### “Discovery” of GloVe Model

In the original GlOve paper, the authors discuss group theory when coming up with Equation (4). Is it possible that the authors came up with this model, figured out it was good, and then later found out various group theory justifications that justified it? Or was it discovered sequentially as it is described in the…

### VAE latent space collapse

I’m trying to train a VAE using a graph dataset. However, my latent space shrinks epoch by epoch. Meanwhile, my ELBO plot comes to a steady state after few epochs. I tried to play around with parameters and I realized by increasing the batch size or training data, this happens faster, and ELBO comes to…

### Can a neural network be trained to classify a number N1 as being divisible by N2?

So I’ve been working on my own little dynamic architecture for a deep neural network (any number of hidden layers with any number of nodes in every layer) and got it solving the XOR problem efficiently. I moved on to trying to see if I could train my network on how to classify a number…

### Creating new images from existing images

Are there any tools online that can help generate new images from existing images? These new images would be flips, rotations, translations, etc. of the original image.

### Regression using neural network

I’d like to ask for any kind of assistance regarding the following problem: I was given the following training data: 100 numbers, each one is a parameter, they together define a number X(also given).This is one instance,I have 20 000 instances for training.Next, I have 5000 lines given, each containing the 100 numbers as parameters.My…

### What makes GAN or VAE better at image generation than NN that directly maps inputs to images

Say a simple neural network’s input is a collection of tags (encoded in some way), and the output is an image that corresponds to those tags. Say this network consists of some dense layers and some reverse (transpose) convolution layers. What is the disadvantage of this network, that directs people to invent fairly complicated things…

### dropout versus less units in convolutionnal network

I m wondering why dropout is favored compared to reducing the number of units in hidden layers for convolutional network. If large set of units leads to overfitting and droping out “average” the response units, why not just suppress units? I have read different questions/answers on the dropout topic including these interesting one but did…

### Using U-NET for image semantic segmentation

If it is not the right place to ask this question, please tell me and I move it to the right place. I’m getting literally crazy trying to understand how U-NET works. Maybe it is very easy but I’m stuck (and I have a terrible headache). So, I need your help. I’m going to segment…

### optical chemical structure recognition from images

I want to recognize name of the chemical structure from the image of chemical structure, like in the given image it is benzene structure I want to recognize that it is benzene from the image(I should be able to recognize all these structures as benzene) What way I could follow to achieve this? (like this…