Many applications of deep learning use feedforward neural network architectures (Fig. 1), which learn to map a fixed-size input (for example, an image) to a fixed-size output (for example, a probability for each of several categories). To go from one layer to the next, a set of units...
5.1. Primitive Operations 基础的操作主要分为4种:self-attention (SA), guided-attention (GA), feed-forward network (FFN), and relation self-attention (RSA)。 这几种操作的核心是scaled dot-product attention,计算方式如下: 对于$Q \in \mathbb{R}^{m \times d},K \in \mathbb{R}^{n \times ...
Code for the paper entitled "ForecastNet: A Time-Variant Deep Feed-Forward Neural Network Architecture for Multi-Step-Ahead Time-Series Forecasting" - jjdabr/forecastNet
Many applications of deep learning use feedforward neural network architectures (Fig. 1), which learn to map a fixed-size input (for example, an image) to a fixed-size output (for example, a prob-ability for each of several categories). To go from one layer to the next, a set of un...
Network architecture and training We use VAMPnets to learn molecular kinetics from simulation data of a range of model systems. While any neural network architecture can be employed inside the VAMPnet lobes, we chose the following setup for our applications: the two network lobes are identical clo...
The formulation of F(x) + x can be realized by feedforward neural networks with “shortcut connections” (Fig. 2). Shortcut connections [2, 33, 48] are those skipping one or more layers. In our case, the shortcut connections simply perform identity mapping, and their outputs are added...
(a) Feedforward neural network architecture to estimate the inverse model for meta structure. Diffraction profile of the meta surface is input to the network. We use a 4-layer architecture with decreasing number of units. The output of the network is 8 design parameters of the meta structure....
Feedforward Neural Networks 前馈神经网络 G Gaussian Mixture Model(GMM)高斯混合模型 Genetic Algorithm(GA)遗传算法 Generalization 泛化 Generative Adversarial Networks(GAN)生成对抗网络 Generative Model 生成模型 Generator 生成器 Global Optimization 全局优化 ...
残差网络(Residual Network)。在上述普通网络的基础上,我们插入捷径连接(图3,右),将网络转换为其对应的残差版本。当输入和输出的维度相同时,可以直接使用恒等捷径(公式(1))(图3中的实线捷径)。当维度增加时(图3中的虚线捷径),我们考虑两个选项:(A)捷径仍然执行恒等映射,并为增加维度填充额外的零个条目...
feedforward neural network:前馈神经网络 down-sampled:下采样 rescaled:重新调整图像大小 nonlinearity:非线性单元 gradient descent:梯度下降 stochastic gradient descent(SGD):随机梯度下降法 Rectified Linear Units(ReLUs):修正线性单元 iteration:迭代 cross-GPU parallelization:跨CPU并行化操作 local response normalizati...