Questions tagged [attention]

For questions about the concept of attention in artificial intelligence and machine learning. Attention-like mechanisms were successfully used in natural language processing and computer vision tasks, such as machine translation. For a review of attention-based mechanism used in NLP, take a look at "Attention in Natural Language Processing" by Andrea Galassi et al.

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34 views

In Computer Vision, what is the difference between a transformer and attention?

Having been studying computer vision for a while, I still cannot understand what the difference between a transformer and attention is?
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12 views

Computing the mean attention distance for ViT

Recently I came across the paper that introduces the Vision Transformer (ViT) "AN IMAGE IS WORTH 16X16 WORDS: TRANSFORMERS FOR IMAGE RECOGNITION AT SCALE". The thing I don't really ...
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39 views

Are there any advantages of the local attention against convolutions?

Transformer architectures, based on the self-attention mechanism, have achieved outstanding performance in a variety of applications. The main advantage of this approach is that the given token can ...
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12 views

Does the transformers model (in “Attention is All You Need”) exclude the encoder in language modelling tasks?

The language model I am referring to is the one outlined in "Attention is All You Need": My understanding is that when the task is translation, the encoder's input could be "Hi, my ...
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34 views

How do autoregressive attention mechanism work in multi-headed attention?

[LONG POST!!] I am working on a DNN model that works as an improviser to generate music sequences. The idea of generating music is based on taking a sequence of music nodes (their index representation)...
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11 views

Visualizing encoder-attention after ResNet in terms of ResNet input

I have a transform-encoder only architecture, which has the following structure: ...
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24 views

How is the variational lower bound for hard attention derived in Show, Attend and Tell

How is the jump from line 1 to line 2 done in equation 10 of Show, Attend and Tell? While we're at it, another thing that might be muddying the waters for me is that I'm not clear on what the sum is ...
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0answers
16 views

How to Select Model Parameters for Transformer (Heads, number of layers, etc)

Is there a general guideline on how the Transformer model parameters should be selected, or the range of these parameters that should be included in a hyperparameter sweep? Number of heads Number of ...
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1answer
37 views

Do RNNs/LSTMs really need to be sequential?

There are many articles comparing RNNs/LSTMs and the Attention mechanism. One of the disadvantages of RNNs that is often mentioned is that while Attention can be computed in parallel, RNNs are highly ...
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38 views

SAGAN - is there a mistake in the original paper?

in the original paper the following scheme of the self-attention appears: https://arxiv.org/pdf/1805.08318.pdf In a later overview: https://arxiv.org/pdf/1906.01529.pdf this scheme appears: ...
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38 views

Can an existing transformer model be modified to estimate the next most probable number in a sequence of numbers?

Models based on the transformer architectures (GPT, BERT, etc.) work awesome for NLP tasks including taking an input generated from words and producing probability estimates of the next word as the ...
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2answers
111 views

Why does GPT-2 Exclude the Transformer Encoder?

After looking into transformers, BERT, and GPT-2, from what I understand, GPT-2 essentially uses only the decoder part of the original transformer architecture and uses masked self-attention that can ...
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1answer
53 views

How is the transformers' output matrix size arrived at?

In this tensorflow article, the comments in the code say that MHA should output with one of the dimensions being the sequence length of the query/key. However, that means that the second MHA in the ...
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What does the outputlayer of BERT for masked language modelling look like?

In the tutorial BERT – State of the Art Language Model for NLP the masked language modeling pre-training steps are described as follows: In technical terms, the prediction of the output words ...
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3answers
116 views

What kind of word embedding is used in the original transformer?

I am currently trying to understand transformers. To start, I read Attention Is All You Need and also this tutorial. What makes me wonder is the word embedding used in the model. Is word2vec or GloVe ...
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29 views

What is the purpose of “alignment” in the self-attention mechanism of transformers?

