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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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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29 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
73 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
26 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
27 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 ...
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2answers
58 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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50 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 ...
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1answer
42 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
47 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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38 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
45 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
20 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
31 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
27 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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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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54 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
48 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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2answers
135 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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45 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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32 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
26 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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25 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
117 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 ...
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1answer
776 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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38 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
55 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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Which model structure is best suited to build a Math OCR (img2latex)? RAM, DRAM, CRNN or Attention OCR?

I am trying to build an OCR which can read the Mathematical equations just like MAthPix and im2markup. im2markup by HarvardNLP seems like a good model but the thing is that it is built using Torch and ...
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1answer
98 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
130 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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42 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://...
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1answer
229 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
165 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 ...
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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 ...
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21 views

How many spectrogram frames per input character does text-to-speech (TTS) system Tacotron-2 generate?

I've been reading on Tacotron-2, a text-to-speech system, that generates speech just-like humans (indistinguishable from humans) using the GitHub https://github.com/Rayhane-mamah/Tacotron-2. I'm very ...
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1answer
324 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^...
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37 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 ...
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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 ...
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1answer
47 views

time-series prediction : loss going down, then stagnates with very high variance

I am trying to design a model based on LSTM cells to do time-series prediction. The ouput value is an integer in [0,13]. I have noticed that one-hot encoding it and ...
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2answers
87 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 ...
2
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1answer
117 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 ...
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1answer
269 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 ...
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0answers
163 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. ...
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0answers
50 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 ...
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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 ...
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116 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 ...
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1answer
120 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 ...
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329 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 ...
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1answer
951 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 ...
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78 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 ...