An Algorithm of Dynamically-Connected Deep Neural Network
| Autor: | Yize Tang, Shenshen Gu |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Quantitative Biology::Neurons and Cognition
Artificial neural network Computer science Network connection 05 social sciences Training time Training (meteorology) Process (computing) 010501 environmental sciences Overfitting 01 natural sciences Convolutional neural network Power (physics) 0502 economics and business 050207 economics Algorithm 0105 earth and related environmental sciences |
| Zdroj: | 2019 11th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC). |
| DOI: | 10.1109/ihmsc.2019.00040 |
| Popis: | Deep neural network is an important and effective approach to discover patterns inside data. Most existing neural networks, like deep neural network and convolutional neural network, have a fixed network connection among neurons. This will cause overfitting problems and long training time. This article discusses an algorithm to combat these problems, through creating and removing connections during training, leading to faster training and fewer symmetric problems. This enables the network to adapt itself to the proper number of neurons according to the dataset. It greatly speeds up training process, so it is useful for large datasets and deep networks. This method is simple to implement and requires less memory and computing power than existing machine learning algorithm. It also has good results in machine learning problems. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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