Newest 'embeddings' Questions

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vote

1answer

19 views

Perform clustering on high dimensional data

Recently I trained a BYOL model on a set of images to learn an embedding space where similar vectors are close by. The performance was fantastic when I performed approximate K-nearest neighbours ...

asked Jan 14 at 6:54

VEDANT JOSHI

211 bronze badge

2

votes

1answer

75 views

What are knowledge graph embeddings?

What are knowledge graph embeddings? How are they useful? Are there any extensive reviews on the subject to know all the details? Note that I am asking this question just to give a quick overview of ...

reference-request embeddings knowledge-graph knowledge-graph-embeddings

asked Dec 26 '21 at 11:57

nbro♦

31.7k8 gold badges66 silver badges131 bronze badges

2

votes

0answers

27 views

Is there any way to force one input have more effect on model?

Now I am working on building a deep learning model for a regression problem. I used 50 inputs and try to add one new categorical input. The problem is that this one input is much more important than ...

deep-learning architecture embeddings input-layer

asked Dec 23 '21 at 1:15

taegyun kim

212 bronze badges

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votes

1answer

25 views

Best way to resize 3d to 2d matrix

I have a (5, 128, 768) matrix, that is, I have 5 embedding spaces of shape (128, 768). Since they all keep a relation, and for ...

deep-learning embeddings

asked Dec 5 '21 at 0:31

noober

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vote

0answers

19 views

How do the scale of an embedding affects a downstream task?

I am currently training a neural network in a self-supervised fashion, using Contrastive Loss and I want to use that network then to fine-tune it in a classification task with a small fraction of the ...

computer-vision embeddings self-supervised-learning

asked Nov 4 '21 at 12:50

adam

111 bronze badge

1

vote

0answers

14 views

What is the reason behind using node embeddings?

I was reading Chapter 3 from the following book (here) on graph representation learning. The chapter is about node embeddings. Question: What is the point of using node embeddings? Do we use them: to ...

geometric-deep-learning graph-neural-networks graphs embeddings

asked Sep 19 '21 at 19:48

Rocky the Owl

455 bronze badges

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10 views

Mapping ground truth to downsampled embeddings

I am currently pulling embeddings out of the mid layers of PSPNet. I was wondering if anyone knows of a way to see what pixels in the ground truth map to the pixels in the intermediate layers? e.g. we ...

deep-learning computer-vision embeddings semantic-segmentation

asked Sep 1 '21 at 18:02

joshjoshjosh

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0answers

18 views

Is graph embedding linear in its maintaining of graph geometry?

It is claimed that the main goal of graph embedding methods is to pack every node's properties into a vector with a smaller dimension, so node similarity in the original complex irregular spaces can ...

geometric-deep-learning graph-neural-networks embeddings knowledge-graph

asked Aug 10 '21 at 8:48

Nick

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18 views

Why use two different embeddings for actions in this paper?

I was reading this paper Top-𝐾 Off-Policy Correction for a REINFORCE Recommender System and I'm wondering is there a particular advantage to use different embeddings for actions, one embedding is ...

deep-rl reinforce recommender-system embeddings softmax

asked Aug 3 '21 at 10:33

Daviiid

4859 bronze badges

2

votes

2answers

216 views

How to determine the embedding size?

When we are training a neural network, we are going to determine the embedding size to convert the categorical (in NLP, for instance) or continuous (in computer vision or voice) information to hidden ...

deep-learning hyperparameter-optimization hyper-parameters embeddings

asked Jul 7 '21 at 13:26

Lerner Zhang

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votes

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41 views

Anything similar to BERT but for pixel-wise embedding in images

In NLP there is BERT which can take a sentence and turn it into an embedding (vector representation) which in some ways encompasses the "meaning" or more precisely the context of the ...

convolutional-neural-networks image-segmentation transfer-learning embeddings

asked Jun 6 '21 at 9:46

Hugo Gransträm

12 bronze badges

1

vote

0answers

23 views

Why are BERT embeddings interpreted as representations of the corresponding words?

It's often assumed in literature that BERT embeddings are contextual representations of the corresponding word. That is, if the 5th word is "cold", then the 5th BERT embedding is a ...

natural-language-processing word-embedding bert embeddings

asked Jun 2 '21 at 14:59

EmbeddingEnthusiast

112 bronze badges

2

votes

1answer

55 views

What is an "input embedding" in the context of NLP?

