Deep Learning with a Classifier System: Initial Results

• URL: http://arxiv.org/abs/2103.01118v1
• Date: Mon, 1 Mar 2021 16:40:12 GMT
• Title: Deep Learning with a Classifier System: Initial Results
• Authors: Richard J. Preen and Larry Bull
• Abstract summary: This article presents the first results from using a learning classifier system capable of performing adaptive computation with deep neural networks. The system automatically reduces the number of weights and units while maintaining performance after achieving a maximum prediction error.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This article presents the first results from using a learning classifier system capable of performing adaptive computation with deep neural networks. Individual classifiers within the population are composed of two neural networks. The first acts as a gating or guarding component, which enables the conditional computation of an associated deep neural network on a per instance basis. Self-adaptive mutation is applied upon reproduction and prediction networks are refined with stochastic gradient descent during lifetime learning. The use of fully-connected and convolutional layers are evaluated on handwritten digit recognition tasks where evolution adapts (i) the gradient descent learning rate applied to each layer (ii) the number of units within each layer, i.e., the number of fully-connected neurons and the number of convolutional kernel filters (iii) the connectivity of each layer, i.e., whether each weight is active (iv) the weight magnitudes, enabling escape from local optima. The system automatically reduces the number of weights and units while maintaining performance after achieving a maximum prediction error.

Related papers

• SA-CNN: Application to text categorization issues using simulated annealing-based convolutional neural network optimization [0.0]
  Convolutional neural networks (CNNs) are a representative class of deep learning algorithms. We introduce SA-CNN neural networks for text classification tasks based on Text-CNN neural networks.
  arXiv  Detail & Related papers  (2023-03-13T14:27:34Z)
• Globally Optimal Training of Neural Networks with Threshold Activation Functions [63.03759813952481]
  We study weight decay regularized training problems of deep neural networks with threshold activations. We derive a simplified convex optimization formulation when the dataset can be shattered at a certain layer of the network.
  arXiv  Detail & Related papers  (2023-03-06T18:59:13Z)
• Classified as unknown: A novel Bayesian neural network [0.0]
  We develop a new efficient Bayesian learning algorithm for fully connected neural networks. We generalize the algorithm for a single perceptron for binary classification in citeH to multi-layer perceptrons for multi-class classification.
  arXiv  Detail & Related papers  (2023-01-31T04:27:09Z)
• WLD-Reg: A Data-dependent Within-layer Diversity Regularizer [98.78384185493624]
  Neural networks are composed of multiple layers arranged in a hierarchical structure jointly trained with a gradient-based optimization. We propose to complement this traditional 'between-layer' feedback with additional 'within-layer' feedback to encourage the diversity of the activations within the same layer. We present an extensive empirical study confirming that the proposed approach enhances the performance of several state-of-the-art neural network models in multiple tasks.
  arXiv  Detail & Related papers  (2023-01-03T20:57:22Z)
• Do We Really Need a Learnable Classifier at the End of Deep Neural Network? [118.18554882199676]
  We study the potential of learning a neural network for classification with the classifier randomly as an ETF and fixed during training. Our experimental results show that our method is able to achieve similar performances on image classification for balanced datasets.
  arXiv  Detail & Related papers  (2022-03-17T04:34:28Z)
• Optimization-Based Separations for Neural Networks [57.875347246373956]
  We show that gradient descent can efficiently learn ball indicator functions using a depth 2 neural network with two layers of sigmoidal activations. This is the first optimization-based separation result where the approximation benefits of the stronger architecture provably manifest in practice.
  arXiv  Detail & Related papers  (2021-12-04T18:07:47Z)
• Learning Neural Network Subspaces [74.44457651546728]
  Recent observations have advanced our understanding of the neural network optimization landscape. With a similar computational cost as training one model, we learn lines, curves, and simplexes of high-accuracy neural networks. With a similar computational cost as training one model, we learn lines, curves, and simplexes of high-accuracy neural networks.
  arXiv  Detail & Related papers  (2021-02-20T23:26:58Z)
• Multi-Sample Online Learning for Spiking Neural Networks based on Generalized Expectation Maximization [42.125394498649015]
  Spiking Neural Networks (SNNs) capture some of the efficiency of biological brains by processing through binary neural dynamic activations. This paper proposes to leverage multiple compartments that sample independent spiking signals while sharing synaptic weights. The key idea is to use these signals to obtain more accurate statistical estimates of the log-likelihood training criterion, as well as of its gradient.
  arXiv  Detail & Related papers  (2021-02-05T16:39:42Z)
• Generalized Leverage Score Sampling for Neural Networks [82.95180314408205]
  Leverage score sampling is a powerful technique that originates from theoretical computer science. In this work, we generalize the results in [Avron, Kapralov, Musco, Musco, Velingker and Zandieh 17] to a broader class of kernels.
  arXiv  Detail & Related papers  (2020-09-21T14:46:01Z)
• Online Sequential Extreme Learning Machines: Features Combined From Hundreds of Midlayers [0.0]
  In this paper, we develop an algorithm called hierarchal online sequential learning algorithm (H-OS-ELM) The algorithm can learn chunk by chunk with fixed or varying block size.
  arXiv  Detail & Related papers  (2020-06-12T00:50:04Z)
• An Effective and Efficient Initialization Scheme for Training Multi-layer Feedforward Neural Networks [5.161531917413708]
  We propose a novel network initialization scheme based on the celebrated Stein's identity. A proposed SteinGLM method is shown through extensive numerical results to be much faster and more accurate than other popular methods commonly used for training neural networks.
  arXiv  Detail & Related papers  (2020-05-16T16:17:37Z)

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.