First, the GARCH model is employed to estimate the model parameters. After that, the ARCH effect test is used to test the residuals obtained from the model. Any untrained heteroscedasticity element must be found using this step. The hypothesis of the ARCH test yielded...
ModelDREAM uses RNN to capture sequential information of users' shopping behavior. It extracts users' dynamic representations and scores user-item pair by calculating inner products between users' dynamic representations and items' embedding.Refer to the following paper:...
Time-Series ForecastingThe paper presents a study of deep learning-based models for forecasting future directions of car sales, and car model preferences. An open-source Kaggle multivSaxena, PreetiBahad, PritikaKamal, RajSocial Science Electronic Publishing...
到现在为止我们已经完成了数据预处理、batching、词向量层和Softmax层,我们将这些组件与RNN结合起来,就可以搭建一个完整的神经网络语言模型,下面的代码给出了一个完整的训练程序,采用双层LSTM作为RNN的主体,并共享了Softmax层和Embedding层的参数。完整代码请见TensorFlowExamples/Chapter9/language_model.ipynb。 # coding...
(SAR) measurements which can be provided on a fixed time step can alleviate the current problem. Taking into consideration the ability of Deep Learning architectures to track temporal patterns and identify correlations between optical and SAR data, we apply a CNN-RNN based model that exports...
4.2 Attention-based RNN model 模型结构图如下所示: 在这个模型中,没有使用Encoder-Decoder模式,而是直接使用BiRNN来对原始文本数据进行编码,提取出序列标签数据,同时通过注意力机制获取的上下文变量ci也在RNN中进行融合训练,增强数据的关联性。 为啥要用Attention机制呢?
(outputs, self.W) + self.b#model : [batch_size, n_class]53returnmodel5455input_batch, target_batch =make_batch(sentence)5657model =BiLSTM()5859criterion =nn.CrossEntropyLoss()60optimizer = optim.Adam(model.parameters(), lr=0.001)6162#Training63forepochinrange(10000):64output =model(input...
LSTM-RNN model-based air pollutant concentration forecast methodThe invention relates to an LSTM (Long Short-Term Memory)-RNN model-based air pollutant concentration forecast method. The method comprises the steps of monitoring and collecting air pollutant concentration data in a relatively long time;...
In this work we devise a recurrent neural network-based GRN model to control robots. To simulate the regulatory effects and make our model inferable from time-series data, we develop an enhanced learning algorithm, coupled with some heuristic techniques of data processing for performance improvement...
Specifically, humans always firstly determine one vehicle's coarse-grained category, i.e., the car model/type. Then, under the branch of the predicted car model/type, they are going to identify specific vehicles by relying on subtle visual cues, e.g., customized paintings and windshield ...