As the title entails; how may I find researchers / engineers / students (MSc or PhD) to collaborate with on a project outside of the sphere of my own university? Is there any community where experts are able to network and form collaborations? I’m super eager to do some research on the side.

Already been offered to be involved with several projects at my university, but none of them interest me (mainly been related to either utilizing geometric DL for glaucoma or U-net for lung carcinoma) – and so I wish to look beyond this city.

More information, if relevant:

• My domain expertise lies in healthcare/medicine, education, and gaming industry – but I am open to exploring other domains.
• Since I know some people care; my university is ranked worldwide top <40 in engineering. My master’s degree is in machine learning, with a bachelor’s degree in medical engineering (I can verify this).
• Due to affiliation with my universty, I am eligible to publish to arXiv without an endorsement from a registered author.
• I work as a machine learning consultant and data science instructor alongside my studies.
• I’m very familiar with other technologies, such as Kafka and its ecosystem, Flutter, and more.

To clarify; I am looking for, in particular, people that already know a lot of machine learning who wish to collaborate on a research project – whether it’d be deep reinforcement learning, bayesian deep learning, or whatever else. Hence why I didn’t feel like this would be appropriate to post at /r/learnmachinelearning

I think I must be misunderstanding how backtranslation, because I’m not seeing how this could help. I’ll describe my current understanding then I’ll ask my question.

​

The usual setup is that you have some some small set B of parallel data between a source and target language. Your goal is to make a model that a language in the source language and produced the translated version in the target language.

​

In addition to the small dataset B, you also have some potentially very large corpus A of monolingual data in the target language. In order to leverage this data, you train a model in the reverse direction i.e target to source, by using B with the entries flipped. Then you use this model to make A’, which consists of the translations of entries in A by using the reverse model. Finally, you add A’ to B, get some final set C which you then train source –> target model.

In some sense, this should only help if your target –> source model is good. However, you trained this model only on B. This raises the following questions:

​

1) if you can build a good target –> source model from just B, why can’t you do the same with source –> target?

​

2) If you do get some improvements, why can’t you continue this process again? i.e. Train the source –> target model using C, then grab some large monolingual corpus from the source language, backtranslate that to make some new set A”, then add A” to C and re-train the target –> source model then make more source –> target examples by backtranslating the new model? Rise and repeat till you run out of compute.

​

Finally, is there a good reference for this kind of stuff? Most papers which use backtranslation are extremely vague about it.

Hi all,

I’m launching Curated Papers, for the first time in this subreddit!

It’s a website that let you organize lists of academic papers, share curated lists and discover lists made by others.

Think that you need to get into a new field of study… so instead of manually researching what papers to read, going over references, juggling papers back and forth (research that can take a long time), you could instead discover a curated list on the subject, made by a researcher coming from this field.

Of course that it will not entirely replace your need to search for papers, but at least you’ll start with a good basis moving forward.

It’s also some sort of a social network built around curated lists and academic papers, you can for example, like, discuss and follow curated lists, academic papers, or other users and stay up to date with your interests.

I’ll be happy to get your feedback, did you like it? do you have any feature request?

Thanks!

Link to new announcement

https://twitter.com/TVGuestpert/status/1177003728664088576

Link to new deleted old announcement with tweet detailing Siraj’s behavior

https://twitter.com/ieatcatfood99/status/1177070520585449473

There seems to be no reason this is not possible. There’s a reason I think it’s good not only in concept but also in performance. It is not a good performance that is required of the models that consist of RF, but RF requires the models to be overfitting as possible and to be different from each other as possible. This seems to be possible enough for DL for tabular data.

The nice thing about this is that the model could deal with recursion on tabular data. DL is weak at processing tabular data and tree-based models cannot handle recursion. So it would be nice to be good at both.

I looked for a while but couldn’t find anything about this. I wonder if there’s anything I couldn’t find…

Dear Reddit-ML community,

In the imagenet dataset, ( classes 445 -n02892767- [’bikini, two-piece’] and 459- n02837789- [’brassiere, bra, bandeau’]) there are many images that are verifiably pornographic (you can see the porn-star’s webpage in the pic!), shot in a non-consensual setting, voyeuristic and also entail underage nudity (See collage here).This has deep ramifications not just in the legal realm for downloading and storing these images, but also has a trickle down effect with regards to the models trained on this dataset. Ex: If you are an artist making/selling neural art, the unethical nature of the seed images could sully the sanctity of the art (See: https://openreview.net/forum?id=HJlrwcP9DB )

The question now is: What’s the best path forward? Image deletion and replacement? Do chime in with your thoughts!

PS: I had written to the creators of the dataset (waay before the ImageNet Roulette thingy), but received no replies.

https://openreview.net/forum?id=HJedXaEtvS

The researchers propose a method of training neural networks that can later be edited to correct mistakes. In other words, if your network says something stupid or inappropriate, you’ll be able to make it right quickly.

So, I originally posted this on StackOverflow, but I was told that my question was “too broad” and my thread was closed.

I’m working on replicating the research done in this paper.

I have a pandas DF which looks like this:

Date In1 In2 In3 ... Out Day1 -1 1 -1 -1 Day2 1 -1 1 1 Day3 -1 1 -1 -1 Day4 -1 1 1 1 Day5 1 1 1 1 ... 

Now, I’ve already done what they did in the paper. Which is to say, I’ve trained multiple models in scikit to predict "Out" based on all the feature columns "In1", ..., "In10".

However, these are daily predictions and I wanna see what would happen if I make weekly predictions.

Essentially, I want to use df.loc[Day1:Day5, In1:In10] to predict df.loc[Day5, "Out"].

Of course, "Out" would be redefined as cumulative returns over the last 5 days, rather than what it currently is i.e. daily returns.

The problem is, I have absolutely no idea how to go about making a single prediction with N samples. (in this case 5)

My X_train/X_test are DataFrames with the "Out" column dropped & my y_train/y_test is a Series of the "Out" column. I prefer this because I’m not entirely comfortable with arrays.

Is there a way to make scikit use N samples for a single prediction?