WebThese deep learning algorithms are commonly used for ordinal or temporal problems, such as language translation, natural language processing (nlp), speech recognition, and image captioning; they are incorporated into popular applications such as Siri, voice search, and Google Translate. We have presented a depth-gated RNN architecture. In particular, we have extended LSTM to use the depth gate that modulates a linear dependence of the memory cells in the upper and lower layer recurrent units. We observed better performances using this new model on a machine translation experiment and a language modeling task. 4

(PDF) Depth-Gated Recurrent Neural Networks

WebAug 27, 2015 · In standard RNNs, this repeating module will have a very simple structure, such as a single tanh layer. The repeating module in a standard RNN contains a single layer. LSTMs also have this chain like … WebSep 13, 2024 · RNNs are trained via what’s known as backpropagation through time which is an extension over normal backpropagation that handles the recurrent layer. … cdc45 pathway

Depth-Gated LSTM - arXiv

WebFeb 13, 2024 · The interpretability of deep learning models has raised extended attention these years. It will be beneficial if we can learn an interpretable structure from deep learning models. In this article,... WebThe interpretability of deep learning models has raised extended attention these years. It will be beneficial if we can learn an interpretable structure from deep learning models. In this article, we focus on recurrent neural networks (RNNs), especially gated RNNs whose inner mechanism is still not clearly understood. WebDec 28, 2024 · As shown in Figure 5, compared with other LSTM variants (Gated Recurrent Units (GRUs), Depth Gated RNNs, & Clockwork RNNs), ELU has more stable performance and effectively reduces time consumption. ELSTM has been simplified by the gate structure, reducing the amount of calculation and greatly shortening the convergence time. ... cdc42 gtp antibody