Hey, we at deepset.ai have been working very hard on an open source framework for Transfer Learning in NLP. It’s called FARM and the core idea is that we separate out the language model component (takes tokens, returns vectors) from the prediction head part (uses these vectors to make meaningful predictions). We currently have support for Text Classification, NER, Language Model Finetuning and Question Answering. Feel free to direct questions and suggestions to me. If this seems like something that you’re interested in, leave us a star on github!

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https://github.com/deepset-ai/FARM

Hi,

this is Florian from edaviz and we are currently thinking about working on bamboolib.

Please check out the short preview video and let us know what you think:

https://youtu.be/yM-j5bY6cHw

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The main benefits of bamboolib will be:

• you can manipulate your pandas df via a user interface within your Jupyter Notebook
• you get immediate feedback on all your data transformations
• you can stop googling for pandas commands
• you can export the Python pandas code of your manipulations

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What is your opinion about the library?

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Thank you for your feedback,

Florian

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PS: if you want to get updates about bamboolib, join our mailing list which is linked on the github repo

https://github.com/tkrabel/bamboolib

There are a lot of ‘SOTA’ lists but they usually include a few NLP, CV, and RL results and call it a day. However, there are a ton of papers that beat the SOTA on a variety of silly stuff.

Is there an easy way to find a lot of SOTAs to try to beat the simplest ones for practice/fun?

Hi everyone,

It’s easy to find documentation on single or multi classification and single variable regression, but when it comes to multiple regressions, I find it much harder.

If one wants to predict multiple variables (that we suppose not independent), like the consumption of a set of articles (given historical data etc…). So for each article we have, we want to predict its consumption.

What would be the most common way to handle this? What is the actual preferred approach ? For basic ML, I’m used to XGBoost and LightGBM but those two are not supporting multiple variable prediction yet. Regarding DL, I believe this would ask a tremendous amount of data (the more article we have).

Are there any papers talking about this subject? Or any piece of work that would help?

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The training data can be seen as such (and we want to predict art1, art2 etc…) :

Song: https://twitter.com/ai_antihype/status/1155091710281580548?s=20

Bonus song: https://twitter.com/ai_antihype/status/1155220318731919360

Is there a resource to find the best model for a particular task?

What’s the best way to do so? As of now, I usually rely on my uni profs or seniors..

Am I the only one who finds it hard?

How do you folks go about this? What has worked/not worked?

I have seen that they have had impressive results for general CS/coding programs.

I am wondering how they have been doing in ML. I only know of ShapestMinds who is doing this, and I am think InsightAI does this as well.

I’m a third-year undergraduate student in CS, focusing in ML and I’ve been reading a few papers on GANs. I was hoping someone could show me relevant papers on GAN architectures.

In particular, I have the idea that you can create more complicated GAN systems and perhaps get better results. AFAIK, most GANs are structured as a combination of a discriminator and a generator. My thinking is that you could split up the generator into multiple generators and then create an ensemble network, let’s call it a Decider. Ensembles generally train their generators on all available data and then cleverly combine the generators through techniques like boosting. Unlike the traditional ensembles, I want to train the Decider and the multiple generators adversarially. More specifically, the utility function of each generator would seek to maximize the probability it is selected by the Decider. Similarly, the Decider would seek to optimize its correct selection of a generator.

Is anyone aware of multiple adversarial levels in GANs? My google-fu hasn’t been strong enough to find anything related. I’m also wondering if there’s an obvious reason such experiment would fail to produce anything interesting. Any feedback is greatly appreciated.