@WickFelix
Joined May 2014
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In our latest Cyclic Boosting release github.com/Blue-Yonder-OSS/c…, we introduce quantile regression (via pinball loss) and subsequent (requiring 3 predicted quantiles) fit-less estimation of full individual probability distributions by means of quantile-parameterized distributions.

Release Cyclic Boosting 1.2.0 · Blue-Yonder-OSS/cyclic-boosting

enabled optional hierarchical iterations in training process added bin occupancy plots in training analysis plots of one-dimensional features added functionality to estimate full individual probabi...

github.com
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Try it out for your probabilistic, ML-based decision making!
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brief summary of main ML concepts: medium.com/@felixwick83/unde…
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We just open-sourced Cyclic Boosting, a pure-Python ML algorithm that's explainable, accurate, robust, easy to use, and fast! Learn more in our presentation #Cycl… @wickfelix at #PyConDE #PyDataBerlin 2023.pycon.de/program/MYARJG…

Exploring the Power of Cyclic Boosting: A Pure-Python, Explainable, and Efficient ML Method...

We just open-sourced Cyclic Boosting, a pure-Python ML algorithm that's explainable, accurate, robust, easy to use, and fast! Learn more in our presentation #CyclicBoosting #MachineLearning #OpenSo...

2023.pycon.de
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First open-source pre-release (still lots of polishing needed) of the Cyclic Boosting ML algorithms: github.com/Blue-Yonder-OSS/c… Feel free to try it out, simply do: pip install cyclic-boosting Please come back with criticism and suggestions. Contributors highly welcome!

GitHub - Blue-Yonder-OSS/cyclic-boosting: implementation of Cyclic Boosting machine learning...

implementation of Cyclic Boosting machine learning algorithms - Blue-Yonder-OSS/cyclic-boosting

github.com
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A short history of the world in 13 maps & infographics: 1. Everybody alive today compared to everybody who has ever lived
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my recommendation for an off-the-shelf ML algorithm for regression on structured data: scikit-learn.org/stable/modu…

HistGradientBoostingRegressor

Gallery examples: Time-related feature engineering Model Complexity Influence Lagged features for time series forecasting Comparing Random Forests and Histogram Gradient Boosting models Categorical...

scikit-learn.org
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This is legit ninja shit.
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Evolution provided us with moderate math skills, AI to the rescue. deepmind.com/blog/discoverin…
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Just stumbled upon this really nice story from @andrey_kurenkov about the history of neural networks: skynettoday.com/overviews/ne…
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Was it really just symmetry breaking via setting random initial weights what Rosenblatt missed to get backpropagation to work? (as described in Genius Makers)
such a nice example how ML is supercharging the scientific method: nature.com/articles/d41586-0…

What's next for AlphaFold and the AI protein-folding revolution

Nature - DeepMind software that can predict the 3D shape of proteins is already changing biology.

nature.com
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climate change, pandemics, nationalism, nuclear threat: crunchtime for humankind's take on the Fermi paradox these years Will the non-idiots please stand up?
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Temporal Confounding is a profound issue in time series forecasting: sciforum.net/paper/view/1088…
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Double Descent: overparametrize and smooth
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We did this a while ago (admittedly, in a rather explicit way): arxiv.org/abs/2002.03425

Cyclic Boosting -- an explainable supervised machine learning algorithm

Supervised machine learning algorithms have seen spectacular advances and surpassed human level performance in a wide range of specific applications. However, using complex ensemble or deep...

arxiv.org
If I were to give an example of the current state of AI, this is what I would use: openai.com/blog/multimodal-n…
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Could the described Fallacies of Abstraction be a hint that inference by forward-passing is too simple?
The world can be described by maths. Unfortunately, the human mind is weak at it (as evolution focused on other skills). But there might be help: collection of data and pattern recognition by ML. An example: deepmind.com/blog/article/ex…

Exploring the beauty of pure mathematics in novel ways

More than a century ago, Srinivasa Ramanujan shocked the mathematical world with his extraordinary ability to see remarkable patterns in numbers that no one else could see. The self-taught mathemat...

deepmind.google
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The same holds for complex systems.
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