Gated Recurrent Unit (GRU) - Revision history

BPeat at 17:38, 3 May 2023

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2023-03-28T19:27:50Z

Text replacement - "http:" to "https:"

BPeat at 15:19, 19 March 2023

2023-03-19T15:19:03Z

BPeat at 17:38, 11 June 2020

2020-06-11T17:38:36Z

BPeat at 17:16, 11 June 2020

2020-06-11T17:16:02Z

BPeat at 17:00, 11 June 2020

2020-06-11T17:00:30Z

BPeat at 16:46, 11 June 2020

2020-06-11T16:46:06Z

BPeat at 16:45, 11 June 2020

2020-06-11T16:45:33Z

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BPeat at 00:11, 1 July 2019

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 ** [[Average-Stochastic Gradient Descent (SGD) Weight-Dropped LSTM (AWD-LSTM)]]
 ** [[Hopfield Network (HN)]]
-* [[Attention]] Mechanism  ...[[Transformer]] Model   ...[[Generative Pre-trained Transformer (GPT)]]
+* [[Attention]] Mechanism  ...[[Transformer]] ...[[Generative Pre-trained Transformer (GPT)]] ... [[Generative Adversarial Network (GAN)|GAN]] ... [[Bidirectional Encoder Representations from Transformers (BERT)|BERT]]
 * [https://towardsdatascience.com/understanding-gru-networks-2ef37df6c9be Understanding GRU Networks | Simeon Kostadinov - Towards Data Science]
 * [https://towardsdatascience.com/animated-rnn-lstm-and-gru-ef124d06cf45 Animated RNN, LSTM and GRU | Raimi Karim - Towards Data Science]