I've been reading about transformers & have been having some difficulty understanding the concept of alignment. Based on this article Alignment means matching segments of original text with their ...
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1answer
65 views

Reasoning behind performance improvement with hopfield networks

In the paper Hopfield networks is all you need, the authors mention that their modern Hopfield network layers are a good replacement for pooling, GRU, LSTM, and attention layers, and tend to ...
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46 views

Factors that causing totally different outcomes from an exactly same model and datasets

Here is a model that trains time series data in (batch, step, features) way. I have kept the random state for train test split function the same. Every parameter below the same, running the model ...
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Is the dimensionality of the query and key tensors in this implementation of the transformer's self-attention correct?

Looking at this tutorial matmul_qk = tf.matmul(q, k, transpose_b=True) # (..., seq_len_q, seq_len_k) is the output of two tensors multiplied of shape: ...
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How are the parameters $\alpha_i$ of hard attention trained?

I have a question about Show, Attend and Tell: Neural Image CaptionGeneration with Visual Attention paper by Xu. The basic mechanism of stochastic hard attention is that each pixel of the input image ...
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1answer
160 views

What's the difference between content-based attention and dot-product attention?

I'm following this blog post which enumerates the various types of attention. It mentions content-based attention where the alignment scoring function for the $j$th encoder hidden state with respect ...
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2answers
113 views

Recent deep learning textbook (i.e. covering at least GANs, LSTM and transformers and attention)

I am searching for an academic (i.e. with maths formulae) textbook which covers (at least) the following: GAN LSTM and transformers (e.g. seq2seq) Attention mechanism The closest match I got is Deep ...
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1answer
47 views

Can the attention mechanism improve the performance in the case of short sequences?

I am aware that the attention mechanism can be used to deal with long sequences, where problems related to gradient vanishing and, more generally, representing effectively the whole sequence arise. ...
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1answer
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In attention models with multiple layers, are weight matrices shared across layers?

In articles that describe neural architectures with multiple attention layers of the same form, are the weight matrices usually the same across the layers? Consider as an example, "Attention is ...
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2answers
110 views

In the multi-head attention mechanism of the transformer, why do we need both $W_i^Q$ and ${W_i^K}^T$?

In the Attention is all you need paper, on the 4th page, we have equation 1, which describes the self-attention mechanism of the transformer architecture $$ \text { Attention }(Q, K, V)=\operatorname{...
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How to handle long sequences with transformers?

I have a time series sequence with 10 million steps. In step $t$, I have a 400 dimensional feature vector $X_t$ and a scalar value $y_t$ which I want to predict during inference time and I know during ...
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2answers
191 views

What is different in each head of a multi-head attention mechanism?

I have a difficult time understanding the "multi-head" notion in the original transformer paper. What makes the learning in each head unique? Why doesn't the neural network learn the same ...
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1answer
282 views

What is the difference between Attention Gate and CNN filters?

Attention models/gates are used to focus/pay attention to the important regions. According to this paper, the authors describe that a model with Attention Gate (AG) can be trained from scratch. Then ...
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158 views

What is the gradient of an attention unit?

The paper Attention Is All You Need describes the Transformer architecture, which describes attention as a function of the queries $Q = x W^Q$, keys $K = x W^K$, and values $V = x W^V$: $\text{...
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1answer
94 views

What is the cost function of a transformer?

The paper Attention Is All You Need describes the transformer architecture that has an encoder and a decoder. However, I wasn't clear on what the cost function to minimize is for such an architecture. ...
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1answer
32 views

Is the Decoder mask (triangular mask) applied only in the first decoder block, or to all blocks in Decoder?

The Decoder mask, also called "look-ahead mask", is applied in the Decoder side to prevent it from attending future tokens. Something like this: ...
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1answer
70 views

Transformers: how to get the output (keys and values) of the encoder?

I was reading the paper Attention Is All You Need. It seems like the last step of the encoder is a LayerNorm(relu(WX + B) + X), i.e. an add + normalization. This should result in a $n$ x $d^{model}$ ...
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1answer
45 views

Transformers: how does the decoder final layer output the desired token?

In the paper Attention Is All You Need, this section confuses me: In our model, we share the same weight matrix between the two embedding layers [in the encoding section] and the pre-softmax linear ...
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10 views

Best strategy for Classification of Science Subjects. Phy, Chem , Maths and Bio? BERT, Transformers, Attention+SLTM, Self-Attention+LSTM?