When reading about NLP, I saw it said that "input embeddings" are a main element of encoder-decoder learning frameworks for sequence modelling. What is an "input embedding" in the ...

natural-language-processing embeddings

asked May 30 '21 at 20:31

The Pointer

4312 silver badges15 bronze badges

5

votes

2answers

320 views

Is an embedding a representation of a word or its meaning?

What does the term "embedding" actually mean? An embedding is a vector, but is that vector a representation of a word or its meaning? Literature loosely uses the word for both purposes. ...

terminology word-embedding embeddings

asked May 29 '21 at 7:11

hanugm

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0

votes

0answers

26 views

Which well known node embedding algorithms to use for weighted graphs?

I am looking for a node representation learning algorithm to generate node embeddings that supports weighted graphs. I modified GCN to support weighted graphs, but I want to know an algorithm that ...

reference-request graph-neural-networks algorithm-request embeddings model-request

asked May 24 '21 at 22:54

ali naman

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votes

2answers

114 views

Is it realistic to train a transformer-based model (e.g. GPT) in a self-supervised way directly on the Mel spectrogram?

In music information retrieval, one usually converts an audio signal into some kind "sequence of frequency-vectors", such as STFT or Mel-spectrogram. I'm wondering if it is a good idea to ...

transformer gpt audio-processing embeddings self-supervised-learning

asked May 24 '21 at 21:31

Peter Franek

837 bronze badges

0

votes

1answer

40 views

How does t-SNE preserves embedding orders?

According to the triplet loss Wikipedia page: t-SNE (t-distributed Stochastic Neighbor Embedding) preserves embedding orders via probability distributions, whereas triplet loss works directly on ...

dimensionality-reduction embeddings dimensionality

asked May 8 '21 at 12:58

Revolucion for Monica

1555 bronze badges

2

votes

0answers

18 views

Embedding from Transformer-based model from paragraph or documnet (like Doc2Vec)

I have a set of data that contains the different lengths of sequences. On average the sequence length is 600. The dataset is like this: ...

natural-language-processing transformer bert embeddings

asked Apr 22 '21 at 18:50

Bloodstone Programmer

1652 bronze badges

0

votes

1answer

34 views

Are the Word2Vec encoded embeddings available online? [closed]

I am trying to do an NLP project and was wondering if there is anywhere online where the Word2Vec embeddings are stored (the actual n-dimmensional vectors). I want to search up a word and see what its ...

deep-learning natural-language-processing word-embedding word2vec embeddings

asked Feb 18 '21 at 13:05

user1234

234 bronze badges

0

votes

1answer

33 views

Converting age and sex variables to a 64-unit dense layer

I am studying a preprint for my own learning (https://www.medrxiv.org/content/medrxiv/early/2020/04/27/2020.04.23.20067967.full.pdf) and I am befuddled by the following detail of the neural network ...

convolutional-neural-networks papers embeddings

asked Feb 17 '21 at 18:07

barnhillec

1113 bronze badges

0

votes

0answers

39 views

Seq2Seq Modelling: when implementing some machine translation net, how are special tokens embedded?

When implementing any encoder-decoder network for machine translation, during training we provide the true output sentence to the decoder so that the context vector (from source language) may be ...

recurrent-neural-networks long-short-term-memory word-embedding gated-recurrent-unit embeddings

asked Feb 8 '21 at 6:02

anurag

1516 bronze badges

0

votes

0answers

42 views

How to change the number of input neurons in embedding layer?

I was building a recommender system using Tensorflow recommenders (TFRS) library . I was following the official tutorial for ranking model , where they have used two-tower model. The part where I have ...

neural-networks deep-learning tensorflow recommender-system embeddings

asked Feb 7 '21 at 12:22

Ajinkya Dandvate

11 bronze badge

0

votes

0answers

31 views

Sparse Multi-hot encoding and autoencoders

I'm working with graph neural networks. I have a large graph. Each node has 4 features [A,B,C,D]: 2 categorical with high cardinality: 86k (A) and 148k (B) different features 2 integer with ranges: [...

graph-neural-networks knowledge-graph embeddings

asked Feb 6 '21 at 0:48

med

11 bronze badge

2

votes

1answer

63 views

How does the embeddings work in vision transformer from paper?