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 |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
 }}
-[http://www.youtube.com/results?search_query=Gated+Recurrent+Unit+GRU+Neural+Network+deep+machine+learning+M YouTube Search]
+[https://www.youtube.com/results?search_query=Gated+Recurrent+Unit+GRU+Neural+Network+deep+machine+learning+M YouTube Search]
-[http://www.google.com/search?q=Gated+Recurrent+Unit+GRU+Neural+Network+deep+machine+learning+ML ...Google search]
+[https://www.google.com/search?q=Gated+Recurrent+Unit+GRU+Neural+Network+deep+machine+learning+ML ...Google search]
-* [http://www.asimovinstitute.org/author/fjodorvanveen/ Neural Network Zoo | Fjodor Van Veen]
+* [https://www.asimovinstitute.org/author/fjodorvanveen/ Neural Network Zoo | Fjodor Van Veen]
 * [[Recurrent Neural Network (RNN)]] Variants:
 ** [[Long Short-Term Memory (LSTM)]]
@@ Line 17: / Line 17: @@
 ** [[Hopfield Network (HN)]]
 * [[Attention]] Mechanism  ...[[Transformer]] Model   ...[[Generative Pre-trained Transformer (GPT)]]
-* [http://towardsdatascience.com/understanding-gru-networks-2ef37df6c9be Understanding GRU Networks | Simeon Kostadinov - Towards Data Science]
+* [https://towardsdatascience.com/understanding-gru-networks-2ef37df6c9be Understanding GRU Networks | Simeon Kostadinov - Towards Data Science]
-* [http://towardsdatascience.com/animated-rnn-lstm-and-gru-ef124d06cf45 Animated RNN, LSTM and GRU | Raimi Karim - Towards Data Science]
+* [https://towardsdatascience.com/animated-rnn-lstm-and-gru-ef124d06cf45 Animated RNN, LSTM and GRU | Raimi Karim - Towards Data Science]
-a gating mechanism in [[Recurrent Neural Network (RNN)]] Gated recurrent units (GRU) are a slight variation on LSTMs. They have one less gate and are wired slightly differently: instead of an input, output and a forget gate, they have an update gate. This update gate determines both how much information to keep from the last state and how much information to let in from the previous layer. The reset gate functions much like the forget gate of an LSTM but it’s located slightly differently. They always send out their full state, they don’t have an output gate. In most cases, they function very similarly to LSTMs, with the biggest difference being that GRUs are slightly faster and easier to run (but also slightly less expressive). In practice these tend to cancel each other out, as you need a bigger network to regain some expressiveness which then in turn cancels out the performance benefits. In some cases where the extra expressiveness is not needed, GRUs can outperform LSTMs. Chung, Junyoung, et al. “Empirical evaluation of gated recurrent neural networks on sequence modeling.” arXiv preprint arXiv:1412.3555 (2014). The GRU is like a [[Long Short-Term Memory (LSTM)]] with forget gate[2] but has fewer parameters than LSTM, as it lacks an output gate.[3] GRU's performance on certain tasks of polyphonic music modeling and speech signal modeling was found to be similar to that of LSTM. GRUs have been shown to exhibit even better performance on certain smaller datasets. [http://en.wikipedia.org/wiki/Gated_recurrent_unit Gated Recurrent Unit | Wikipedia]
+a gating mechanism in [[Recurrent Neural Network (RNN)]] Gated recurrent units (GRU) are a slight variation on LSTMs. They have one less gate and are wired slightly differently: instead of an input, output and a forget gate, they have an update gate. This update gate determines both how much information to keep from the last state and how much information to let in from the previous layer. The reset gate functions much like the forget gate of an LSTM but it’s located slightly differently. They always send out their full state, they don’t have an output gate. In most cases, they function very similarly to LSTMs, with the biggest difference being that GRUs are slightly faster and easier to run (but also slightly less expressive). In practice these tend to cancel each other out, as you need a bigger network to regain some expressiveness which then in turn cancels out the performance benefits. In some cases where the extra expressiveness is not needed, GRUs can outperform LSTMs. Chung, Junyoung, et al. “Empirical evaluation of gated recurrent neural networks on sequence modeling.” arXiv preprint arXiv:1412.3555 (2014). The GRU is like a [[Long Short-Term Memory (LSTM)]] with forget gate[2] but has fewer parameters than LSTM, as it lacks an output gate.[3] GRU's performance on certain tasks of polyphonic music modeling and speech signal modeling was found to be similar to that of LSTM. GRUs have been shown to exhibit even better performance on certain smaller datasets. [https://en.wikipedia.org/wiki/Gated_recurrent_unit Gated Recurrent Unit | Wikipedia]
 Bidirectional Gated Recurrent Unit (BiGRU) looks exactly the same as its unidirectional counterpart. The difference is that the gate is not just connected to the past, but also to the future. Schuster, Mike, and Kuldip K. Paliwal. “Bidirectional recurrent neural networks.” IEEE Transactions on Signal Processing 45.11 (1997): 2673-2681.
-http://www.data-blogger.com/wp-content/uploads/2017/08/gru.png
+https://www.data-blogger.com/wp-content/uploads/2017/08/gru.png
-http://www.asimovinstitute.org/wp-content/uploads/2016/09/gru.png
+https://www.asimovinstitute.org/wp-content/uploads/2016/09/gru.png
 <youtube>xSCy3q2ts44</youtube>

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 ** [[Average-Stochastic Gradient Descent (SGD) Weight-Dropped LSTM (AWD-LSTM)]]
 ** [[Hopfield Network (HN)]]
 * [http://towardsdatascience.com/understanding-gru-networks-2ef37df6c9be Understanding GRU Networks | Simeon Kostadinov - Towards Data Science]
 * [http://towardsdatascience.com/animated-rnn-lstm-and-gru-ef124d06cf45 Animated RNN, LSTM and GRU | Raimi Karim - Towards Data Science]

