6) Neural network self-structure learning algorithm 神经网络自构形学习算法 1. Neural network self-structure learning algorithmis applied in the rule-reasoning layer of the fuzzy-neural network to optimize rules, and the algorithm is expanded in the layers under the rule-reasoning layer of the fuz...
Self-learning for neural network arrays. In an exemplary embodiment, a method includes determining input voltages to be applied to one or more input neurons of a neural network, and determining target output voltages to be obtained at one or more output neurons of the neural network in response...
But the form of our overall/final classifier is clearly just awhole big neural network. So, having trained up an initial set of parameters for our model (training the first layer using an autoencoder, and the second layer via logistic/softmax regression), we canfurther modify all the parame...
Netron - Visualizer for neural network and machine learning models. (Source Code) MIT Python/Nodejs Offen - Fair, lightweight and open web analytics tool. Gain insights while your users have full access to their data. (Demo, Source Code) Apache-2.0 Go/Docker Open Web Analytics - Web analyt...
但是Representation Learning 在乎的并不是整个Data Distribution; 而是怎么得到更抽象、High-level 的表示法。End-to-End Training 让人们发现,Deep Neural Network 是有能力解构数据的 Hierarchical Internal Representation 的,何不利用这种强大的能力呢? 也就是Learn the dataset, not the data points. ...
关键词:self-supervised, graph neural network, EEG 论文:Self-Supervised Graph Neural Networks for Improved Electroencephalographic Seizure Analysis 代码:https://github.com/tsy935/eeg-gnn-ssl 一句话总结 作者提出两种将EEG表示为图结构的方法,并采用一篇ICLR2018的DCGRU模型对该图结构数据进行时空相关性建...
2.2 Self-supervised Learning Training a network on an auxiliary objective, mutual information ...
Furthermore, a novel self-learning method based on restricted Boltzmann machine (RBM) and probabilistic neural network (PNN) is proposed for fault identification. The benefits of the proposed RSVDD and RBM-PNN scheme are illustrated by Tennessee Eastman benchmark. 展开 ...
A neural network model for a mechanism of visual pattern recognition is proposed in this paper. The network is self-organized by learning without a teacher, and acquires an ability to recognize stimulus patterns based on the geometrical similarity (Gestalt) of their shapes without affected by their...
3.3 Properties of the Discrete-Time Q-Learning Algorithm 52 3.3.1 Non-Discount Case 52 3.3.2 Discount Case 59 3.4 Neural Network Implementation for the Discrete-Time Q-Learning Algorithm 64 3.4.1 The Action Network 65 3.4.2 The Critic Network 67 ...