1.概括这篇文章主要有三处贡献: ·提出双自编码器(dual autoencoder)网络,生成可辨别且更具鲁棒性的隐含表示,并且通过互信息估计和不同的重构结果进行训练。·提出了将联合训练框架,并通过深度谱聚类将隐含…
In this paper, we propose an attribute network representation learning method for dual-channel autoencoder. One channel is for the network structure, and adopting the multi-hop attention mechanism is used to capture the node's high-order neighborhood information and calculate the neighborhood w...
The proposed dual-channel autoencoder network architecture is illustrated in Fig. 3. Both the global encoder and the key region encoder in the network employ the same structure of 3D-CNN, where each layer consists of a convolutional layer, a Batch Normalization layer, and a Leaky ReLU activatio...
Through a large number of unannotated samples training, the two convolutional autoencoders in our network can reconstruct the infrared-visible image patches respectively to improve feature representation ability. In the high-level convolution layers, the cross-domain features are extracted by sharing ...
VideoMAE V2: Scaling Video Masked Autoencoders with Dual Masking Limin Wang1,2,* Bingkun Huang1,2,* Zhiyu Zhao1,2 Zhan Tong1 Yinan He2 Yi Wang2 Yali Wang3,2 Yu Qiao2,3 1 State Key Laboratory for Novel Software Technology, Nanjing University, China 2 Shang...
7、Deep Learning for Solar Power Forecasting – An Approach Using Autoencoder and LSTM Neural Networks 深度学习太阳能预测- 这些算法的组合显示了他们的预测强度与标准MLP和物理预测模型相比在预测21个太阳能发电厂的能源产量。 •为a创建数值天气预报(NWP)某些时间范围和使用物理的某个位置天气预报模型。 •...
22 proposed an adaptive variational autoencoder generative adversarial network for data augmentation and applied it to fault diagnosis. They constructed a new adaptive network to extract key features, designed an adaptive loss calculation method to achieve the interaction between model loss and function ...
LSTM-autoencoder with attentions for multivariate time seriesThis repository contains an autoencoder for multivariate time series forecasting. It features two attention mechanisms described in A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction and was inspired by Seanny123's ...
a dual-modality encoder-decoder (DM-ED) model is proposed. DM-ED model employs encoder-decoder (ED) framework to enhance multi-steps prediction performance, and LSTM is embedded in the encoder and decoder layers, respectively, thus capturing high time-dependence and non-linearity of rainfall and...
32 introduced a Graph-CNN framework employing a graph-autoencoder to learn fixed-size representations of protein pockets from representative druggable protein binding sites. Zhu et al. proposed DataDTA33, which predicts pockets from protein 3D structures and extracts their descriptors as partial input ...