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This Week in Machine Learning: XTREME, COVID-19 Resource for ML Research and More

Easter is over and we’re all back to our daily tasks. But the world of science hasn’t been resting like we did. Let’s see what’s happened in ML over the past week.

Here goes a dose of the latest news, discoveries, and inspiring stories from the world of Machine Learning. There is something for everyone. Enjoy your read!

Weekly Roundup: April 7th – 13th

> Neptune.ai blog – make sure to visit our blog to find out interesting and in-depth articles on machine learning.

Also, we’ve recently launched a podcast so tune in and enjoy! 🎧

List of COVID-19 Resource for Machine Learning and Data Science Research by Asif Razzaq on Martechpost | April 12

A list of COVID-19 tools and public datasets that can be helpful in understanding the disease and performing data-driven research. Helpful for all those who work with medical ML.

XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalization on Google AI blog | April 13

An interesting read on general-purpose multilingual representations. The article covers 40 typologically diverse languages (spanning 12 language families) and includes nine tasks that collectively require reasoning about different levels of syntax or semantics. For all the fans of NLP.

> Machine Learning and Data Science free online courses to do in quarantine by Gonzalo Ferreiro Volpi on Towards Data Science blog | April 9

As the title suggests, on this list you’ll find free courses. From beginner to advanced. Happy learning!

> DARPA snags Intel to lead its machine learning security tech by Zack Whittaker on TechCrunch | April 9
Chip maker Intel has been chosen to lead a new initiative led by the U.S. military’s research wing, DARPA, aimed at improving cyber-defenses against deception attacks on machine learning models.

> KDnuggets™ News of the week with top stories and tweets of the past week, plus opinions, tutorials, events, webinars, meetings, and jobs.

> Old but gold, the reliable Reddit thread on ML for more news on machine learning


That’s all folks! I hope you found something of interest in this weekly roundup. Don’t forget to check our blog for more inspiring articles.

👉 Came across an interesting ML article? Or maybe you wrote one yourself and would like to share it with other people? Let us know, we’ll spread the news in our weekly roundup!


READ NEXT

ML Experiment Tracking: What It Is, Why It Matters, and How to Implement It

10 mins read | Author Jakub Czakon | Updated July 14th, 2021

Let me share a story that I’ve heard too many times.

”… We were developing an ML model with my team, we ran a lot of experiments and got promising results…

…unfortunately, we couldn’t tell exactly what performed best because we forgot to save some model parameters and dataset versions…

…after a few weeks, we weren’t even sure what we have actually tried and we needed to re-run pretty much everything”

– unfortunate ML researcher.

And the truth is, when you develop ML models you will run a lot of experiments.

Those experiments may:

  • use different models and model hyperparameters
  • use different training or evaluation data, 
  • run different code (including this small change that you wanted to test quickly)
  • run the same code in a different environment (not knowing which PyTorch or Tensorflow version was installed)

And as a result, they can produce completely different evaluation metrics. 

Keeping track of all that information can very quickly become really hard. Especially if you want to organize and compare those experiments and feel confident that you know which setup produced the best result.  

This is where ML experiment tracking comes in. 

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