This survey investigates current techniques for representing qualitative data for use as input to neural networks. Techniques for using qualitative data in neural networks are well known. However, researchers continue to discover new variations or entire
As planned, the neural network accepts data for the last 50 days as input and predicts the course for the next 5 days. To do this, I read the .csv file, processed the data in such a way that after the transformation I got two dataframes, the first one is responsible for the input ...
Gist面向数据压缩,发掘训练模式以及各个层数据的特征,对特定数据进行不同方案的压缩,从而达到节省空间的目的。 不同类别数据的分布 如上图,DNN中数据的分布大部分是Feature map占主导,所以Gist主要处理该数据。 不同类型Layer Tensor在DNN中的空间占比 上图给出了Relu后是Pool(ReLU outputs followed by a Pool)这种...
High-dimensional data can be converted to low-dimensional codes by training a multilayer neural network with a small central layer to reconstruct high-dimensional input vectors. Gradient descent can be used for fine-tuning the weights in such "autoencoder" networks, but this works well only if t...
Reducing the Dimensionality of Data with Neural Networks G.E. Hinton and R.R. Salakhutdinov 摘要 训练一个带有很小的中间层的多层神经网络,可以重构高维空间的输入向量,实现从高维数据到低维编码的效果。(原文为high-dimensional data can be converted to low-dimensional codes)在这样的Autoencoder network中,...
Neural Networks for Complex Data. Marie Cottrell,Madalina Olteanu,Fabrice Rossi,Joseph Rynkiewicz,Nathalie Villa-Vialaneix. Kiinstliche Intelligenz . 2012M. Cottrell, M. Olteanu, F. Rossi, J. Rynkiewicz, and N. Villa-Vialaneix. Neural networks for complex data. KI - Ku¨nstliche Intelligenz, ...
High-dimensional data can be converted to low-dimensional codes by training a multilayer neural network with a small central layer to reconstruct high-dimensional input vectors. Gradient descent can be used for fine-tuning the weights in such ‘‘autoencoder’’ networks, but this works well only...
When using neural networks to train a large number of data for classification, there generally exists a learning complexity problem. In this paper, a new geometrical interpretation of McCulloch-Pitts (M–P) neural model is presented. Based on the interpretation, a new constructive learning approach...
Deep neural networks for data association in particle trackingieeexplore.ieee.org/document/8363615 摘要 使用活细胞延时显微成像了解细胞内动力学过程的第一步是提取图像中所有相关粒子的精确轨迹。这项任务的一个关键方面是跨时间帧在粒子检测之间建立精确的关联。
New life for neural networks. With the help of neural networks, data sets with many dimensions can be analyzed to find lower dimensional structures within them. Cottrell,W G. - 《Science》 被引量: 39发表: 2006年 Comparison of neural networks and regression analysis: A new insight In recent...