for Training Weight and Activation Quantized Networks [4.103701929881022]
  Training deep learning models with low-precision weights and activations involves a demanding optimization task. We show how to overcome the nature of network quantization. We also show numerical evidence of the recurrence phenomenon of weight evolution in training quantized deep networks.
  arXiv  Detail & Related papers  (2020-12-10T09:14:43Z)
• Optical Phase Dropout in Diffractive Deep Neural Network [0.0]
  Unitary learning serves to update unitary weights update in deep complex-valued neural network with full connections. The square matrix property of unitary weights induces that the function signal has a limited dimension that could not generalize well. Phase dropout in unitary space that is evolved from a complex dropout and has a statistical inference is formulated for the first time. The equivalence between digital and diffractive model determines compound modulations that could successfully circumvent the nonlinear activations physically implemented in [DN]2.
  arXiv  Detail & Related papers  (2020-11-28T13:33:23Z)
• Improve Generalization and Robustness of Neural Networks via Weight Scale Shifting Invariant Regularizations [52.493315075385325]
  We show that a family of regularizers, including weight decay, is ineffective at penalizing the intrinsic norms of weights for networks with homogeneous activation functions. We propose an improved regularizer that is invariant to weight scale shifting and thus effectively constrains the intrinsic norm of a neural network.
  arXiv  Detail & Related papers  (2020-08-07T02:55:28Z)
• Beyond Dropout: Feature Map Distortion to Regularize Deep Neural Networks [107.77595511218429]
  In this paper, we investigate the empirical Rademacher complexity related to intermediate layers of deep neural networks. We propose a feature distortion method (Disout) for addressing the aforementioned problem. The superiority of the proposed feature map distortion for producing deep neural network with higher testing performance is analyzed and demonstrated.
  arXiv  Detail & Related papers  (2020-02-23T13:59:13Z)

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.