Hello everyone,

I’m looking for papers/reports/theses that use ML/DL algorithms to tune hyperparameters of an optimization problem. It could even be for another ML/DL algorithm. If there’s such anything, please cite them here.

Thank you, all.

Here is the case, I get news from several news sources every minute. Basically, they are WordPress post, as the script we are using for news aggregator is based on WordPress Plugin.
Now, we are fetching those post to Laravel site via one of those WordPress to Laravel(https://github.com/corcel/corcel).

So far, I’m using TextRank(https://github.com/DavidBelicza/PHP-Science-TextRank), we can do following for any posts:
Find sentences,
Remove stopwords,
Create integer values by find and count the matching words,
Change the integer values by the related words’ integer values,
Normalize values to create scores,
Order by scores
To be more precise, we can get a bag of words from any WordPress Post.
Now, I am looking for perfect algorithms, in this case, that will be able to cluster/ group lists of articles into the same Coverage table. Coverage can have any data, what I think is we need coverage ID field, and a field that accepts an array of post ID that is similar to each other and has the same Coverage ID.
We also have a table called newsTag, that has the following field: postId, most important topic mentioned. You can ignore the topic mentioned because, it depends on only the topic that is a category, so if we cluster based on a topic mentioned from newsTag, we will be limiting clustering ability because in some post there is no topic mentioned.
I’ve looked up a few algorithms like TF-LDF, cosine similarity, k- means, etc. But I am not sure which fits perfectly in this case, basically, a dynamic algorithm that doesn’t depend on a number of articles, so we can clustering new articles in real-time. Thank you for reading, appreciate any kind of help!

Here’s an article for beginners on how I deployed my machine learning model using Flask and Gunicorn on AWS. Check out the GitHub repo for the same. I would love to receive any recommendations and reviews. Check out the web app at bit.ly/stylizeapp

It is based on Artistic Neural Style Transfer, the paper by Johnson. The Gatys implementation is given here.

I made a repository which explores 2 methods (Gatys, 2015 and Johnson, 2016) of Neural Style Transfer. I’ve included a link to Google Collab in the repository, making it easy to run the code on your own images.

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I would love feedback on the repository, as well as any suggestions for future reading. I find this topic fascinating.

Do model documentations ever get in the way of your work? or are they easy for you?

the typical things I try to find out when reading dense documentation

-What is the input, output?

-How I should train the model?

-Will it work on my dataset?

Anything else you try to gather when understanding model documentation?

I am trying to perform material segmentation (essentially semantic segmentation with respect to materials) on street-view imagery. My datasets only has ground truth for select regions, so not all pixels have a label, and I calculate loss and metrics only within these ground truth regions. I use Semantic FPN (with the ResNet-50 backbone pre-trained on ImageNet), a learning rate of 0.001, momentum of 0.8, and learning rate is divided by 4 if there is no validations loss improvement after three epochs. My loss function is a per-pixel multiclass cross-entropy loss.

My dataset is extremely limited. Not only are not all pixels classified, I also only have 700 images and a severe class imbalance. I tried tackling this imbalance through loss class weighting (based on the number of ground truth pixels for each respective class, i.e. their area sizes), but it barely helps. I also possess, for every image, a depth map, which I (can) supply as a fourth channel to the input layer.

A table of results

Visualizations of images trained only on RGB

Visualizations of images trained on RGBD

Visualizations of images trained only on RGB, but with class loss weighting

Visualizations of images trained only RGBD, and with class loss weighting

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Performance is pretty crappy. What’s more, there is very little difference between results of my four experiments. Why is this? I would expect that the addition of depth information (which encodes surface normals and perhaps texture information; pretty discriminitive information). Besides the overall metrics being rather low, the predictions are very messy, and the networks rarely, if ever, predicts “small” classes (in terms of area size), e.g. plastic or gravel. This is to be expected with such a small amount of data, but I was wondering if there are any “performance hacks” that can boost my network, or if I am missing any obvious stuff? Or is data likely the only bottleneck here? Any suggestions are greatly appreciated!

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PS. I also tried a simple ResNet-50 FCN (I simply upsample ResNet’s output until I have the same resolution; there aren’t even skip connections), and the results are worse, but at least they are smooth. Why are these more smooth?

UPDATE: Last time I got the advice to use (generalized) dice loss, which is specifically designed to combat class imbalance in semantic segmentation problems. However, in my case, the opposite happens. Why? I do not use the per-class weights, which in the paper is calculated as the inverse of the squared area of the class’ ground truth. Even if I just use the inverse of the unsquared area, I just get a loss of 1 all the time. This is because the ratio of nominator to denominator becomes too small. I can’t wrap my head around why that is. I also posted this question more thoroughly to StackExchange. I am quite at a loss at what else to do to improve my results. I thought depth of my network might be a bottleneck? I now use ResNet50, and have trouble implementing deeper networks. Any advice is greatly appreciated!

I’m studying Information Science & Computational Linguistics in Germany. We’re doing a master thesis on the prevention of cyberbullying and hate speech in online communication tools and social media.In order to get real life data to test the algorithm, we implemented a chat room:
http://bullychat.inf-d.de

You can send messages, which will then be analyzed by our algorithm. You’ll see whether the message was deemed appropriate and you can approve or disapprove the result. More instructions in the link!
You don’t have to reveal any personal information and all data will purely be used for scientific purposes and will not be made public.

We’d be grateful for every participant! Just write about anything and try different ways to insult. Thank you!

Hey,

So I am taking CS231N online currently. I saw that Stanford also released the videos for the CS230 and CS224N classes. I intend to take the CS224N class but looking through CS230, I was wondering what the adding value of it is if I take CS224N and CS231N. Is there material there covered that isn’t covered in either lecture series or does it simply rehash a lot of the concepts of the other two classes?