I am working on a project where I have to first classify the Subjects of the given question and then the respective Chapter and then the sub-topic. In a nutshell, I have to predict the Subject, Grade ...
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1answer
83 views

Why are “Transformers” called this way?

What is the reason behind the name "Transformers", for Multi Head Self-Attention-based neural networks from Attention is All You Need? I have been googling this question for a long time, and ...
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1answer
91 views

Any comparison between transformer and RNN+Attention on the same dataset?

I am wondering what is believed to be the reason for superiority of transformer? I see that some people believe because of the attention mechanism used, it’s able to capture much longer dependencies. ...
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3answers
522 views

What is the purpose of Decoder mask (triangular mask) in Transformer?

I'm trying to implement transformer model using this tutorial. In the decoder block of the Transformer model, a mask is passed to "pad and mask future tokens in the input received by the decoder&...
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0answers
64 views

How are weight matrices in attention trained?

I have been looking into transformers lately and been reading tons of tutorials. All of them address the intuition behind attention, which I understand, but they treat training the weight matrices for ...
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0answers
37 views

Is it possible to express attention as a Fourier convolution?

Convolutions can be expressed as a matrix-multiplication (see e.g. this post) and as an element-wise multiplication using the Fourier domain (https://en.wikipedia.org/wiki/Convolution_theorem). ...
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1answer
30 views

Appropriate metric and approach for natural language generation for small sentences

I am trying to create a language generation model to generate very short sentences/words, like a rapper name generator. The sentences in my dataset are anywhere between 1 word and 15 words (3-155 ...
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0answers
29 views

How do non-local neural networks relate to attention and self-attention?

I've been reading non-local neural networks as explained in the original paper. My understanding is that they solve the restrained reception of local filters. I see how they are different from ...
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1answer
313 views

What is the weight matrix in self-attention?

I've been looking into self-attention lately, and in the articles that I've been seeing, they all talk about "weights" in attention. My understanding is that the weights in self-attention ...
5
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1answer
2k views

What exactly are the “parameters” in GPT-3's 175 billion parameters and how are they chosen/generated?

When I studied neural networks, parameters were learning rate, batch size etc. But even GPT3's ArXiv paper does not mention anything about what exactly the parameters are, but gives a small hint that ...
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39 views

Where can I place an attention module for different computer vision tasks?

Where can I place an attention module for different computer vision tasks? For example, in this Github issue, the author is not sure where to place the attention module. Are there any rules or best ...
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1answer
124 views

What is the big fuzz about SHA-RNN versus Transformers?

In his paper introducing SHA-RNN (https://arxiv.org/pdf/1911.11423.pdf) Stephen Merity states that neglecting one direction of research (in this case LSTMs) over another (transformers) merily because ...
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1answer
242 views

Transformers - is the self attention matrix softmax output (layer 1) symmetric?

Let's assume, that we embedded a vector of length 49 into a matrix using 512-d embeddings. If we then multiply the matrix by it transposed version we receive a matrix of 49 by 49. Which is symmetric. ...
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1answer
193 views

What are the keys and values of the attention model for the encoder and decoder in the “Attention Is All You Need” paper?

I have recently encountered the paper on NLP. It is very new to me and I am still unable to see how that works. I have used all the resources over there from the original paper to Youtube videos and ...
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0answers
46 views

How to understand the matrices used in the Attention layer?

Attention-scoring mechanism seems to be a commonly-used component in various seq2seq models, and I was reading about the original "Location-based Attention" in Bahadanau well-known paper at https://...
5
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1answer
604 views

What is the intuition behind the attention mechanism?

Attention idea is one of the most influential ideas in deep learning. The main idea behind attention technique is that it allows the decoder to "look back” at the complete input and extracts ...
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2answers
449 views

Why does this multiplication of $Q$ and $K$ have a variance of $d_k$, in scaled dot product attention?

In scaled dot product attention, we scale our outputs by dividing the dot product by the square root of the dimensionality of the matrix: The reason why is stated that this constrains the ...