Neural networks.Artificial intelligence.Deep neural networks have made significant impacts in many fields of computer science and engineering, especially for large-scale and high-dimensional learning problems.
By using deep neural networks as approximation functions and making profit of large computational capabilities of modern clusters of computers, all deep RL algorithms are capable of addressing much larger problems than before, and to approximate gradients with unprecedented accuracy, which makes them more...
This universal approximation theorem of operators is suggestive of the structure and potential of deep neural networks (DNNs) in learning continuous operators or complex systems from streams of scattered data. Here, we thus extend this theorem to DNNs. We design a new network with small ...
In this talk, we shall discuss mathematical theory behind this new approach and approximation rate of deep network; we will also show how this new approach differs from the classic approximation theory, and how this new theory...
Deep neural networks (DNNs) have been successfully utilized in many scientific problems for their high prediction accuracy, but their application to genetic studies remains challenging due to their poor interpretability. Here we consider the problem of s
A Deep Neural Network’s Loss Surface Contains Every Low-dimensional Pattern 概 作者关于Loss Surface的情况做了一个理论分析, 即证明足够大的神经网络能够逼近所有的低维损失patterns. 相关工作 loss l
An example of a deep neural network. The input layer, the kth layer of the deep neural network, and the output layer are presented in the figure Full size image As for networks with one hidden layer, they are also universal approximators. However, the approximation theory for deep networks ...
This creates an overall flatter image, which however maintains a trace of the C4 texture, which is picked up by the neural network. Figure 4 show examples (in panels (a–f)) where a small quantity of C4 was concealed. The small size, alongside the fact that a single energy attenuation ...
Active researches are currently being performed to incorporate the wealth of scientific knowledge into data-driven approaches (e.g., neural networks) in order to improve the latter’s effectiveness. In this study, the Theory-guided Neural Network (TgNN) is proposed for deep learning of subsurface...
Autoencoder is trying to construct the functionh(x)=x. In other words it is trying to find an approximation of a function ensuring that a neural network feedback is approximately equal to the values of input parameters. For the solution of the problem to be nontrivial, the number of neuro...