I have recently read about U-Net and thought that since it is very similar with autoencoders then perhaps I could use it for a project in place of autoencoders for dimension reduction. But I haven’t found any papers that use it for that purpose only segmentation and generative models. Is there a theoretical issue using it for that purpose?

For more details of this release, please go to GitHub release page here

Attention is mostly discussed in the context of sequences, but I was thinking about what it meant in the context of message passing. Clearly when a certain word has “attention” it is relaying long-distance information, but is that synonymous with something like belief propagation?

I am working on a live gesture recognition project. As of now, I used backprojection for segmenting skin and applied cv2.connectedcomponents to identify all the blobs. However, I am not able to isolate the hands. A lot of false predictions come in. Can you please suggest me a better way to go about this project. Thanks jn advance.

• I wonder what the most important advances in my field have been in the past month. I could go on arXiv and search every paper in ML… eff that noise. I’m going to arxiv-sanity and twitter! That’s sane! There’s no way I can miss anything important this way! Every company should track their changes through tweets!
• Hey this paper sounds amazing. The abstract is great. All I need is the main algorithm with every variable clearly described, how it’s different from current techniques, and the results! Oh the algorithm they used is the one not in the box? And it’s described across four pages, and not in order of operations? Brilliant!

Papers are a great storage format for reference, but we’re all in CS. Why are we using the storage format as the information retrieval format?? That’s insane. It’s figuratively like we’re trying to understand code changes, but instead of documentation, we could use diff, but we don’t even do that and we just compare the old files and the new files by eye…

Are there any non-profits working on this? OpenAI, can you become non-sketchy for like 10 seconds again and get this thing hammered out? Anyone?

In my company we do not train word2vec (or Glove or fastext) for each context separately (i.e. we use the same algorithm for movie reviews and for food reviews). as i think meaning changes based on context (go read the book is a good sentiment for a book review but bad one for movie review), I was wondering do you train your embedding algorithm for each context or not.

thanks!

may I ask you a question?

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My lab research topic is CNN, but I want to study Stock time series prediction using RNN.

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All six proposals were rejected in three months …

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In conclusion, I think I should decide whether to study CNN or leave the lab.

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Is it right to study RNN personally while studying CNN?

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Or is it right to see another lab?

Hello! Not having the computational resources to train a seq2seq transformer-based model, I’m trying to do that by fine-tuning BERT as an encoder and GPT2 as a decoder. Has anyone tried something similar? How can I condition GPT2 on the encoder’s output?