From Neural Architecture Search: A Survey, first published in 2018:

“Moreover, common search spaces are also based on predefined building blocks, such as different kinds of convolutions and pooling, but do not allow identifying novel building blocks on this level; going beyond this limitation might substantially increase the power of NAS.”

I wonder now if somebody tried that since this was written or if anybody has some thoughts about the feasability of this idea.

In other words what could we expect to achieve sooner or later if our theoretical work stopped today and we just focused on collecting more data and adding more compute? Are there any types of problems that definitely not wouldn’t be solvable?

If you have any paper discussion this subject, feel free to share them.

Hi, I saw this video: https://www.youtube.com/watch?v=AhEVk7TLVjQ explaining the difference and I’m quite confused.

What is the actual basis for deciding whether to use pre-trained models or learn from scratch? My understanding is, if you don’t have resources you have to use pre-trained models. If you have enough resources you can learn from scratch with your own data.

1- Imputing a bunch of text and getting an output that tries to replicate it as close as possible 2- The same but with images

Hey all,

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Not sure if this is the best place to post this, but I am looking to enhance my portfolio and ML skills more importantly through Kaggle competitions and side projects.

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About me:

cornell grad with coursework experience in ML, did an internship dealing with NLP and CV applications. now working as a data engineer and doing a project dealing with using nlp learning from unstructured data.

currently following along with cs231 and 230 to improve my deep learning skills. Almost done with the andrew ng

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looking for like minded individuals to partake in side projects or kaggle contests with!

In terms of performance, which would be better? The price is about the same

With the 2080Tis, I have the overhead of inter gpu communication, and have 44 GB of memory, 17408 cuda cores total, 2176 tensor cores total, 1545 Mhz speed

With the 2 Titans, I dont have as much overhead i suppose, have 48GB, only 9216 cuda cores total, 1152 tensor cores total, 1770 Mhz speed.

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Which do I go with. Once again, the price is about the same

Hello guys, I am trying to implement this paper. They used restricted boltzmann machines to get evolution of a system. Due to nature of the problem “activation” function is not sigmoid but tanh which is later transformed into probability. Different activation function means that gradient is different, although in the paper they wrote “standard” gradient equations. When I tried to train model with standard gradient equations I couldn’t get any meaningful results.I am aware that RBM minimizes KL divergence, but I couldn’t figure out how to implement that (how to get probability function). After trying to find what should I put for loss function when training with eg. Adam I stumbled upon some random github code which for loss function had:

L = energy(data_vec) – energy(genrated_vec)

(yes without absolute value). Well, I plugged that in and it worked like a charm. When I tried to put absolute value over loss it did not worked. I am having trouble understanding why is this correct loss for RBM and I would appreciate help 🙂

What is sota for image manipulation detection? I have not seen much work in this field happening, does anybody know the reason for that? The only recent paper have seen is from CVPR2018 but I thought there would be more work being done?

Thanks for your help!

I found this :

https://www.fujitsu.com/global/about/resources/news/press-releases/2019/1015-01.html

Looks like it was posted about a month ago, I haven’t really found much more info on it, was trying to see if it was anything groundbreaking, the details provided are slim to none. Just gives a basic overview of how neural networks seem to work, and some fancy terms for normalization/transformations/landmark detection. Thanks.

They make some bold claims:

This technology has achieved a high detection accuracy rate of 81% even with limited training data.

This technology is also more accurate than other existing technologies according to certain facial expression recognition technology benchmarks

Building a Deep Learning home server with 4x 2080 ti blower style GPUs and I am wondering what CPU to get for this machine. The delima is that I am using python Pytorch and Numpy which has a lot of support with Intels MLK packages that sabotage AMD performance. However, I am utilizing the GPUs and CUDA technology for the matrix multiplication and can get away with Numpy Openblas for my environments on the AMD machine. I’m trying to keep this project under $8000 and want the best performance. Also, I am primary using DDPG and DQN networks. What would you do?

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