Hi all,

/r/machinelearning is growing rampantly, with over a thousand new subscribers every day. As our community grows, it is important to have fertile ground for newcomers to learn the ropes. Since there is already an active subreddit for aiding in the development of machine learning skills, we feel that this is the right time to demarcate the content between these two subs.

As a new rule, all beginner-level content should be posted to our sister sub, /r/learnmachinelearning. This will free up “real estate” on our page for more in-depth, expert discussions and provide a more focused learning space for beginners. That’s not to say that all tutorials are outright banned — in particular, explanations of recent or niche papers are still welcome.

We were all beginners once and newcomers to ML are bringing great things to this sub and the general community. Please do continue to engage with and learn from the community here. But we recommend /r/learnmachinelearning if you do want to start getting your hands dirty.

We hope that this specialization will be beneficial to everyone in the long run.

Best regards, the moderator team

I received some negative feedback on a recent paper submission stating that my results would not be reproducible. However, I took care to only use easily accessible, public benchmark data and wrote code to make it easy for anyone to reproduce my results. I did remove the GitHub link to the code in my submission to preserve anonymity (stating in the submission that this was the reason for not providing a functioning link). What is the best way to handle this in the future without compromising double or triple blind reviewing? Especially if the code should remain private until acceptance.

I’m talking about architectures such as AlexNet, Inception and object detectors like YOLO, SSD. I’ve read a bit online and I’m really confused how they work together.
Lets say I want to detect a specific object/person from a video and put a box around them with a label describing the state of that object/person. How would that work? What would be steps taken by the object detector and feature extractor? A workflow for this would be really helpful.

I am trying to understand the negative consequences of oversampling in the context of image classification. If I am using a decent amount amount of image transformations, I believe it will effectively be equivalent to SMOTE for tabular data, since I am not exactly repeating any image in a batch. Does the behaviour and test set accuracy of a classifier in any way depend on the actual class distribution in the train set and by oversampling am I doing any harm?

To take an example I was training a classifier on a dataset with 5 classes, having heavy class imbalance. To balance it out I oversampled the minority classes so that all classes have equal number of images. This caused a significant performance drop on the test set that I have, while cross validation performance was fairly high on the oversampled set. When analysing the class distributions I saw that for the original train set the distribution was: 1,3,2,4,5 with decreasing number of samples. The test set has a class distribution 3,1,2,4,5 but the predictions after training on oversampled data have distribution 4,3,2,5,1. Mathematically speaking, how can this behaviour of over predicting a less frequently occuring class be explained?

Is there maybe some survey paper that summarizes the different architectures that are used wiedly for word based text recognition/classification? Or can you recommend somethong or are there some must-reads? Thanks!

The main reasons for creating the service of Machine Learning models inheritance visualization are described in this article.

Other reasons are not so obvious, and will be described later.

https://arxiv.org/pdf/1908.01874.pdf

Been primarily working in a different field of DL for a while, but got a project coming up related to NLP. I’ve done some research though the most frequent ones that seem to be showing up are GPT-2, BERT, and ELMo. However, I am under the impression that these are burying others that may be better suited for the task.

If it’s of relevance; my domain expertise is in medicine, and intend to use it for medical purposes.

A step-by-step tutorial on how to implement and adapt simple language model to Wikipedia text.

A pre-trained BERT, XLNET is publicly available ! But, for NLP beginners, It could be hard to use/adapt after full understanding. For them, I covered whole, end-to-end implementation process for language modeling, using recurrent network, we already know.

I hope that this repo can be a good solution for people who want to have their own language model 🙂

https://github.com/lyeoni/pretraining-for-language-understanding