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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12
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2answers
187 views

Is there any artificial intelligence that possesses “concentration”?

Humans can do multiple tasks at the same (e.g. reading while listening to music), but we memorize information from less focused sources with worse efficiency than we do from our main focus or task. ...
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1answer
2k views

Will attention based networks prevail over RNN and LSTM? [closed]

There is no point in picking one of the growing number of articles that come up in a web search for, "Deep learning attention networks," however the bold claims in Attention Is All You Need, Ashish ...
8
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1answer
2k views

Why is dot product attention faster than additive attention?

In section 3.2.1 of Attention Is All You Need the claim is made that: Dot-product attention is identical to our algorithm, except for the scaling factor of $\frac{1}{\sqrt{d_k}}$. Additive ...
7
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1answer
628 views

What is the intuition behind the dot product attention?

I am watching Attention all you need, In that what is the intuition behind the dot product attention? $$A(q,K, V) = \sum_i\frac{e^{q.k_i}}{\sum_j e^{q.k_j}} v_i$$ becomes: $$A(Q,K, V) = softmax(QK^...
6
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1answer
1k views

A mathematical explanation of Attention Mechanism

I am trying to understand why attention models are different than just using neural networks. Essentially the optimization of weights or using gates for protecting and controlling cell state (in ...
5
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3answers
335 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&...
5
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1answer
1k 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 ...
4
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1answer
450 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 ...
4
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1answer
80 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 ...
4
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0answers
357 views

What is the difference between GAT and GaAN?

I was looking at two papers Graph Attention Networks (GAT) by Petar Veličković and GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs by Jiani Zhang. I'm trying to ...
3
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1answer
70 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. ...
3
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2answers
104 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 ...
3
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2answers
89 views

Why can't LSTMs keep track of the “important parts” of a sequence?

I keep reading about how LSTMs can't remember the "important parts" of a sequence which is why attention-based mechanisms are required. I was trying to use LSTMs to find people's name format. For ...
3
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1answer
165 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 ...
3
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0answers
126 views

Why don't people use nonlinear activation functions after projecting the query key value in attention?

Why don't people use nonlinear activation functions after projecting the query key value in attention? It seems like doing this would lead to much-needed nonlinearity, otherwise, we're just doing ...
3
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0answers
123 views

What is the intuition behind the calculation of the similarity between encoder and decoder states?

Suppose that we are doing machine translation. We have a conditional language model with attention where we are are trying to predict a sequence $y_1, y_2, \dots, y_J$ from $x_1, x_2, \dots x_I$: $$P(...
2
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1answer
91 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 ...
2
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2answers
354 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 ...
2
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3answers
70 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 ...
2
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1answer
48 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 $d^{model}$ ...
2
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1answer
61 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. ...
2
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1answer
36 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 ...
2
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1answer
364 views

Is the Mask Needed for Masked Self-Attention During Inference with GPT-2

My understanding is that masked self-attention is necessary during training of GPT-2, as otherwise it would be able to directly see the correct next output at each iteration. My question is whether ...
2
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0answers
12 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 ...
2
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0answers
74 views

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 ...
2
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1answer
225 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 ...
2
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0answers
45 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://...
2
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0answers
27 views

How is visual attention mechanism different from a two branch convolutional neural network?

I am doing some research on the visual attention mechanism in remote sensing domain (where the features learnt from one layer are highlighted using the attention mask derived from another layer). From ...
2
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0answers
47 views

How does positional encoding work in the transformer model?

In the transformer model, to incorporate positional information of texts, the researchers have added a positional encoding to the model. How does positional encoding work? How does the positional ...
2
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0answers
29 views

Impact or applications of introducing attention in deep networks modelling multi-agent systems

I have been reading quite a lot about the research progress in the domain of self attention-based neural networks that were introduced by Google Inc. in their paper titled "Attention is all you ...
2
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0answers
183 views

Why is the transformer for time series forecasting faster than RNN?

I've been reading different papers which implements the Transformer for time series forecasting. Most of the them are claiming that the training time is significantly faster then using a normal RNN. ...
2
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0answers
63 views

What is the difference between Squeeze-and-excite and bottleneck modules from Mobilenet v2?

Squezee-and-excite networks introduced SE blocks, while MobileNet v2 introduced linear bottlenecks. What is the effective difference between these two concepts? Is it only implementation (depth-wise ...
2
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0answers
43 views

How do I tag the most interesting parts of a video?

This is a follow-up question from my previous question here. I'm new to ML/DL, and one thing I need to do is to use a machine or deep learning video attention model which as the name suggests, can tag ...
2
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0answers
135 views

Understanding CNN+LSTM concept with attention and need help

I have a question about the context of CNN and LSTM. I have trained a CNN network for image classification. However, I would like to combine it with LSTM for visualizing the attention weights. So, I ...
2
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1answer
141 views

Does GraphSage use hard attention?

I was reading the recent paper Graph Representation Learning via Hard and Channel-Wise Attention Networks, where the authors claim that there is no hard attention operator for graph data. From my ...
2
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0answers
222 views

Why does the BERT encoder have an intermediate layer between the attention and neural network layers with a bigger output?

I am reading the BERT paper BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. As I look at the attention mechanism, I don't understand why in the BERT encoder we have ...
2
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0answers
165 views

How do the sine and cosine functions encode position in the transformer?

After going through both the "Illustrated Transformer" and "Annotated Transformer" blog posts, I still don't understand how the sinusoidal encodings are representing the position of elements in the ...
2
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1answer
160 views

How does transformer leverage GPU which trains faster than RNN?

How does transformer leverage GPU which trains faster than RNN? I understand the parameter space of the transformer might be significantly larger than that of the RNN. But why does the transformer ...
1
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1answer
49 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 ...
1
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1answer
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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2answers
89 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 ...
1
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1answer
31 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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1answer
33 views

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 ...
1
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1answer
28 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: ...
1
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1answer
28 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
34 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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1answer
45 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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0answers
27 views

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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0answers
12 views

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 ...
1
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0answers
109 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{...