machine learning posts

Our research in 2016: personal scientific highlights

Year 2016 has been productive for science in my team. Here are some personal highlights: bridging artificial intelligence tools to human cognition, markers of neuropsychiatric conditions from brain activity at rest, algorithmic speedups for matrix factorization on huge datasets…


Artificial-intelligence convolutional networks map well the human visual system

Eickenberg et ...

Data science instrumenting social media for advertising is responsible for todays politics

To my friends developing data science for the social media, marketing, and advertising industries,

It is time to accept that we have our share of responsibility in the outcome of the US elections and the vote on Brexit. We are not creating the society that we would like. Facebook, Twitter ...

Of software and Science. Reproducible science: what, why, and how

At MLOSS 15 we brainstormed on reproducible science, discussing why we care about software in computer science. Here is a summary blending notes from the discussions with my opinion.

“Without engineering, science is not more than philosophy”    —   the community

How do we enable better Science? Why do we do software ...

MLOSS 2015: wising up to building open-source machine learning

Note

The 2015 edition of the machine learning open source software (MLOSS) workshop was full of very mature discussions that I strive to report here.

I give links to the videos. Some machine-learning researchers have great thoughts about growing communities of coders, about code as a process and a deliverable ...

MLOSS: machine learning open source software workshop @ ICML 2015

Note

This year again we will have an exciting workshop on the leading-edge machine-learning open-source software. This subject is central to many, because software is how we propagate, reuse, and apply progress in machine learning.

Want to present a project? The deadline for the call for papers is Apr 28th ...

Job offer: working on open source data processing in Python

We, Parietal team at INRIA, are recruiting software developers to work on open source machine learning and neuroimaging software in Python.

In general, we are looking for people who:

  • have a mathematical mindset,
  • are curious about data (ie like looking at data and understanding it)
  • have an affinity for problem-solving ...

PRNI 2016: call for organization

The steering committee of PRNI (Pattern Recognition for NeuroImaging) is opening a call for bid to organize the conference in June 2016, in Europe

Scikit-learn 2014 sprint: a report

A week ago, the 2014 edition of the scikit-learn sprint was held in Paris. This was the third time that we held an internation sprint and it was hugely productive, and great fun, as always.

Great people and great venues

We had a mix of core contributors and newcomers, which ...

Scikit-learn 0.15 release: highlights

We have just released the 0.15 version of scikit-learn. Hurray!! Thanks to all involved.

A long development stretch

It’s been a while since the last release of scikit-learn. So a lot has happened. Exactly 2611 commits according my count. Quite clearly, we have more and more existing code ...

Google summer of code projects for scikit-learn

I’d like to welcome the four students that were accepted for the GSoC this year:

Welcome to all of you. Your submissions were excellent, and you demonstrated a good will ...