Am Neumarkt 😱

2021-11-16 13:34:23 MLU-Explain - 来自亚马逊工程师 Jared Wilber的交互可视化核心机器学习概念的视觉论文 #visualessay Open / Comment

2021-11-16 11:24:05 #visualization

Nicolas P. Rougier released his book on scientific visualization. He made some aesthetically pleasing figures. And the book is free.

https://github.com/rougier/scientific-visualization-book Open / Comment

2021-11-12 17:14:12 #DS #Visualization

Okay, I'll tell you the reason I wrote this post. It is because xkcd made [this](https://xkcd.com/2537/).

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Choosing proper colormaps for our visualizations is important. It's almost like shooting a photo using your phone. Some phones capture details in every corner, while some phones give us overexposed photos and we get no details in the bright regions.

A proper colormap should make sure we see the details we need to see. To address the importance of colormaps, we use the two examples shown on the website of colorcet[^colorcet]. The two colormaps, hot, and fire, can be found in matplotlib and colorcet, respectively.

I can not post multiple images in one message, please see the full post for the comparisons of the two colormaps. Really, it is amazing. Find the link below:
https://github.com/kausalflow/community/discussions/20


It is clear that "hot" brings in some overexposure. The other colormap, "fire", is a so-called perceptually uniform colormap. More experiments are performed in colorcet. Glasbey et al showed some examples of inspecting different properties using different colormaps[^Glasbey2007].


One of the methods to make sure the colormap shows enough details is to use perceptually uniform colrmaps[^Kovesi2015]. Kovesi provides a method to validate if a color map has uniform perceptual contrast[^Kovesi2015].

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References and links mentioned in this post:

[^colorcet]: Anaconda. colorcet 1.0.0 documentation. [cited 12 Nov 2021]. Available: https://colorcet.holoviz.org/
[^colorcet-github]: holoviz. colorcet/index.ipynb at master · holoviz/colorcet. In: GitHub [Internet]. [cited 12 Nov 2021]. Available: https://github.com/holoviz/colorcet/blob/master/examples/index.ipynb
[^Kovesi2015]: Kovesi P. Good Colour Maps: How to Design Them. arXiv [cs.GR]. 2015. Available: http://arxiv.org/abs/1509.03700
[^Glasbey2007]: Glasbey C, van der Heijden G, Toh VFK, Gray A. Colour displays for categorical images. Color Research & Application. 2007. pp. 304–309. doi:10.1002/col.20327
[^matplotlib-colormaps]: Choosing Colormaps in Matplotlib — Matplotlib 3.4.3 documentation. [cited 12 Nov 2021]. Available: https://matplotlib.org/stable/tutorials/colors/colormaps.html Open / Comment

2021-11-11 15:20:38 #ML #fun

animegan v2! (I stole this animation from reddit. https://www.reddit.com/r/MachineLearning/comments/qo4kp8/r_p_animeganv2_face_portrait_v2/ )

Try it out:

1. Telegram bot (works pretty well): https://t.me/face2stickerbot
2. Dashboard (sometimes it doesn't work): https://huggingface.co/spaces/akhaliq/AnimeGANv2

Code: https://github.com/bryandlee/animegan2-pytorch

Redditors made some funny photos too.
https://www.reddit.com/r/MachineLearning/comments/qo4kp8/r_p_animeganv2_face_portrait_v2/


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This post is also available here: https://community.kausalflow.com/c/ml-applications/animeganv2 Open / Comment

2021-11-10 23:08:17 #ML #news

1. https://ai.googleblog.com/2021/11/model-ensembles-are-faster-than-you.html
2. Wang X, Kondratyuk D, Christiansen E, Kitani KM, Alon Y, Eban E. Wisdom of Committees: An Overlooked Approach To Faster and More Accurate Models. arXiv [cs.CV]. 2020. Available: http://arxiv.org/abs/2012.01988

Most companies probably have several models to solve the same problem. There are model A, model B, even model C. The final result is some kind of aggregation of the three models. Or the models are cascaded like what's shown in the figure. But it takes a lot of computing resources to run the features through the three models.

Wang et al shows that ensembles are not more resource demanding than big models with similar performance in CV tasks. Open / Comment

2021-11-09 08:44:54 #fun

Lol, thank you Mr Lossfunction. But, which sanitizer are you using?

https://www.reddit.com/r/learnmachinelearning/comments/qpolnw/data_cleaning_is_so_must/ Open / Comment

2021-11-08 19:06:43 #DS #news

This is a post about Zillow's Zetimate Model.

Zillow (https://zillow.com/ ) is an online real-estate marketplace and it is a big player. But last week, Zillow withdrew from the house flipping market and planned to layoff a handful of employees.

There are rumors indicating that this action is related to their machine learning based price estimation tool, Zestimate ( https://www.zillow.com/z/zestimate/ ).

At a first glance, Zestimate seems fine. Though the metrics shown on the website may not be that convincing, I am sure they've benchmarked more metrics than those shown on the website.
There are some discussions on reddit.

Anyways, this is not the best story for data scientists.

1. News: https://www.reddit.com/r/MachineLearning/comments/qlilnf/n_zillows_nnbased_zestimate_leads_to_massive/
2. This is Zestimate: https://www.zillow.com/z/zestimate/
3. https://www.wired.com/story/zillow-ibuyer-real-estate/ Open / Comment

2021-11-08 19:05:21 #ML

(See also https://bit.ly/3F1Kv2F )

Centered Kernel Alignment (CKA) is a similarity metric designed to measure the similarity of between representations of features in neural networks[^Kornblith2019].

CKA is based on the Hilbert-Schmidt Independence Criterion (HSIC). HSIC is defined using the centered kernels of the features to compare[^Gretton2005]. But HSIC is not invariant to isotropic scaling which is required for a similarity metric of representations[^Kornblith2019]. CKA is a normalization of HSIC.

The attached figure shows why CKA makes sense.

CKA has problems too. Seita et al argues that CKA is a metric based on intuitive tests, i.e., calculate cases that we believe that should be similar and check if the CKA values is consistent with this intuition. Seita et al built a quantitive benchmark[^Seita].

[^Kornblith2019]: http://arxiv.org/abs/1905.00414

[^Gretton2005]: https://link.springer.com/chapter/10.1007%2F11564089_7

[^Seita]: https://bair.berkeley.edu/blog/2021/11/05/similarity/ Open / Comment

2021-11-03 10:26:03 #DS #fun

Looks familiar.

https://xkcd.com/2533/ Open / Comment

2021-11-03 10:11:12 Live stream finished (220 days) Open / Comment