Keras and Tensorflow implementation of Siamese Recurrent Architectures for Learning Sentence Similarity
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Updated
May 11, 2019 - Python
Keras and Tensorflow implementation of Siamese Recurrent Architectures for Learning Sentence Similarity
Keras and PyTorch implementations of the MaLSTM model for computing Semantic Similarity.
Usage of Siamese Recurrent Neural network architectures for semantic textual similarity
Tensorflow based implementation of deep siamese LSTM network for sentence classification using character embeddings
Anomaly Classification in Time Series Data
This repository contains keras implementation of the paper Learning Sentence Similarity with Siamese Recurrent Architectures
Siamese Manhattan Bi-GRU for semantic similarity between sentences
Implementation of all Siamese networks for sentence similarity
The Facenet paper of 2015 proposed an interesting solution for huge multiclass problems. Instead of the traditional approach, we try to learn a similarity function i.e. degree of difference between 2 inputs. If the degree of difference between the inputs is less than a threshold then the inputs are classified as similar else different.
Code-base for our submission to the DSTC7 challenge (Subtask-1)
Solution for the Quora question pairs competition on kaggle
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