Gated recurrent unit

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  1. A Gated Recurrent Unit (GRU), as its name suggests, is a variant of the RNN architecture, and uses gating mechanisms to control and manage the flow of information between cells in the neural network.[1]
  2. The structure of the GRU allows it to adaptively capture dependencies from large sequences of data without discarding information from earlier parts of the sequence.[1]
  3. The ability of the GRU to hold on to long-term dependencies or memory stems from the computations within the GRU cell to produce the hidden state.[1]
  4. The GRU cell contains only two gates: the Update gate and the Reset gate.[1]
  5. As mentioned, the Gated Recurrent Units (GRU) is one of the popular variants of recurrent neural networks and has been widely used in the context of machine translation.[2]
  6. The GRU unit was introduced in 2014 and is claimed to be motivated by the Long Short-Term Memory unit.[2]
  7. According to the researchers at the University of Montreal, a gated recurrent unit (GRU) was introduced to produce each recurrent unit to capture dependencies of various time scales.[2]
  8. In GRU, two gates including a reset gate that adjusts the incorporation of new input with the previous memory and an update gate that controls the preservation of the precious memory are introduced.[2]
  9. However, as shown by Gail Weiss, Yoav Goldberg and Eran Yahav, the LSTM is "strictly stronger" than the GRU as it can easily perform unbounded counting, while the GRU cannot.[3]
  10. in 2014, GRU (Gated Recurrent Unit) aims to solve the vanishing gradient problem which comes with a standard recurrent neural network.[4]
  11. GRU can also be considered as a variation on the LSTM because both are designed similarly and, in some cases, produce equally excellent results.[4]
  12. To solve the vanishing gradient problem of a standard RNN, GRU uses, so-called, update gate and reset gate.[4]
  13. Gated Recurrent Unit (GRU) is one of the most used recurrent structures, which makes a good trade-off between performance and time spent.[5]
  14. Inspired by human reading, we introduce binary input gated recurrent unit (BIGRU), a GRU based model using a binary input gate instead of the reset gate in GRU.[5]
  15. A gated recurrent unit (GRU) is a gating mechanism in recurrent neural networks (RNN) similar to a long short-term memory (LSTM) unit but without an output gate.[6]
  16. GRU’s try to solve the vanishing gradient problem that can come with standard recurrent neural networks.[6]
  17. A GRU can be considered a variation of the long short-term memory (LSTM) unit because both have a similar design and produce equal results in some cases.[6]
  18. GRU’s are able to solve the vanishing gradient problem by using an update gate and a reset gate.[6]
  19. Gated recurrent units (GRUs) were inspired by the common gated recurrent unit, long short-term memory (LSTM), as a means of capturing temporal structure with less complex memory unit architecture.[7]
  20. As a result, it is difficult to know a priori how successful a GRU-RNN will perform on a given data set.[7]
  21. Most experiments have been carried out with a 1D CNN, GRU and PSSM profiles.[8]
  22. The flowchart for identifying electron transport proteins using 1D RNN, GRU, and PSSM profiles.[8]
  23. Therefore, in this study, we attempted to input all PSSM profiles into deep neural networks via GRU architectures.[8]
  24. Our deep learning architecture to identify electron transport protein contains 1D CNN to extract the features, and GRU to learn the features in order to build models.[8]
  25. The results demonstrate that the AE-GRU is better than other recurrent neural networks, such as Long Short-Term Memory (LSTM) and GRU.[9]
  26. attention-based bi-directional GRU model for customer feedback analysis task of English.[10]
  27. GRU's performance on certain tasks of polyphonic music modeling and speech signal modeling was found to be similar to that of LSTM.[11]
  28. A gated recurrent unit (GRU) is a successful recurrent neural network architecture for time-series data.[12]
  29. The GRU is typically trained using a gradient-based method, which is subject to the exploding gradient problem in which the gradient increases significantly.[12]
  30. This problem is caused by an abrupt change in the dynamics of the GRU due to a small variation in the parameters.[12]
  31. In this paper, we find a condition under which the dynamics of the GRU changes drastically and propose a learning method to address the exploding gradient problem.[12]
  32. Similar to LSTM blocks, the GRU also has mechanisms to enable “memorizing” information for an extended number of time steps.[13]
  33. reset_after GRU convention (whether to apply reset gate after or before matrix multiplication).[14]
  34. Why do we use LSTM and GRU?[15]
  35. Generally, both LSTM and GRU are used with the intuition to solve the vanishing gradient issue, LSTM has a complex design when compared to GRU which is much simpler.[15]
  36. As we have discussed above, GRU is faster than LSTM, apart from its speed, it is able to handle the vanishing gradient problem very well.[15]
  37. In the series of deep learning and machine learning blogs, I came up with the core concept and functioning of LSTM and GRU.[15]
  38. In this work, we propose a dual path gated recurrent unit (GRU) network (DPG) to address the SSS prediction accuracy challenge.[16]
  39. The CNN module is composed of a 1D CNN without pooling, and the RNN part is composed of two parallel but different GRU layers.[16]
  40. This study develops an Autoencoder Gated Recurrent Unit (AE-GRU) model to predict the RUL of equipment.[17]
  41. In particular, AE is used to select features, and then the correlation between sensors is found through the GRU model, and the RUL is predicted by Multi-Layer Perception (MLP) model.[17]
  42. The second part is the GRU model which is a type of RNN that can deal with time series data.[17]
  43. The GRU model finds key information in historical sensor data in combination with the MLP model.[17]
  44. The information which is stored in the Internal Cell State in an LSTM recurrent unit is incorporated into the hidden state of the Gated Recurrent Unit.[18]
  45. This collective information is passed onto the next Gated Recurrent Unit.[18]
  46. ( ): It is often overlooked during a typical discussion on Gated Recurrent Unit Network.[18]
  47. E.g., setting num_layers=2 would mean stacking two GRUs together to form a stacked GRU , with the second GRU taking in outputs of the first GRU and computing the final results.[19]
  48. If non-zero, introduces a Dropout layer on the outputs of each GRU layer except the last layer, with dropout probability equal to dropout .[19]
  49. tensor containing the output features h_t from the last layer of the GRU, for each t .[19]
  50. Definition - What does Gated Recurrent Unit (GRU) mean?[20]
  51. I am trying to find a GRU implementation within DeepLearning4J but cannot seem to find one.[21]
  52. Does anyone know if GRU's are implemented within DL4J?[21]

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