Quaternion-Valued Recurrent Projection Neural Networks on Unit Quaternions

• URL: http://arxiv.org/abs/2001.11846v1
• Date: Thu, 30 Jan 2020 11:25:45 GMT
• Title: Quaternion-Valued Recurrent Projection Neural Networks on Unit Quaternions
• Authors: Marcos Eduardo Valle and Rodolfo Anibal Lobo
• Abstract summary: We present the quaternion-valued recurrent projection neural networks (QRPNNs) QRPNNs are obtained by combining the non-local projection learning with the quaternion-valued recurrent correlation neural network (QRCNNs)
• Score: 1.3706331473063877
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Hypercomplex-valued neural networks, including quaternion-valued neural networks, can treat multi-dimensional data as a single entity. In this paper, we present the quaternion-valued recurrent projection neural networks (QRPNNs). Briefly, QRPNNs are obtained by combining the non-local projection learning with the quaternion-valued recurrent correlation neural network (QRCNNs). We show that QRPNNs overcome the cross-talk problem of QRCNNs. Thus, they are appropriate to implement associative memories. Furthermore, computational experiments reveal that QRPNNs exhibit greater storage capacity and noise tolerance than their corresponding QRCNNs.

Related papers

• Delay Neural Networks (DeNN) for exploiting temporal information in event-based datasets [49.1574468325115]
  Delay Neural Networks (DeNN) are designed to explicitly use exact continuous temporal information of spikes in both forward and backward passes. Good performances are obtained, especially for datasets where temporal information is important.
  arXiv  Detail & Related papers  (2025-01-10T14:58:15Z)
• CTRQNets & LQNets: Continuous Time Recurrent and Liquid Quantum Neural Networks [76.53016529061821]
  Liquid Quantum Neural Network (LQNet) and Continuous Time Recurrent Quantum Neural Network (CTRQNet) developed. LQNet and CTRQNet achieve accuracy increases as high as 40% on CIFAR 10 through binary classification.
  arXiv  Detail & Related papers  (2024-08-28T00:56:03Z)
• Investigating Sparsity in Recurrent Neural Networks [0.0]
  This thesis focuses on investigating the effects of pruning and Sparse Recurrent Neural Networks on the performance of RNNs. We first describe the pruning of RNNs, its impact on the performance of RNNs, and the number of training epochs required to regain accuracy after the pruning is performed. Next, we continue with the creation and training of Sparse Recurrent Neural Networks and identify the relation between the performance and the graph properties of its underlying arbitrary structure.
  arXiv  Detail & Related papers  (2024-07-30T07:24:58Z)
• A Hybrid Neural Coding Approach for Pattern Recognition with Spiking Neural Networks [53.31941519245432]
  Brain-inspired spiking neural networks (SNNs) have demonstrated promising capabilities in solving pattern recognition tasks. These SNNs are grounded on homogeneous neurons that utilize a uniform neural coding for information representation. In this study, we argue that SNN architectures should be holistically designed to incorporate heterogeneous coding schemes.
  arXiv  Detail & Related papers  (2023-05-26T02:52:12Z)
• Extrapolation and Spectral Bias of Neural Nets with Hadamard Product: a Polynomial Net Study [55.12108376616355]
  The study on NTK has been devoted to typical neural network architectures, but is incomplete for neural networks with Hadamard products (NNs-Hp) In this work, we derive the finite-width-K formulation for a special class of NNs-Hp, i.e., neural networks. We prove their equivalence to the kernel regression predictor with the associated NTK, which expands the application scope of NTK.
  arXiv  Detail & Related papers  (2022-09-16T06:36:06Z)
• The Spectral Bias of Polynomial Neural Networks [63.27903166253743]
  Polynomial neural networks (PNNs) have been shown to be particularly effective at image generation and face recognition, where high-frequency information is critical. Previous studies have revealed that neural networks demonstrate a $textitspectral bias$ towards low-frequency functions, which yields faster learning of low-frequency components during training. Inspired by such studies, we conduct a spectral analysis of the Tangent Kernel (NTK) of PNNs. We find that the $Pi$-Net family, i.e., a recently proposed parametrization of PNNs, speeds up the
  arXiv  Detail & Related papers  (2022-02-27T23:12:43Z)
• Quaternion-Valued Convolutional Neural Network Applied for Acute Lymphoblastic Leukemia Diagnosis [1.4174475093445233]
  This paper explores the quaternion-valued convolutional neural network application for a pattern recognition task from medicine. Precisely, we compare the performance of real-valued and quaternion-valued convolutional neural networks to classify lymphoblasts from the peripheral blood smear microscopic images. The quaternion-valued convolutional neural network achieved better or similar performance than its corresponding real-valued network but using only 34% of its parameters.
  arXiv  Detail & Related papers  (2021-12-13T14:03:09Z)
• Quantum-inspired Complex Convolutional Neural Networks [17.65730040410185]
  We improve the quantum-inspired neurons by exploiting the complex-valued weights which have richer representational capacity and better non-linearity. We draw the models of quantum-inspired convolutional neural networks (QICNNs) capable of processing high-dimensional data. The performance of classification accuracy of the five QICNNs are tested on the MNIST and CIFAR-10 datasets.
  arXiv  Detail & Related papers  (2021-10-31T03:10:48Z)
• Neuroevolution of a Recurrent Neural Network for Spatial and Working Memory in a Simulated Robotic Environment [57.91534223695695]
  We evolved weights in a biologically plausible recurrent neural network (RNN) using an evolutionary algorithm to replicate the behavior and neural activity observed in rats. Our method demonstrates how the dynamic activity in evolved RNNs can capture interesting and complex cognitive behavior.
  arXiv  Detail & Related papers  (2021-02-25T02:13:52Z)
• Combining Spiking Neural Network and Artificial Neural Network for Enhanced Image Classification [1.8411688477000185]
  spiking neural networks (SNNs) that more closely resemble biological brain synapses have attracted attention owing to their low power consumption. We build versatile hybrid neural networks (HNNs) that improve the concerned performance.
  arXiv  Detail & Related papers  (2021-02-21T12:03:16Z)
• Toward Trainability of Quantum Neural Networks [87.04438831673063]
  Quantum Neural Networks (QNNs) have been proposed as generalizations of classical neural networks to achieve the quantum speed-up. Serious bottlenecks exist for training QNNs due to the vanishing with gradient rate exponential to the input qubit number. We show that QNNs with tree tensor and step controlled structures for the application of binary classification. Simulations show faster convergent rates and better accuracy compared to QNNs with random structures.
  arXiv  Detail & Related papers  (2020-11-12T08:32:04Z)
• Nonparametric Regression Quantum Neural Networks [0.11470070927586014]
  In this paper we analyze and implement the nonparametric quantum neural networks (LNR-QNN), non regression quantum neural networks (PNR-QNN) The implementation is constructed through the Gauss-Jordan Elimination neural networks (GJE-QNN)
  arXiv  Detail & Related papers  (2020-02-07T14:44:45Z)

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.