Arnaud Joly

Speech scientist @ Amazon // PhD in #machinelearning // #scikit-learn core contributor // clusterlib maintainer // Tree lover

[Florent F. Garcin @ffgarcin]

.@glouppe talking about #Bayesian optimization with #scikit at @PictetGroup

[Mathieu Blondel @mblondel_ml]

Praise for the L-BFGS based implementation of MLPClassifier in scikit-learn https://t.co/18nsMRWLqY

[Damien ERNST @DamienERNST1]

[Please RT] Open positions in computational energy policy and planning at the @UniversiteLiege

https://t.co/2fyiRr6gOz

[Andreas Mueller @amuellerml]

We just released scikit-learn 0.19. Update with pip or conda. A big thank you to everybody who contributed! https://t.co/rkytflTGu3

[Arnaud Joly @JolyArnaud]

Machines are learning to read and to reason. #sigir2017

[Arnaud Joly @JolyArnaud]

I have seen Godzilla in Tokyo. Be aware!

[Arnaud Joly @JolyArnaud]

@glouppe @joeri_hermans Congratulation! Have fun!

[Arnaud Joly @JolyArnaud]

@glouppe Thanks!

[Gilles Louppe @glouppe]

Context-dependent feature analysis with random forests https://t.co/DAJSI1f5u0 Nice follow up by @Antho401 of my earlier paper on VIs in RFs

[Mathieu Blondel @mblondel_ml]

Our #icml2016 paper on polynomial networks and factorization machines is now online https://t.co/N8ztuRMWvn

[Jake VanderPlas @jakevdp]

"if you need a month to clean up your code, why do you trust your results?" #NYURepro

[Vincent François-Lavet @VinFL]

Now inverted pendulum & mountain car in the DeeR framework with @open_ai Gym. Really easy! https://t.co/94gm7pDZJs

[Vincent François-Lavet @VinFL]

I've just implemented the Double Q-learning in the deer framework! Coming soon : "Prioritized experience replay"... https://t.co/ldDZoBYfOL

[Andrej Karpathy @karpathy]

Announcing OpenAI Gym: a toolkit for standardizing RL environments and evaluation https://t.co/SCQiWHFquC +blog post https://t.co/tKyp7Ftnqw

[Ben Hamner @benhamner]

Most AI breakthroughs constrained by high quality datasets, not algorithms https://t.co/I8iBhvaTTk

[Vincent François-Lavet @VinFL]

New Toy environment example for the deep Q learning framework : https://t.co/Dm2I03MOyF

[Arnaud Joly @JolyArnaud]

Deep learning + reinforcement learning in python
https://t.co/nDqoDeApdS

[Vincent François-Lavet @VinFL]

New paper for deep reinforcement learning. Improved performances thanks to an adaptive discount factor : https://t.co/CJNI7pA4YM #NIPS2015

[NeuroDebian Project @NeuroDebian]

Fresh #sklearn (0.17.0) is available across #NeuroDebian's

[Andreas Mueller @amuellerml]

Scikit-learn 0.17 is out via pip and conda! changelog: https://t.co/G1JFjUMIOP LDA topic models, faster NMF, faster trees, faster T-SNE, ...