wwydmanski @wwydmanski@qoto.org

The original comparison of Adam vs AdamW is quite memorable.

On a related note, I absolutely recommend reading through reviews of that publication
https://openreview.net/forum?id=rk6qdGgCZ

#MachineLearning #DataScience

The deadline has been extended to 20th May.
Help @US_FDA in doing what comes after #drugdiscovery - keeping track of the drug safety.

Design an #NLP algorithm that will relieve humans of this task, submit it to CAMDA, and let's see each other on @iscb's ISMB!
#MachineLearning
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RT @CAMDA_conf
Validation Leaderboards now live! Disrupt #DrugDevelopment by Literature #AI for #DILI: Build a digital twin of @US_FDA experts and identify r…
https://twitter.com/CAMDA_conf/status/1524379583658827776

Together with our friends from #FDA and David Kreil’s and Sepp Hochreiter's IARAI we've been working on an #NLP-oriented #challenge that is focused on tracking safety of the drug after clinical trials.
Consider a doctor that wants to help their patient through pharmacological treatment. Even if it doesn't cause any immediate adverse reaction, it still involves a major, long-term risk - #DILI, drug-induced liver injury.
Even though the #toxicity of the drugs is modelled and tested thoroughly during clinical trials, this method isn't 100% accurate and the danger related to them might be observed only later.
Right now, there is only one way of guarding against it in a real world scenario - constantly reading through publications regarding the drugs that you want to prescribe. Of course, there are many information banks which contain and aggregate this information, but they are still human-curated - which means that this problem isn't solved, it's just dumped on somebody else.
We want the participants to prove once and for all that humans can be relieved if this task.
The final, validation round has just began! You still have time until 16.05 to submit your results to ISMB 2022.

https://bipress.boku.ac.at/camda2022/
If you know anyone who might be interested, it'd be fantastic if you could spread the word.
#machinelearning

Together with FDA and @HochreiterSepp's IARAI we've been working on an #NLP-oriented challenge that is focused on tracking safety of the drug after clinical trials.
Submit before 16th may to join us at @CAMDA_conf 2022 in Wisconsin!
https://bipress.boku.ac.at/camda2022/

#MachineLearning

Local random walk is a basis for many #MachineLearning algorithms. However, there is an interesting alternative - maximum entropy random walk.

Overview from TFML by J.Duda: https://tfml.gmum.net/assets/files/2015/Duda.pdf

Application in medical imaging by Galinsky&Frank: https://par.nsf.gov/servlets/purl/10063685