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.

Hi all,

I’m currently interviewing for positions as either research scientist or SWE with a couple of big N companies. I am finishing a PhD in an ML related subject, and what I would really like is to find a job where I can do interesting applied research and maybe publish the occasional paper, but in industry rather than academia. However I have no previous experience with the tech industry and so I am flying a bit blind, applying to companies that have large ML teams and hoping to get lucky.

My question is how do you make it into those interesting teams, is it the same process as for generalist new grad roles? As far as the recruiter at Google and FB told me, if I pass the interview and accept the offer I will be matched with a team and can put down some preferences then, but how much leverage do I really have? Do the majority of PhDs just end up on product related teams? Does it depend on the office, what projects are going on at the moment, and if so how easy is it to transfer to research oriented teams later on?

Thanks!

Hello everyone,

I am trying to know more about AI edge computing and how it can be useful for robotic manipulators in next-generation factories. I hope we can you can share from your insight and experience about such a topic.

Thanks