Questions tagged [attention-mechanism]
The attention-mechanism tag has no usage guidance.
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10 views
Weight matrices in transformers
I am trying to understand the transformer architecture.
I am aware that the encoder/decoder contains multiple stacked self attention layers. Further each layer contains multiple heads. For example ...
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1answer
18 views
In Deep Learning, how many kinds of Attention exist? And what is the history of Attention models? [closed]
How many definitions of attention are commonly employed for Deep Learning tasks?
That's what I've encountered up to now:
Self-attention
Bahdanau
Luong
Multi-Head (used in Transformers)
Could you ...
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1answer
18 views
Any good Implementations of Bi-LSTM bahdanau attention in Keras?
From past few weeks I'm trying to learn sequence to sequence machine translation modelling but I couldn't find any good examples/tutorials with bahdanau attention implemented. I did come across a ton ...
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8 views
Training a model for Single Image Super Reoslution
I'm trying to implement the Attention-based approach for SISR paper. However, during something odd happens. The MAE for the first output of the model is very small. But as the training progresses, the ...
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4 views
How can we use Attention Layer in a Encoder Decoder Model Architecture for Next Phrase Prediction?
I am currently building a Encoder Decoder Model for Next Phrase Prediction, but only using this model is just giving satisfactory results. So I chose to attach a Attention Layer to the same ...
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2answers
103 views
What is the advantage of positional encoding over one hot encoding in a transformer model?
I'm trying to read and understand the paper Attention is all you need and in it, they used positional encoding with sin for even indices and cos for odd indices.
In the paper (Section 3.5), they ...
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17 views
two different attention methods for seq2seq
I see two different ways of applying attention in seq2seq:
(a) the context vector (the weighted sum of encoder hidden states) fed into the output softmax, as shown in the diagram below. The diagram ...
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0answers
14 views
How to Visualize Graph Attention
I am quite new to the concept of attention. I am working with graph data and running graph convolution on it to learn node level embedding first. Then an attention layer to aggregate the nodes to ...
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20 views
Hard Attention without RL or similar
I want to implement hard attention with Tensorflow in my neural network.
To summarize my understanding of soft and hard attention, let's say we attend over $x_i$'s: In soft attention, we calculate ...
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10 views
How to understand the difference between query and memory in the attention of transformer?
The code is like this:
...
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19 views
Pytorch Implementing Simple Attention using Dummy data
Hi I am trying to implement simple/General attention in Pytorch , So far the model seems to working , but what i am intersted in doing is getting the attention weights , so that i can visualize it .
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1answer
481 views
Gumbel-Softmax trick vs Softmax with temperature
From what I understand, the Gumbel-Softmax trick is a technique that enables us to sample discrete random variables, in a way that is differentiable (and therefore suited for end-to-end deep learning)....
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65 views
DA-RNN Keras implementation
Is there DA-RNN implementation with Keras or TensorFlow?
If its a commented notebook it would be amazing
https://arxiv.org/abs/1704.02971 here is the paper I am referring, I only found Torch ...
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238 views
In a Transformer model, why does one sum positional encoding to the embedding rather than concatenate it?
While reviewing the Transformer architecture, I realized something I didn't expect, which is that :
the positional encoding is summed to the word embeddings
rather than concatenated to it.
...
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1answer
73 views
Attention Mechanism: Why use context vector instead of attention weights?
In attention, the context vector ($c$) is derived from the sum of the attention weights ($\alpha$) multiplied by the encoder hidden states ($h$), where the weights are obtained by multiplying the ...
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0answers
117 views
ValueError: Dimensions must be equal, but are 256 and 12 for 'attention_layer/MatMul_1' (op: 'MatMul') with input shapes: [?,256], [12,256]
I'm working on a sequence-to-sequence approach using LSTM and a VAE with an attention mechanism.
...
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413 views
ValueError: Cannot convert a partially known TensorShape to a Tensor: (?, 256)
I'm working on a sequence to sequence approach using LSTM and a VAE with an attention mechanism.
...
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2answers
7k views
What is the positional encoding in the transformer model?
I'm new to ML and this is my first question here, so sorry if my question is silly.
I'm trying to read and understand the paper Attention is all you need and in it, there is a picture:
I don't know ...
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0answers
213 views
What's the difference between Attention vs Self-Attention? What problems does each other solve that the other can't?
As stated in the question above..is there a difference between attention and self attention mechanism ? Also additionally can anybody share with me tips and tricks about how self attention mechanism ...
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1answer
2k views
Can BERT do the next-word-predict task?
As BERT is bidirectional (uses bi-directional transformer), is it possible to use it for the next-word-predict task? If yes, what needs to be tweaked?
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1answer
224 views
What is the reason for the speedup of transformer-xl?
The inference speed of transformer-xl is faster than transformer.
Why?
If state reuse is the reason, so it is compared by two 32seq_len + state-reuse vs one 64seq_len + no-state-reuse?
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0answers
174 views
Keras value error: Operands could not be broadcast with with shapes(100,100) - GRU
I am trying to use Hierarchical Attention Networks for classification of news articles using 20 newsgroup dataset that i downloaded from the internet. I came across this code of the implementation and ...
2
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1answer
638 views
Variable input/output length for Transformer
I was reading the paper "Attention is all you need" (https://arxiv.org/pdf/1706.03762.pdf ) and came across this site
http://jalammar.github.io/illustrated-transformer/ which provided a great ...
2
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0answers
632 views
How to train tensorflow's transformer model on my own data?
https://github.com/tensorflow/models/blob/master/official/transformer has an implementation of transformer model.
I want to train the model on my own data(consisting of two files, src.txt, and tgt.txt)...
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2answers
970 views
Keras Attention Guided CNN problem
I am working on a CNN for XRay image classification and I can't seem to be able to properly train it. I am trying to implement the following paper in Keras: https://arxiv.org/pdf/1801.09927.pdf
In ...
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1answer
80 views
Why and how BERT can learn different attentions for each head?
https://towardsdatascience.com/deconstructing-bert-distilling-6-patterns-from-100-million-parameters-b49113672f77
I read the blog above. It visualizes that different color/head has different ...
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26 views
Sub-Object Attention models
Questions first:
I need help to focus myself on the most relevant attention model papers (Attention to Attention if you will). Where should I start?
Have you heard of attention models that focus on ...
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0answers
126 views
How do I implement an attention mechanism for convolutional neural network in Keras?
I have a convolutional neural network in Keras on which I'd like to add an attention mechanism? Has anyone done this? It seems Keras doesn't have an in-built attention mechanism and the ones I've ...
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0answers
1k views
Why does Position Embeddings work?
In the papers "Convolutional Sequence to Sequence Learning" and
"Attention Is All You Need", positions embeddings are simply added to the input words embeddings to give the model a sense of the order ...
6
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1answer
622 views
How do attention mechanisms in RNNs learn weights for a variable length input
Attention mechanisms in RNNs are reasonably common to sequence to sequence models.
I understand that the decoder learns a weight vector $\alpha$ which is applied as a weighted sum of the output ...
