For one of my course works I’m supposed to find a scientific paper from my field of research that involves an optimisation problem solution based on the Karush-Kuhn-Tucker conditions.

My research field is computer vision and more specifically ocular biometrics. I’ve found this paper from the general field of deep learning, but haven’t been able to find anything from a more specific field. Is anyone aware of any such paper from biometrics or at least CV-based identity recognition in general?

Hi,

Vision and Language Group, a deep learning group at IIT Roorkee, has written summaries for various NeurIPS 2019 papers:

Uniform convergence may be unable to explain generalization in deep learning:

https://github.com/vlgiitr/papers_we_read/blob/master/summaries/uniform_convergence.md

This Looks Like That: Deep Learning for Interpretable Image Recognition:

https://github.com/vlgiitr/papers_we_read/blob/master/summaries/this_looks_like_that.md

vGraph: A Generative Model for Joint Community Detection and Node Representational Learning:

https://github.com/vlgiitr/papers_we_read/blob/master/summaries/vgraph.md

If you found the summaries useful do star the repo. Also more NeurIPS paper summaries will soon be updated 🙂

The quickest way to get started is using this Colab notebook – you can find the source code & more detail in the GitHub repo.

The project provides datasets, pre-fine-tuned models and uses the excellent huggingface/transformers library.

Is anyone of you interested in doing a project involving abstract art + deep learning. I want to achieve something as the work I have done but with machine intelligence. For me intelligence has always been identifying abstract aesthetics in something. I wonder can a neural net implicitly understand aesthetics like say symmetry or colors. Got any ideas?

You can find the full video https://www.youtube.com/watch?v=-KoBZiA5cSY

I am currently asking people to fill out a survey for me to wonder how many people get stressed in work, Many replies for be grate as I need to collect this information and write it into my project.

Survey: https://docs.google.com/forms/d/e/1FAIpQLSdarBNNJayXyI1fuStmhbQUHfiA6vFYw9-BWQbxlJX4nEWVUQ/viewform?usp=sf_link

Hello all,

i got a simple question about supervised learning using hidden feature from deep-generative model.

Assume that we have to generate a MNIST while also classifying it, then VAE might be an go-to option because it solves MLE problem. Reducing mode-covering metric=KL(P_real || P_fake); Bleeds probability mass to wherever data is, makes easy to discriminate a feature although it generates blurred images.

But GAN solves minimax problem; It may be fallen in mode-collapse, or capture more sharp-density distribution than VAE. I think it may harm the performance of classification. My question may be boiled-down into ‘How to make GAN’s hidden feature more interpretable?’

– Is it true that GAN suffers from classification problem?

– Then is there any good-idea about solving classification combined with GAN?

– Maybe preparing more complex classifier is an option to matching highly non-linear feature manifold into class labels.

– I hardly found out some breaking-through papers dealing with such GAN-classification problems.

Any discussion, advice, recommendation on papers are very appreciated in advance.

Hello all,

i got a simple question about supervised learning using hidden feature from deep-generative model.

Assume that we have to generate a MNIST while also classifying it, then VAE might be an go-to option because it solves MLE problem. Reducing mode-covering metric=KL(P_real || P_fake); Bleeds probability mass to wherever data is, makes easy to discriminate a feature although it generates blurred images.

But GAN solves minimax problem; It may be fallen in mode-collapse, or capture more sharp-density distribution than VAE. I think it may harm the performance of classification. My question may be boiled-down into ‘How to make GAN’s hidden feature more interpretable?’

– Is it true that GAN suffers from classification problem?

– Then is there any good-idea about solving classification combined with GAN?

– Maybe preparing more complex classifier is an option to matching highly non-linear feature manifold into class labels.

– I hardly found out some breaking-through papers dealing with such GAN-classification problems.

Any discussion, advice, recommendation on papers are very appreciated in advance.

I’m interested in creating a neural network to generate Hip Hop tracks (no lyrics) and I have some questions:

Should I use a GAN model? Will this require hardcore GPUs or will I be able to do this on my fairly powerful laptop? Any recommended readings or projects I can adapt from?

I have experimented with Neural Networks using Keras in Python on my laptop.

Thank you.