The Study of Architecture MLP with Linear Neurons in Order to Eliminate the "vanishing Gradient" Problem

No abstract is available for this paper.

• Publication year

  2017

• Venue

  International Conference on Artificial Intelligence and Soft Computing

• Publication date

  2017-06-11

• Fields of study

  Mathematics, Computer Science

• Identifiers
  DOI 10.1007/978-3-319-59063-9_9
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• Estimation of deep neural networks capabilities based on a trigonometric approach

  2016cited by this paper
• Big data and deep learning

  2016cited by this paper
• Estimation of Deep Neural Networks Capabilities Using Polynomial Approach

  2016cited by this paper
• Mastering the game of Go with deep neural networks and tree search

  2016cited by this paper
• Learning architectures with enhanced capabilities and easier training

  2015cited by this paper
• Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification

  2015cited by this paper
• Dedicated deep neural network architectures and methods for their training

  2015cited by this paper
• Highway Networks

  2015cited by this paper
• Human-level control through deep reinforcement learning

  2015cited by this paper
• Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

  2015cited by this paper
• Very Deep Convolutional Networks for Large-Scale Image Recognition

  2014cited by this paper
• Convolutional neural networks at constrained time cost

  2014cited by this paper
• Utilizing Dual Neural Networks as a Tool for Training, Optimization, and Architecture Conversion

  2013cited by this paper
• Selection of Proper Neural Network Sizes and Architectures—A Comparative Study

  2012cited by this paper
• ImageNet classification with deep convolutional neural networks

  2012cited by this paper
• Neural Network Learning Without Backpropagation

  2010cited by this paper
• Understanding the difficulty of training deep feedforward neural networks

  2010cited by this paper
• Improved Computation for Levenberg–Marquardt Training

  2010cited by this paper
• Exploring Strategies for Training Deep Neural Networks

  2009cited by this paper
• The Vanishing Gradient Problem During Learning Recurrent Neural Nets and Problem Solutions

  1998cited by this paper
• Learning long-term dependencies with gradient descent is difficult

  1994cited by this paper

• Multi-Step Ahead Water Level Forecasting Using Deep Neural Networks

  2024cites this paper
• Partitioned neural network training via synthetic intermediate labels

  2024cites this paper
• Testing Outlier Detection Algorithms for Identifying Early Stage Solute Clusters in Atom Probe Tomography.

  2024cites this paper
• Application of Self-Supervised Learning to MICA Model for Reconstructing Imperfect 3D Facial Structures

  2023cites this paper
• Establishment of machine learning hyperparameters for predicting the extensional properties of noodles from the thermo-mechanical properties of wheat flour

  2022cites this paper
• A Comparative Study on Regularization Techniques in Convolutional Neural Networks

  2022cites this paper
• IAES International Journal of Artificial Intelligence (IJ-AI)

  2022cites this paper
• Resource Recommendation Based on Industrial Knowledge Graph in Low-Resource Conditions

  2022cites this paper
• Speaker Adversarial Neural Network (SANN) for Speaker-independent Speech Emotion Recognition

  2022cites this paper
• Deep neural networks for spatiotemporal PM2.5 forecasts based on atmospheric chemical transport model output and monitoring data.

  2022cites this paper
• Attention-based advantage actor-critic algorithm with prioritized experience replay for complex 2-D robotic motion planning

  2022cites this paper
• Predicting chaotic time series and replicating chaotic attractors based on two novel echo state network models

  2022cites this paper
• Cryptocurrency Price Estimation Using Hyperparameterized Oscillatory Activation Functions in LSTM Networks

  2022cites this paper
• Modelling on Car‐Sharing Serial Prediction Based on Machine Learning and Deep Learning

  2022cites this paper
• Feature Mining and Classification of Microarray Data Using Modified ResNet-SVM Architecture

  2021cites this paper
• Kernelized Linear Autoencoder

  2021cites this paper
• A Novel Matrix Completion Model Based on the Multi-Layer Perceptron Integrating Kernel Regularization

  2021cites this paper
• Two Dimensional Convolutional Neural Network Approach for Real-Time Bangla Sign Language Characters Recognition and Translation

  2021influential citation
• Deep Neural Network for Electroencephalogram based Emotion Recognition

  2021influential citation
• A Greedy Iterative Layered Framework for Training Feed Forward Neural Networks

  2020cites this paper
• A Predictive Dispatching Rule Assisted by Multi-Layer Perceptron for Scheduling Wafer Fabrication Lines

  2020cites this paper
• TLSurv: Integrating Multi-Omics Data by Multi-Stage Transfer Learning for Cancer Survival Prediction

  2020cites this paper
• Smart Attendance Management System Based on Face Recognition Using CNN

  2020cites this paper
• Customized Inverse Design of Metamaterial Absorber Based on Target-Driven Deep Learning Method

  2020cites this paper
• EMSGD: An Improved Learning Algorithm of Neural Networks With Imbalanced Data

  2020cites this paper
• Error Back Propagation Algorithm with Adaptive Learning Rate

  2019cites this paper
• Approximation-based Estimation of Learning Rate for Error Back Propagation Algorithm

  2019cites this paper
• Exploiting deep convolutional neural networks for a neural-based learning classifier system

  2019cites this paper
• Radar emitter identification based on EPSD-DFN

  2018cites this paper
• Neural Networks Saturation Reduction

  2018cites this paper