• breandan

    breandan

    1 year ago
    This Friday Mar. 26th at 12:00 EDT / 16:00 UTC we are hosting Erik Meijer at our reading group. He will discuss some mathematical topics, possibly related to Kotlin somehow. In case anyone here is available, please feel welcome to drop by and say hello! It would be great to have your feedback.
    Inside Every Calculus Is A Little Algebra Waiting To Get Out
    Because of deep learning, there has been a surge in interest in automatic differentiation, especially from the functional programming community. As a result there are many recent papers that look at AD from a Category Theory perspective. However, Category Theorists have already been looking at differentiation and calculus in general since the late 60’s in the context of Synthetic Differential Geometry, but it seems that this work is largely ignored by those interested in AD. In this talk, we will provide a gentle introduction to the ideas of SDG, by relating them to dual numbers, and show how it provides a simple and purely algebraic approach to (automatic) differentiation.
    https://mcgill.zoom.us/j/89551971819
    breandan
    altavir
    +1
    5 replies
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  • i

    Iaroslav Postovalov

    1 year ago
    I've created Jupyter integration supporting the display of KMath's MST objects.
    i
    Ilya Muradyan
    +1
    7 replies
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  • altavir

    altavir

    1 year ago
    @Ролан You will probably like this one. A universal multiplatform Gauss-Legendre numerical integration, which could work on
    StructureND
    .
    altavir
    2 replies
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  • z

    Zhelenskiy

    1 year ago
    Current implementation of BigInteger implementation and power is slow so I provided it with corresponding tests: PR .
    z
    altavir
    +1
    23 replies
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  • f

    Filipe Duarte

    1 year ago
    Operator overload of Nd4j INDArray #kotlindl
    f
    altavir
    17 replies
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  • altavir

    altavir

    1 year ago
    @Pavel Gorgulov @zaleslaw I would like to invite you to a public review of our (aniversary #300) PR by @Ролан and his interns: https://github.com/mipt-npm/kmath/pull/300. The PR introduces API and basic multiplatform implementation of tensor algebra. It is huge. Also I would like to start some kind of discusstion about what features do we want from tensors mathematics. Current implementation has one significant benifit, it works on top of generic KMath
    StructureND
    meaning it is compatible with any other library which have binings for KMath and one could pass those structures around between different implementations (like Viktor for example). @Ролан I also would like for your students to make a public seminar (in English) describing what have they done and sharing their experience.
    altavir
    zaleslaw
    2 replies
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  • i

    Iaroslav Postovalov

    1 year ago
    We just merged Jupyter integration to the KMath
    dev
    branch.
    i
    Ролан
    +2
    10 replies
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  • i

    Iaroslav Postovalov

    1 year ago
    i
    1 replies
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  • z

    Zhelenskiy

    1 year ago
    One of the problems of many big int implementations is that they work extremely slow for not so big numbers.. I think it is a common case when number of really big numbers operations is slow so real numbers can be used with longs in most cases and overflows are rare. So, I created a demo to demonstrate it. I didn't compare with KMath's implementation but compared with java.lang.Math's one. Even when I give no helping info, it is about 4 times faster. When I used explicit
    LongBased
    type, it became 10 faster than generic one. The speed was the same with just manual long checking. This is because I used inline types. However, it is still a lot slower than just pure usage of
    Int
    s and Longs. But that may be achieved if the code is run under HotSpot and it decides to use intrinsic there. Here is the repo: https://github.com/zhelenskiy/BigInteger/tree/main.
    z
    i
    +1
    78 replies
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