Data Science Stack Exchange
Data Science Stack Exchange

Questions tagged [embeddings]

196 questions
17
votes
2 answers

One Hot Encoding vs Word Embedding - When to choose one or another?

A colleague of mine is having an interesting situation, he has quite a large set of possibilities for a defined categorical feature (+/- 300 different values) The usual data science approach would be to perform a One-Hot Encoding. However, wouldn't…
Jonathan DEKHTIAR
  • 610
  • 2
  • 6
  • 10
12
votes
2 answers

What is the difference between and Embedding Layer and an Autoencoder?

I'm reading about Embedding layers, especially applied to NLP and word2vec, and they seem nothing more than an application of Autoencoders for dimensionality reduction. Are they different? If so, what are the differences between them?
Leevo
  • 6,445
  • 3
  • 18
  • 52
11
votes
1 answer

Confusion about Entity Embeddings of Categorical Variables - Working Example!

Problem Statement: I have problem making the Entity Embedding of Categorical Variable works for a simple dataset. I have followed the original github, or paper, or other blogposts[1,2,or this 3], or this Kaggle kernel; still not working. Data Part:…
TwinPenguins
  • 4,429
  • 3
  • 22
  • 54
9
votes
2 answers

Are there any graph embedding algorithms like this already?

I wrote an algorithm for generating node embeddings based on the graph's topology. Most of the explanation is done in the readme file and the examples. The question is: Am I reinventing the wheel? Does this approach have any practical advantages…
monomonedula
  • 201
  • 1
  • 2
8
votes
1 answer

Difference between Gensim word2vec and keras Embedding layer

I used the gensim word2vec package and Keras Embedding layer for various different projects. Then I realize they seem to do the same thing, they all try to convert a word into a feature vector. Am I understanding this properly? What exactly is the…
Edamame
  • 2,785
  • 5
  • 25
  • 34
7
votes
2 answers

Concatenating embedding and hand-designed features for logistic regression

An interviewer told me that we cannot concatenate an embedding from a neural network (such as a pre-trained image representation) and hand designed features (such as image metadata) for use in a linear model such as logistic regression. He says…
plutothepup
  • 71
  • 1
7
votes
1 answer

How much text is enough to train a good embedding model?

I need to train a word2vec embedding model on Wikipedia articles using Gensim. Eventually, I will use the entire Wikipedia for that but for the moment, I'm doing some experimentation/optimization to improve the model quality and I was wondering how…
Abdulrahman Bres
  • 221
  • 2
  • 15
6
votes
1 answer

What is the neural network architecture behind Facebook's Starspace model?

Recently, Facebook released a paper concerning a general purpose neural embedding model called StarSpace. In their paper, they explain the loss function and the training procedure of the model, but they don't emphasize much on the architecture of…
ChiPlusPlus
  • 547
  • 3
  • 5
  • 15
6
votes
1 answer

Unordered Input

I was just wondering what the best approach is for training a neural network (or any other machine learning algorithm) where the order of the inputs does not matter. For example: f(x1,x2,x3,x4) = f(x2,x1,x3,x4) = f(x2,x4,x1,x3) My current approach…
simeon
  • 173
  • 1
  • 4
5
votes
1 answer

Why are character level models considered less effective than word level models?

I have read that character level models need more computation power than word embeddings, and this is one of the major reasons for their less effectiveness, but i got curious because the word embeddings need a huge vocabulary while character level…
yashdk
  • 51
  • 1
5
votes
1 answer

Graph Embedding Vs Graph Convolution Network

I'm new in Graph-Embedding and GCN(Graph/Geometric Convolution Network). I'm confused and not very much sure about "How training works in GCN"? As per my understanding, GCN training data will be in the form of "Adjacency Matrix" + "Degree Matrix" +…
vipin bansal
  • 1,282
  • 11
  • 19
5
votes
1 answer

Tensorflow: how to look up and average a different amount of embedding vectors per training instance, with multiple training instances per minibatch?

In a recommender system setting: let's say I want to learn to predict future item purchases based on user past purchases using an approach inspired by Youtube's recommender system: Concretely, let's say I have a trainable content-based network that…
Pablo Messina
  • 197
  • 1
  • 3
  • 11
5
votes
2 answers

How can you efficiently cluster speech segments by speaker?

We have ~30 audio snippets, of which around 50% are from the same speaker, who is our target speaker, and the rest are from various different speakers. We want to extract all audio snippets from our target speaker, so basically figure out which…
Yes
  • 181
  • 4
4
votes
2 answers

Can we use embeddings or latent vectors for a recommender system?

I'm having a hard time understanding why people use any vector they find as a candidate for a recommender system. In my mind, a recommender system requires a space where distance represents similarity. Of course, before you can construct such a…
Mehran
  • 277
  • 1
  • 2
  • 13
4
votes
2 answers

Isn't graph embedding a step back from non-euclidean space?

As I understand, we use graph embedding to make a euclidean representation of non-euclidean structure - graph. Does it mean that conceptually we just take a step back to, may be, more complex, but still grid processing?
Nikita
  • 41
  • 5
1
2 3
…
13 14