A layer-stress learning framework universally augments deep neural network tasks

• URL: http://arxiv.org/abs/2111.08597v1
• Date: Sun, 14 Nov 2021 15:14:13 GMT
• Title: A layer-stress learning framework universally augments deep neural network tasks
• Authors: Shihao Shao, Yong Liu, Qinghua Cui
• Abstract summary: We present a layer-stress deep learning framework (x-NN) which implemented automatic and wise depth decision on shallow or deep feature map in a deep network. x-NN showed outstanding prediction ability in the Alzheimer's Disease Classification Technique Challenge PRCV 2021, in which it won the top laurel and outperformed all other AI models.
• Score: 6.2067442999727644
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep neural networks (DNN) such as Multi-Layer Perception (MLP) and Convolutional Neural Networks (CNN) represent one of the most established deep learning algorithms. Given the tremendous effects of the number of hidden layers on network architecture and performance, it is very important to choose the number of hidden layers but still a serious challenge. More importantly, the current network architectures can only process the information from the last layer of the feature extractor, which greatly limited us to further improve its performance. Here we presented a layer-stress deep learning framework (x-NN) which implemented automatic and wise depth decision on shallow or deep feature map in a deep network through firstly designing enough number of layers and then trading off them by Multi-Head Attention Block. The x-NN can make use of features from various depth layers through attention allocation and then help to make final decision as well. As a result, x-NN showed outstanding prediction ability in the Alzheimer's Disease Classification Technique Challenge PRCV 2021, in which it won the top laurel and outperformed all other AI models. Moreover, the performance of x-NN was verified by one more AD neuroimaging dataset and other AI tasks.

Related papers

• 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)
• Towards Scalable and Versatile Weight Space Learning [51.78426981947659]
  This paper introduces the SANE approach to weight-space learning. Our method extends the idea of hyper-representations towards sequential processing of subsets of neural network weights.
  arXiv  Detail & Related papers  (2024-06-14T13:12:07Z)
• Deeper or Wider: A Perspective from Optimal Generalization Error with Sobolev Loss [2.07180164747172]
  We compare deeper neural networks (DeNNs) with a flexible number of layers and wider neural networks (WeNNs) with limited hidden layers. We find that a higher number of parameters tends to favor WeNNs, while an increased number of sample points and greater regularity in the loss function lean towards the adoption of DeNNs.
  arXiv  Detail & Related papers  (2024-01-31T20:10:10Z)
• Unveiling the Unseen: Identifiable Clusters in Trained Depthwise Convolutional Kernels [56.69755544814834]
  Recent advances in depthwise-separable convolutional neural networks (DS-CNNs) have led to novel architectures. This paper reveals another striking property of DS-CNN architectures: discernible and explainable patterns emerge in their trained depthwise convolutional kernels in all layers.
  arXiv  Detail & Related papers  (2024-01-25T19:05:53Z)
• Fully Spiking Actor Network with Intra-layer Connections for Reinforcement Learning [51.386945803485084]
  We focus on the task where the agent needs to learn multi-dimensional deterministic policies to control. Most existing spike-based RL methods take the firing rate as the output of SNNs, and convert it to represent continuous action space (i.e., the deterministic policy) through a fully-connected layer. To develop a fully spiking actor network without any floating-point matrix operations, we draw inspiration from the non-spiking interneurons found in insects.
  arXiv  Detail & Related papers  (2024-01-09T07:31:34Z)
• From Alexnet to Transformers: Measuring the Non-linearity of Deep Neural Networks with Affine Optimal Transport [32.39176908225668]
  We introduce the concept of the non-linearity signature of DNN, the first theoretically sound solution for measuring the non-linearity of deep neural networks. We provide extensive experimental results that highlight the practical usefulness of the proposed non-linearity signature.
  arXiv  Detail & Related papers  (2023-10-17T17:50:22Z)
• Models Developed for Spiking Neural Networks [0.5801044612920815]
  Spiking neural networks (SNNs) have been around for a long time, and they have been investigated to understand the dynamics of the brain. In this work, we reviewed the structures and performances of SNNs on image classification tasks. The comparisons illustrate that these networks show great capabilities for more complicated problems.
  arXiv  Detail & Related papers  (2022-12-08T16:18:53Z)
• RDRN: Recursively Defined Residual Network for Image Super-Resolution [58.64907136562178]
  Deep convolutional neural networks (CNNs) have obtained remarkable performance in single image super-resolution. We propose a novel network architecture which utilizes attention blocks efficiently.
  arXiv  Detail & Related papers  (2022-11-17T11:06:29Z)
• Deep Reinforcement Learning Guided Graph Neural Networks for Brain Network Analysis [61.53545734991802]
  We propose a novel brain network representation framework, namely BN-GNN, which searches for the optimal GNN architecture for each brain network. Our proposed BN-GNN improves the performance of traditional GNNs on different brain network analysis tasks.
  arXiv  Detail & Related papers  (2022-03-18T07:05:27Z)
• Locality Guided Neural Networks for Explainable Artificial Intelligence [12.435539489388708]
  We propose a novel algorithm for back propagation, called Locality Guided Neural Network(LGNN) LGNN preserves locality between neighbouring neurons within each layer of a deep network. In our experiments, we train various VGG and Wide ResNet (WRN) networks for image classification on CIFAR100.
  arXiv  Detail & Related papers  (2020-07-12T23:45:51Z)
• Progressive Tandem Learning for Pattern Recognition with Deep Spiking Neural Networks [80.15411508088522]
  Spiking neural networks (SNNs) have shown advantages over traditional artificial neural networks (ANNs) for low latency and high computational efficiency. We propose a novel ANN-to-SNN conversion and layer-wise learning framework for rapid and efficient pattern recognition.
  arXiv  Detail & Related papers  (2020-07-02T15:38:44Z)

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.