Residual-Concatenate Neural Network with Deep Regularization Layers for Binary Classification

• URL: http://arxiv.org/abs/2205.12775v1
• Date: Wed, 25 May 2022 13:42:35 GMT
• Title: Residual-Concatenate Neural Network with Deep Regularization Layers for Binary Classification
• Authors: Abhishek Gupta, Sruthi Nair, Raunak Joshi, Vidya Chitre
• Abstract summary: We train a deep neural network that uses many regularization layers with residual and concatenation process for best fit with Polycystic Ovary Syndrome Diagnosis prognostication. The network was built with improvements from every step of failure to meet the needs of the data and achieves an accuracy of 99.3% seamlessly.
• Score: 3.1871776847712523
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Many complex Deep Learning models are used with different variations for various prognostication tasks. The higher learning parameters not necessarily ensure great accuracy. This can be solved by considering changes in very deep models with many regularization based techniques. In this paper we train a deep neural network that uses many regularization layers with residual and concatenation process for best fit with Polycystic Ovary Syndrome Diagnosis prognostication. The network was built with improvements from every step of failure to meet the needs of the data and achieves an accuracy of 99.3% seamlessly.

Related papers

• Spectrum-Informed Multistage Neural Networks: Multiscale Function Approximators of Machine Precision [1.2663244405597374]
  We propose using the novel multistage neural network approach to learn the residue from the previous stage. We successfully tackle the spectral bias of neural networks. This approach allows the neural network to fit target functions to double floating-point machine precision.
  arXiv  Detail & Related papers  (2024-07-24T12:11:09Z)
• Automated Heterogeneous Low-Bit Quantization of Multi-Model Deep Learning Inference Pipeline [2.9342849999747624]
  Multiple Deep Neural Networks (DNNs) integrated into single Deep Learning (DL) inference pipelines pose challenges for edge deployment. This paper introduces an automated heterogeneous quantization approach for DL inference pipelines with multiple DNNs.
  arXiv  Detail & Related papers  (2023-11-10T05:02:20Z)
• Precision Machine Learning [5.15188009671301]
  We compare various function approximation methods and study how they scale with increasing parameters and data. We find that neural networks can often outperform classical approximation methods on high-dimensional examples. We develop training tricks which enable us to train neural networks to extremely low loss, close to the limits allowed by numerical precision.
  arXiv  Detail & Related papers  (2022-10-24T17:58:30Z)
• Physically constrained neural networks to solve the inverse problem for neuron models [0.29005223064604074]
  Systems biology and systems neurophysiology are powerful tools for a number of key applications in the biomedical sciences. Recent developments in the field of deep neural networks have demonstrated the possibility of formulating nonlinear, universal approximators.
  arXiv  Detail & Related papers  (2022-09-24T12:51:15Z)
• Multivariate Anomaly Detection based on Prediction Intervals Constructed using Deep Learning [0.0]
  We benchmark our approach against the oft-preferred well-established statistical models. We focus on three deep learning architectures, namely, cascaded neural networks, reservoir computing and long short-term memory recurrent neural networks.
  arXiv  Detail & Related papers  (2021-10-07T12:34:31Z)
• Differentially private training of neural networks with Langevin dynamics forcalibrated predictive uncertainty [58.730520380312676]
  We show that differentially private gradient descent (DP-SGD) can yield poorly calibrated, overconfident deep learning models. This represents a serious issue for safety-critical applications, e.g. in medical diagnosis.
  arXiv  Detail & Related papers  (2021-07-09T08:14:45Z)
• Non-Gradient Manifold Neural Network [79.44066256794187]
  Deep neural network (DNN) generally takes thousands of iterations to optimize via gradient descent. We propose a novel manifold neural network based on non-gradient optimization.
  arXiv  Detail & Related papers  (2021-06-15T06:39:13Z)
• All at Once Network Quantization via Collaborative Knowledge Transfer [56.95849086170461]
  We develop a novel collaborative knowledge transfer approach for efficiently training the all-at-once quantization network. Specifically, we propose an adaptive selection strategy to choose a high-precision enquoteteacher for transferring knowledge to the low-precision student. To effectively transfer knowledge, we develop a dynamic block swapping method by randomly replacing the blocks in the lower-precision student network with the corresponding blocks in the higher-precision teacher network.
  arXiv  Detail & Related papers  (2021-03-02T03:09:03Z)
• 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)
• 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)
• MS-Net: Multi-Site Network for Improving Prostate Segmentation with Heterogeneous MRI Data [75.73881040581767]
  We propose a novel multi-site network (MS-Net) for improving prostate segmentation by learning robust representations. Our MS-Net improves the performance across all datasets consistently, and outperforms state-of-the-art methods for multi-site learning.
  arXiv  Detail & Related papers  (2020-02-09T14:11:50Z)

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.