Manhattan LSTM (MaLSTM) - Revision history

BPeat at 02:39, 3 March 2024

2024-03-03T02:39:19Z

BPeat at 00:37, 27 June 2023

2023-06-27T00:37:38Z

BPeat at 12:47, 17 February 2023

2023-02-17T12:47:24Z

BPeat at 17:55, 17 August 2020

2020-08-17T17:55:33Z

BPeat at 17:54, 17 August 2020

2020-08-17T17:54:07Z

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[http://www.youtube.com/results?search_query=Manhattan+LSTM+MaLSTM YouTube search...]
[http://www.google.com/search?q=Manhattan+LSTM+MaLSTM+machine+learning+ML+artificial+intelligence ...Google search]

* [[Recurrent Neural Network (RNN)]] Variants:
** [[Long Short-Term Memory (LSTM)]]
* [http://medium.com/mlreview/implementing-malstm-on-kaggles-quora-question-pairs-competition-8b31b0b16a07 How to predict Quora Question Pairs using Siamese Manhattan LSTM | Elior Cohen - MLreview] ...and its appliance to Kaggle’s Quora Pairs competition.

<youtube>flJ_AP6W3UI</youtube>
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← Older revision		Revision as of 02:39, 3 March 2024
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	* [http://medium.com/mlreview/implementing-malstm-on-kaggles-quora-question-pairs-competition-8b31b0b16a07 How to predict Quora Question Pairs using Siamese Manhattan LSTM \| Elior Cohen - MLreview] ...and its appliance to Kaggle’s Quora Pairs competition.		* [http://medium.com/mlreview/implementing-malstm-on-kaggles-quora-question-pairs-competition-8b31b0b16a07 How to predict Quora Question Pairs using Siamese Manhattan LSTM \| Elior Cohen - MLreview] ...and its appliance to Kaggle’s Quora Pairs competition.

−	We present a ~~siamese~~ adaptation of the Long Short-Term Memory (LSTM) network for labeled data comprised of pairs of variable-length sequences. Our model is applied to assess semantic similarity between sentences, where we exceed state of the art, outperforming carefully handcrafted features and recently proposed neural network systems of greater complexity. For these applications, we provide word-[[embedding]] vectors supplemented with synonymic information to the LSTMs, which use a fixed size vector to encode the underlying meaning expressed in a sentence (irrespective of the particular wording/syntax). By restricting subsequent operations to rely on a simple Manhattan metric, we compel the sentence representations learned by our model to form a highly structured space whose geometry reflects complex semantic relationships. Our results are the latest in a line of findings that showcase LSTMs as powerful language models capable of tasks requiring intricate understanding. [http://dl.acm.org/doi/10.5555/3016100.3016291 Siamese recurrent architectures for learning sentence similarity \| Jonas Mueller and Aditya Thyagarajan]	+	We present a Siamese adaptation of the [[Long Short-Term Memory (LSTM)]] network for labeled data comprised of pairs of variable-length sequences. Our model is applied to assess semantic similarity between sentences, where we exceed state of the art, outperforming carefully handcrafted features and recently proposed neural network systems of greater complexity. For these applications, we provide word-[[embedding]] vectors supplemented with synonymic information to the LSTMs, which use a fixed size vector to encode the underlying meaning expressed in a sentence (irrespective of the particular wording/syntax). By restricting subsequent operations to rely on a simple Manhattan metric, we compel the sentence representations learned by our model to form a highly structured space whose geometry reflects complex semantic relationships. Our results are the latest in a line of findings that showcase LSTMs as powerful language models capable of tasks requiring intricate understanding. [http://dl.acm.org/doi/10.5555/3016100.3016291 Siamese recurrent architectures for learning sentence similarity \| Jonas Mueller and Aditya Thyagarajan]

← Older revision		Revision as of 00:37, 27 June 2023
Line 14:		Line 14:
	* [http://medium.com/mlreview/implementing-malstm-on-kaggles-quora-question-pairs-competition-8b31b0b16a07 How to predict Quora Question Pairs using Siamese Manhattan LSTM \| Elior Cohen - MLreview] ...and its appliance to Kaggle’s Quora Pairs competition.		* [http://medium.com/mlreview/implementing-malstm-on-kaggles-quora-question-pairs-competition-8b31b0b16a07 How to predict Quora Question Pairs using Siamese Manhattan LSTM \| Elior Cohen - MLreview] ...and its appliance to Kaggle’s Quora Pairs competition.

−	We present a siamese adaptation of the Long Short-Term Memory (LSTM) network for labeled data comprised of pairs of variable-length sequences. Our model is applied to assess semantic similarity between sentences, where we exceed state of the art, outperforming carefully handcrafted features and recently proposed neural network systems of greater complexity. For these applications, we provide word-embedding vectors supplemented with synonymic information to the LSTMs, which use a fixed size vector to encode the underlying meaning expressed in a sentence (irrespective of the particular wording/syntax). By restricting subsequent operations to rely on a simple Manhattan metric, we compel the sentence representations learned by our model to form a highly structured space whose geometry reflects complex semantic relationships. Our results are the latest in a line of findings that showcase LSTMs as powerful language models capable of tasks requiring intricate understanding. [http://dl.acm.org/doi/10.5555/3016100.3016291 Siamese recurrent architectures for learning sentence similarity \| Jonas Mueller and Aditya Thyagarajan]	+	We present a siamese adaptation of the Long Short-Term Memory (LSTM) network for labeled data comprised of pairs of variable-length sequences. Our model is applied to assess semantic similarity between sentences, where we exceed state of the art, outperforming carefully handcrafted features and recently proposed neural network systems of greater complexity. For these applications, we provide word-[[embedding]] vectors supplemented with synonymic information to the LSTMs, which use a fixed size vector to encode the underlying meaning expressed in a sentence (irrespective of the particular wording/syntax). By restricting subsequent operations to rely on a simple Manhattan metric, we compel the sentence representations learned by our model to form a highly structured space whose geometry reflects complex semantic relationships. Our results are the latest in a line of findings that showcase LSTMs as powerful language models capable of tasks requiring intricate understanding. [http://dl.acm.org/doi/10.5555/3016100.3016291 Siamese recurrent architectures for learning sentence similarity \| Jonas Mueller and Aditya Thyagarajan]

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 * [[Case Studies]]
-** [[Space/Planets]]
+** [[Astronomy]]
 * [[Recurrent Neural Network (RNN)]] Variants:
 ** [[Long Short-Term Memory (LSTM)]]

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 ** [[Long Short-Term Memory (LSTM)]]
 * [http://medium.com/mlreview/implementing-malstm-on-kaggles-quora-question-pairs-competition-8b31b0b16a07 How to predict Quora Question Pairs using Siamese Manhattan LSTM | Elior Cohen - MLreview] ...and its appliance to Kaggle’s Quora Pairs competition.
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