← Older revision		Revision as of 17:38, 11 June 2020
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	a gating mechanism in [[Recurrent Neural Network (RNN)]] Gated recurrent units (GRU) are a slight variation on LSTMs. They have one less gate and are wired slightly differently: instead of an input, output and a forget gate, they have an update gate. This update gate determines both how much information to keep from the last state and how much information to let in from the previous layer. The reset gate functions much like the forget gate of an LSTM but it’s located slightly differently. They always send out their full state, they don’t have an output gate. In most cases, they function very similarly to LSTMs, with the biggest difference being that GRUs are slightly faster and easier to run (but also slightly less expressive). In practice these tend to cancel each other out, as you need a bigger network to regain some expressiveness which then in turn cancels out the performance benefits. In some cases where the extra expressiveness is not needed, GRUs can outperform LSTMs. Chung, Junyoung, et al. “Empirical evaluation of gated recurrent neural networks on sequence modeling.” arXiv preprint arXiv:1412.3555 (2014). The GRU is like a [[Long Short-Term Memory (LSTM)]] with forget gate[2] but has fewer parameters than LSTM, as it lacks an output gate.[3] GRU's performance on certain tasks of polyphonic music modeling and speech signal modeling was found to be similar to that of LSTM. GRUs have been shown to exhibit even better performance on certain smaller datasets. [http://en.wikipedia.org/wiki/Gated_recurrent_unit Gated Recurrent Unit \| Wikipedia]		a gating mechanism in [[Recurrent Neural Network (RNN)]] Gated recurrent units (GRU) are a slight variation on LSTMs. They have one less gate and are wired slightly differently: instead of an input, output and a forget gate, they have an update gate. This update gate determines both how much information to keep from the last state and how much information to let in from the previous layer. The reset gate functions much like the forget gate of an LSTM but it’s located slightly differently. They always send out their full state, they don’t have an output gate. In most cases, they function very similarly to LSTMs, with the biggest difference being that GRUs are slightly faster and easier to run (but also slightly less expressive). In practice these tend to cancel each other out, as you need a bigger network to regain some expressiveness which then in turn cancels out the performance benefits. In some cases where the extra expressiveness is not needed, GRUs can outperform LSTMs. Chung, Junyoung, et al. “Empirical evaluation of gated recurrent neural networks on sequence modeling.” arXiv preprint arXiv:1412.3555 (2014). The GRU is like a [[Long Short-Term Memory (LSTM)]] with forget gate[2] but has fewer parameters than LSTM, as it lacks an output gate.[3] GRU's performance on certain tasks of polyphonic music modeling and speech signal modeling was found to be similar to that of LSTM. GRUs have been shown to exhibit even better performance on certain smaller datasets. [http://en.wikipedia.org/wiki/Gated_recurrent_unit Gated Recurrent Unit \| Wikipedia]
		+
		+	Bidirectional Gated Recurrent Unit (BiGRU) looks exactly the same as its unidirectional counterpart. The difference is that the gate is not just connected to the past, but also to the future. Schuster, Mike, and Kuldip K. Paliwal. “Bidirectional recurrent neural networks.” IEEE Transactions on Signal Processing 45.11 (1997): 2673-2681.

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 ** [[Bidirectional Long Short-Term Memory (BI-LSTM) with Attention Mechanism]]
 ** [[Average-Stochastic Gradient Descent (SGD) Weight-Dropped LSTM (AWD-LSTM)]]
 * [http://towardsdatascience.com/understanding-gru-networks-2ef37df6c9be Understanding GRU Networks | Simeon Kostadinov - Towards Data Science]
 * [http://towardsdatascience.com/animated-rnn-lstm-and-gru-ef124d06cf45 Animated RNN, LSTM and GRU | Raimi Karim - Towards Data Science]

