Rubik's Cube Operator: A Plug And Play Permutation Module for Better Arranging High Dimensional Industrial Data in Deep Convolutional Processes

• URL: http://arxiv.org/abs/2203.12921v1
• Date: Thu, 24 Mar 2022 08:13:56 GMT
• Title: Rubik's Cube Operator: A Plug And Play Permutation Module for Better Arranging High Dimensional Industrial Data in Deep Convolutional Processes
• Authors: Luoxiao Yang, Zhong Zheng, and Zijun Zhang
• Abstract summary: convolutional neural network (CNN) has been widely applied to process the industrial data based input. Unlike images, information in the industrial data based system is not necessarily spatially ordered. We propose a Rubik's Cube Operator (RCO) to adaptively permutate the data organization of the industrial data.
• Score: 6.467208324670583
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The convolutional neural network (CNN) has been widely applied to process the industrial data based tensor input, which integrates data records of distributed industrial systems from the spatial, temporal, and system dynamics aspects. However, unlike images, information in the industrial data based tensor is not necessarily spatially ordered. Thus, directly applying CNN is ineffective. To tackle such issue, we propose a plug and play module, the Rubik's Cube Operator (RCO), to adaptively permutate the data organization of the industrial data based tensor to an optimal or suboptimal order of attributes before being processed by CNNs, which can be updated with subsequent CNNs together via the gradient-based optimizer. The proposed RCO maintains K binary and right stochastic permutation matrices to permutate attributes of K axes of the input industrial data based tensor. A novel learning process is proposed to enable learning permutation matrices from data, where the Gumbel-Softmax is employed to reparameterize elements of permutation matrices, and the soft regularization loss is proposed and added to the task-specific loss to ensure the feature diversity of the permuted data. We verify the effectiveness of the proposed RCO via considering two representative learning tasks processing industrial data via CNNs, the wind power prediction (WPP) and the wind speed prediction (WSP) from the renewable energy domain. Computational experiments are conducted based on four datasets collected from different wind farms and the results demonstrate that the proposed RCO can improve the performance of CNN based networks significantly.

Related papers

• Achieving Rotation Invariance in Convolution Operations: Shifting from Data-Driven to Mechanism-Assured [18.910817148765176]
  This paper designs a set of new convolution operations that are natually invariant to arbitrary rotations. We compare their performance with previous rotation-invariant convolutional neural networks (RI-CNNs) The results show that RIConvs significantly improve the accuracy of these CNN backbones, especially when the training data is limited.
  arXiv  Detail & Related papers  (2024-04-17T12:21:57Z)
• Dynamic Semantic Compression for CNN Inference in Multi-access Edge Computing: A Graph Reinforcement Learning-based Autoencoder [82.8833476520429]
  We propose a novel semantic compression method, autoencoder-based CNN architecture (AECNN) for effective semantic extraction and compression in partial offloading. In the semantic encoder, we introduce a feature compression module based on the channel attention mechanism in CNNs, to compress intermediate data by selecting the most informative features. In the semantic decoder, we design a lightweight decoder to reconstruct the intermediate data through learning from the received compressed data to improve accuracy.
  arXiv  Detail & Related papers  (2024-01-19T15:19:47Z)
• Attention-based Feature Compression for CNN Inference Offloading in Edge Computing [93.67044879636093]
  This paper studies the computational offloading of CNN inference in device-edge co-inference systems. We propose a novel autoencoder-based CNN architecture (AECNN) for effective feature extraction at end-device. Experiments show that AECNN can compress the intermediate data by more than 256x with only about 4% accuracy loss.
  arXiv  Detail & Related papers  (2022-11-24T18:10:01Z)
• Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximations [76.82124752950148]
  We develop a convenient gradient-based method for selecting the data augmentation. We use a differentiable Kronecker-factored Laplace approximation to the marginal likelihood as our objective.
  arXiv  Detail & Related papers  (2022-02-22T02:51:11Z)
• Confusion-based rank similarity filters for computationally-efficient machine learning on high dimensional data [0.0]
  We introduce a novel type of computationally efficient artificial neural network (ANN) called the rank similarity filter (RSF) RSFs can be used to transform and classify nonlinearly separable datasets with many data points and dimensions. Open-source code for RST, RSC and RSPC was written in Python using the popular scikit-learn framework to make it easily accessible.
  arXiv  Detail & Related papers  (2021-09-28T10:53:38Z)
• Rank-R FNN: A Tensor-Based Learning Model for High-Order Data Classification [69.26747803963907]
  Rank-R Feedforward Neural Network (FNN) is a tensor-based nonlinear learning model that imposes Canonical/Polyadic decomposition on its parameters. First, it handles inputs as multilinear arrays, bypassing the need for vectorization, and can thus fully exploit the structural information along every data dimension. We establish the universal approximation and learnability properties of Rank-R FNN, and we validate its performance on real-world hyperspectral datasets.
  arXiv  Detail & Related papers  (2021-04-11T16:37:32Z)
• Transfer Learning with Convolutional Networks for Atmospheric Parameter Retrieval [14.131127382785973]
  The Infrared Atmospheric Sounding Interferometer (IASI) on board the MetOp satellite series provides important measurements for Numerical Weather Prediction (NWP) Retrieving accurate atmospheric parameters from the raw data provided by IASI is a large challenge, but necessary in order to use the data in NWP models. We show how features extracted from the IASI data by a CNN trained to predict a physical variable can be used as inputs to another statistical method designed to predict a different physical variable at low altitude.
  arXiv  Detail & Related papers  (2020-12-09T09:28:42Z)
• Dataset Meta-Learning from Kernel Ridge-Regression [18.253682891579402]
  Kernel Inducing Points (KIP) can compress datasets by one or two orders of magnitude. KIP-learned datasets are transferable to the training of finite-width neural networks even beyond the lazy-training regime.
  arXiv  Detail & Related papers  (2020-10-30T18:54:04Z)
• Curriculum By Smoothing [52.08553521577014]
  Convolutional Neural Networks (CNNs) have shown impressive performance in computer vision tasks such as image classification, detection, and segmentation. We propose an elegant curriculum based scheme that smoothes the feature embedding of a CNN using anti-aliasing or low-pass filters. As the amount of information in the feature maps increases during training, the network is able to progressively learn better representations of the data.
  arXiv  Detail & Related papers  (2020-03-03T07:27:44Z)
• Computational optimization of convolutional neural networks using separated filters architecture [69.73393478582027]
  We consider a convolutional neural network transformation that reduces computation complexity and thus speedups neural network processing. Use of convolutional neural networks (CNN) is the standard approach to image recognition despite the fact they can be too computationally demanding.
  arXiv  Detail & Related papers  (2020-02-18T17:42:13Z)
• A Multi-Scale Tensor Network Architecture for Classification and Regression [0.0]
  We present an algorithm for supervised learning using tensor networks. We employ a step of preprocessing the data by coarse-graining through a sequence of wavelet transformations. We show how fine-graining through the network may be used to initialize models with access to finer-scale features.
  arXiv  Detail & Related papers  (2020-01-22T21:26:28Z)

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.