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quora-duplicate-questions
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embed.py
- embeddings from sparse encodings
- word embeddings via lstm encoding of character sequences
- word embeddings via dense layer + max pooling over character sequences
- word embeddings via convolution + max pooling over character sequences
propogate.py
- lstm layer
- bidirectional lstm layer
- time distributed dense layer
- temporal convolution layer
- dense layer
attend.py
- multiplicative attention
- additive attention
- concat attention
- dot attention
- cosine attention
- softmax attentive matching
- maxpool attentive matching
- argmax attentive matching
encode.py
- lstm encoder
- bidirectional lstm encoder
- max pooling encoder
- sum pooling encoder
- mean pooling encoder
compare.py
- cosine
- euclidian
- manhattan
- dot
- dense (learnable distance function)
- mahalanobis (with learnable covariance matrix) (TODO)