How did everyone do?

Researchers seem to have agreed that the parasitic business model of for-profit journals have no place in ML, yet Nature Machine Intelligence continues to attract submissions. For example, just today articles appeared by many distinguished researchers including Cynthia Rudin and Roland Eils: https://www.nature.com/natmachintell/research.

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What can we do about this, beyond simply not submitting?

The inspiration to this question came from this question

https://www.reddit.com/r/MachineLearning/comments/bj5pbd/d_is_it_ok_to_be_working_in_multiple_research/

I didn’t know you could just do that. Reach out to a research group and ask to collaborate.

Does anyone have any advice on how to go about doing this? Or how to go about locating which groups would be best to collaborate with? Are they mostly just CS departments at universities? Or are there other groups as well?

My colleagues and I are developing an NLP tool for an enterprise customer. In production usage, the tool is going to be applied to a large stream of newswire documents. One current problem is that our training and testing datasets are static, i.e. based on a document sample that doesn’t change over time. With time passing, it might be that the tool’s output in production gradually becomes irrelevant but no one gets alerted about this.

Could anyone here share any experience on continuous, longitudinal quality monitoring for NLP (and, more broadly, ML)? Assuming worse-than-human performance of the model, are there any better options than just to have a dedicated person who would regularly label a sample of recent predictions and raise a flag if anything goes wrong?

I was looking for a python implementation of general exponential family PCA (binomial in particular) but I couldn’t find anything. Maybe I was using the wrong search terms. Sklearn has a bunch of PCA variants but none seem to be what I’m looking for.

Does anyone know of an implementation?

Thanks.

Dataset Website: https://textvqa.org

Challenge Link: https://evalai.cloudcv.org/web/challenges/challenge-page/244/overview

Prize: $10k GCP Credits

Starter Code: https://github.com/facebookresearch/pythia

Paper: https://arxiv.org/abs/1904.08920

Deadline: 18th May (ask for extension if needed)

More details on the challenge: https://textvqa.org/challenge

Explore the dataset: https://textvqa.org/explore

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Detailed Description:Current state-of-the-art VQA models are unable to read and reason about text in images which in contrast is most asked by the users of such systems. TextVQA aims to provide a benchmark for measuring progress of VQA models on text reading and reasoning capabilities.

State-of-the-art VQA models on TextVQA are only around 14% while the human accuracy is ~85%. LoRRA module introduced in TextVQA paper can be attached to any VQA model to add text reading and reasoning capabilities. The current state-of-the-art on TextVQA is ~27% with LoRRA.

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Use the starter code to participate in challenge to win $10k GCP credits and help close this large gap.

Im searching for a finished model/program to generate css from html files really interested if it exists already 🙂 (if it doesnt imma try to make it myself hopefully its not as hard as i think lol)

As the title says, I have a friend who really enjoys machine learning. He’s the type to nerd out over anything that involves complex math or programming, so he really likes machine learning.

I was thinking of getting him a book or maybe access to a course or something machine learning related. We’ve taken an introductory machine learning class, so he has a basic idea of the different models out there and how to use them. I’m looking for something a little bit more advanced that will maybe wrinkle his brain a little but will also be really cool to go through. He’s also thinking of going to grad school soon to study machine learning.

Any suggestions?