Excited to share the first preprint from the @IBMResearch Paris team. We present a differentiable rule induction algorithm, integrating the interpretability of rule learning with a neural network based prediction model. @IBM_France arxiv.org/abs/2201.06515

Last Friday I had the honour of seeing @GJ_Both succesfully defend his PhD. I am very grateful for all the wonderful work he did during the last three years. It was a pleasure working together.

Our team at @IBMResearch is hiring a senior research scientist (Paris-Saclay) to work on safe, explainable and responsible AI systems. If you have 1+ years of research experience after PhD and are excited to join the team, get in touch! tinyurl.com/47rz2rdd

After three fantastic years at @CriResearch I am happy to start a new adventure at @IBMResearch where I will continue to work on interpretable and explainable machine learning

New paper is out on performing automated model discovery using DeepMoD when very few sensors are available to collect the data. Paper will be presented at the ICLR workshop on AI for Modeling Oceans and Climate Change, May 7th!

The next round for research fellows @CriResearch is open again. Join this fantastic place to do research in the center of Paris!

Interesting read on how AI research shifted from theoretical science to an experimental science but lacking a proper methodology. In my own experience, the majority of papers still don’t share their code/data resulting in an avoidable replication crisis: bit.ly/2IyiW9s

Trying to discover interpretable models from data: Our team just released a modular package for deep learning-driven model discovery of PDEs and ODEs: DeePyMoD. Paper, code, and documentation is available on: github.com/PhIMaL/DeePyMoD arxiv.org/abs/2011.04336 @GJ_Both @criparis