← Older revision		Revision as of 16:46, 11 June 2020
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	a gating mechanism in [[Recurrent Neural Network (RNN)]] Gated recurrent units (GRU) are a slight variation on LSTMs. They have one less gate and are wired slightly differently: instead of an input, output and a forget gate, they have an update gate. This update gate determines both how much information to keep from the last state and how much information to let in from the previous layer. The reset gate functions much like the forget gate of an LSTM but it’s located slightly differently. They always send out their full state, they don’t have an output gate. In most cases, they function very similarly to LSTMs, with the biggest difference being that GRUs are slightly faster and easier to run (but also slightly less expressive). In practice these tend to cancel each other out, as you need a bigger network to regain some expressiveness which then in turn cancels out the performance benefits. In some cases where the extra expressiveness is not needed, GRUs can outperform LSTMs. Chung, Junyoung, et al. “Empirical evaluation of gated recurrent neural networks on sequence modeling.” arXiv preprint arXiv:1412.3555 (2014). The GRU is like a [[Long Short-Term Memory (LSTM)]] with forget gate[2] but has fewer parameters than LSTM, as it lacks an output gate.[3] GRU's performance on certain tasks of polyphonic music modeling and speech signal modeling was found to be similar to that of LSTM. GRUs have been shown to exhibit even better performance on certain smaller datasets. [http://en.wikipedia.org/wiki/Gated_recurrent_unit Gated Recurrent Unit \| Wikipedia]		a gating mechanism in [[Recurrent Neural Network (RNN)]] Gated recurrent units (GRU) are a slight variation on LSTMs. They have one less gate and are wired slightly differently: instead of an input, output and a forget gate, they have an update gate. This update gate determines both how much information to keep from the last state and how much information to let in from the previous layer. The reset gate functions much like the forget gate of an LSTM but it’s located slightly differently. They always send out their full state, they don’t have an output gate. In most cases, they function very similarly to LSTMs, with the biggest difference being that GRUs are slightly faster and easier to run (but also slightly less expressive). In practice these tend to cancel each other out, as you need a bigger network to regain some expressiveness which then in turn cancels out the performance benefits. In some cases where the extra expressiveness is not needed, GRUs can outperform LSTMs. Chung, Junyoung, et al. “Empirical evaluation of gated recurrent neural networks on sequence modeling.” arXiv preprint arXiv:1412.3555 (2014). The GRU is like a [[Long Short-Term Memory (LSTM)]] with forget gate[2] but has fewer parameters than LSTM, as it lacks an output gate.[3] GRU's performance on certain tasks of polyphonic music modeling and speech signal modeling was found to be similar to that of LSTM. GRUs have been shown to exhibit even better performance on certain smaller datasets. [http://en.wikipedia.org/wiki/Gated_recurrent_unit Gated Recurrent Unit \| Wikipedia]

−	~~http://upload.wikimedia.org/wikipedia/commons/thumb/b/bf/Gated_Recurrent_Unit%2C_type_2.svg/780px-Gated_Recurrent_Unit%2C_type_2.svg.png~~

	http://www.data-blogger.com/wp-content/uploads/2017/08/gru.png		http://www.data-blogger.com/wp-content/uploads/2017/08/gru.png

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 * [http://towardsdatascience.com/animated-rnn-lstm-and-gru-ef124d06cf45 Animated RNN, LSTM and GRU | Raimi Karim - Towards Data Science]
-a gating mechanism in [[Recurrent Neural Network (RNN)]] The GRU is like a [[Long Short-Term Memory (LSTM)]] with forget gate[2] but has fewer parameters than LSTM, as it lacks an output gate.[3] GRU's performance on certain tasks of polyphonic music modeling and speech signal modeling was found to be similar to that of LSTM. GRUs have been shown to exhibit even better performance on certain smaller datasets. [http://en.wikipedia.org/wiki/Gated_recurrent_unit Gated Recurrent Unit | Wikipedia]
+a gating mechanism in [[Recurrent Neural Network (RNN)]] Gated recurrent units (GRU) are a slight variation on LSTMs. They have one less gate and are wired slightly differently: instead of an input, output and a forget gate, they have an update gate. This update gate determines both how much information to keep from the last state and how much information to let in from the previous layer. The reset gate functions much like the forget gate of an LSTM but it’s located slightly differently. They always send out their full state, they don’t have an output gate. In most cases, they function very similarly to LSTMs, with the biggest difference being that GRUs are slightly faster and easier to run (but also slightly less expressive). In practice these tend to cancel each other out, as you need a bigger network to regain some expressiveness which then in turn cancels out the performance benefits. In some cases where the extra expressiveness is not needed, GRUs can outperform LSTMs. Chung, Junyoung, et al. “Empirical evaluation of gated recurrent neural networks on sequence modeling.” arXiv preprint arXiv:1412.3555 (2014). The GRU is like a [[Long Short-Term Memory (LSTM)]] with forget gate[2] but has fewer parameters than LSTM, as it lacks an output gate.[3] GRU's performance on certain tasks of polyphonic music modeling and speech signal modeling was found to be similar to that of LSTM. GRUs have been shown to exhibit even better performance on certain smaller datasets. [http://en.wikipedia.org/wiki/Gated_recurrent_unit Gated Recurrent Unit | Wikipedia]
 http://upload.wikimedia.org/wikipedia/commons/thumb/b/bf/Gated_Recurrent_Unit%2C_type_2.svg/780px-Gated_Recurrent_Unit%2C_type_2.svg.png
 <youtube>xSCy3q2ts44</youtube>
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