I get the part from the paper where the image is split into P say 16x16 (smaller images) patches and then you have to ...

neural-networks computer-vision transformer embeddings vision-transformer

asked Jan 26 '21 at 4:28

Deshwal

2231 silver badge6 bronze badges

1

vote

0answers

23 views

Is there any research work that shows that we should explicitly mark the word boundaries for 1D CNNs?

I'm doing character embedding for NLP tasks using one-dimensional convolutional neural networks (see Chiu and Nichols (2016) for the motivation). I haven't found any empirical evidence of whether or ...

neural-networks convolutional-neural-networks natural-language-processing reference-request embeddings

asked Jan 3 '21 at 0:58

CorruptedHeapScapeGoat

2961 silver badge11 bronze badges

2

votes

1answer

92 views

In the machine learning literature, what does it mean to say that something is "embedded" in some space?

In the machine learning literature, I often see it said that something is "embedded" in some space. For instance, that something is "embedded" in feature space, or that our data ...

machine-learning terminology definitions embeddings

asked Dec 30 '20 at 17:23

The Pointer

4312 silver badges15 bronze badges

1

vote

1answer

94 views

What is the role of embeddings in a deep recurrent Q network?

When describing the model architecture for a deep recurrent q network, the authors of the paper Learning to Communicate with Deep Multi-Agent Reinforcement Learning each agent consists of a recurrent ...

reinforcement-learning recurrent-neural-networks dqn deep-rl embeddings

asked Jul 3 '20 at 10:21

kikram

434 bronze badges

20

votes

5answers

8k views

What is the difference between latent and embedding spaces?

In general, the word "latent" means "hidden" and "to embed" means "to incorporate". In machine learning, the expressions "hidden (or latent) space" ...

machine-learning terminology word-embedding latent-variable embeddings

asked Mar 17 '19 at 11:56

nbro♦

31.7k8 gold badges66 silver badges131 bronze badges

1

vote

1answer

2k views

How is the constraint $\|f(x)\|_{2}=1$ enforced for the embedding $f(x)$ in the FaceNet paper?

In the FaceNet paper, under section 3.2, the authors mention that: The embedding is represented by $f(x) \in \mathbb{R}^{d}$. It embeds an image $x$ into a $d$-dimensional Euclidean space. ...

papers embeddings facenet

asked Feb 26 '18 at 5:55

Abhijit Balaji

1811 silver badge9 bronze badges

3

votes

2answers

105 views

What is the intuition behind how word embeddings bring information to a neural network?

How is it that a word embedding layer (say word2vec) brings more insights to the neural network compared to a simple one-hot encoded layer? I understand how the word embedding carries some semantic ...

neural-networks machine-learning natural-language-processing word-embedding embeddings

asked Feb 23 '18 at 10:25

silkAdmin

1793 bronze badges

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Questions tagged [embeddings]

Perform clustering on high dimensional data

What are knowledge graph embeddings?

Is there any way to force one input have more effect on model?

Best way to resize 3d to 2d matrix

How do the scale of an embedding affects a downstream task?

What is the reason behind using node embeddings?

Mapping ground truth to downsampled embeddings

Is graph embedding linear in its maintaining of graph geometry?

Why use two different embeddings for actions in this paper?

How to determine the embedding size?

Anything similar to BERT but for pixel-wise embedding in images

Why are BERT embeddings interpreted as representations of the corresponding words?

What is an "input embedding" in the context of NLP?

Is an embedding a representation of a word or its meaning?

Which well known node embedding algorithms to use for weighted graphs?

Is it realistic to train a transformer-based model (e.g. GPT) in a self-supervised way directly on the Mel spectrogram?

How does t-SNE preserves embedding orders?

Embedding from Transformer-based model from paragraph or documnet (like Doc2Vec)

Are the Word2Vec encoded embeddings available online? [closed]

Converting age and sex variables to a 64-unit dense layer

Seq2Seq Modelling: when implementing some machine translation net, how are special tokens embedded?

How to change the number of input neurons in embedding layer?

Sparse Multi-hot encoding and autoencoders

How does the embeddings work in vision transformer from paper?

Is there any research work that shows that we should explicitly mark the word boundaries for 1D CNNs?

In the machine learning literature, what does it mean to say that something is "embedded" in some space?

What is the role of embeddings in a deep recurrent Q network?

What is the difference between latent and embedding spaces?

How is the constraint $\|f(x)\|_{2}=1$ enforced for the embedding $f(x)$ in the FaceNet paper?

What is the intuition behind how word embeddings bring information to a neural network?

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Questions tagged [embeddings]

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