home.social

#tensors — Public Fediverse posts

Live and recent posts from across the Fediverse tagged #tensors, aggregated by home.social.

fetched live
  1. 🤔 Oh, another blog post claiming to solve all math problems with ✨tensors✨! 😴 Neural networks? Just some floating-point soup! 🍜 Let's reinvent the wheel by building a tensor library... from scratch... in C. Because that's exactly what the world needs. 🚀🙄
    zserge.com/posts/tensor/ #mathproblems #tensors #neuralnetworks #programming #Clanguage #innovation #HackerNews #ngated

  2. 🚀 Ah, the riveting world of "Actegories," where we discover that #tensors and monoidal categories are the life of the #Haskell party 🎉. Bartosz bravely dives into the depths of #programming #optics, hoping someone out there is excited about monoidal categories, because let's face it, this is the stuff of legends... or insomniacs. 🤓
    bartoszmilewski.com/2026/06/30 #Actegories #MonoidalCategories #HackerNews #ngated

  3. Learn how to slice, extract, and insert data in tensors using TensorFlow APIs—essential skills for efficient ML and NLP model development. hackernoon.com/tensor-slicing- #tensors

  4. No Tension for Tensors? - We always enjoy [FloatHeadPhysics] explaining any math or physics topic. We don’t ... - hackaday.com/2025/07/09/no-ten #science #tensors #tensor #math

  5. This is an example of amortized complexity and Strassen's "asymptotic spectra" (nice monograph by Zuiddam & Wigderson: math.ias.edu/~avi/PUBLICATIONS)

    Strassen developed this to understand the #complexity of matrix multiplication and #tensors, but it turns out to also show up in a bunch of places:
    - #Entropy
    - #Quantum information
    - Shannon capacity of graphs
    - Communication complexity en.wikipedia.org/wiki/Communic
    - Circuit complexity (Robere & Zuiddam eccc.weizmann.ac.il/report/202)

    #math #probability #ComputationalComplexity #TCS #InformationTheory

  6. There are two obstacles when you use #Assemblyscript and #Deno for #MachineLearning #ML and #SmallLanguageModels #SLM. 1. Assemblyscript works with linear memory and you have to flatten 3D #tensors and 2D #matrices into 1D #arrays for doing any matrix algebra. 2. Deno is a bit more complicated than Node.js when working with #WASM (#Webassemly) files. Here is how you do it. Webassembly makes NNUE models even faster in computation. This together with #WebGPU will become our future. #AI #javascript

  7. Ah, nothing screams "exciting" like a deep dive into #PyTorch internals! 🎉 Let's unravel the mysteries of #tensors, because who doesn't love a bedtime story about C codebases? 💤 Spoiler: it's as thrilling as watching paint dry, but with extra parentheses. 🤓
    blog.ezyang.com/2019/05/pytorc #CCodebase #DeepDive #TechHumor #ProgrammingInsights #HackerNews #ngated

  8. 🚀 **Introducing DevBytes**: Quick, fun dives into software & hardware engineering!
    I’m launching a new blog series that breaks down software concepts with bite-sized insights and hands-on tips. From **tensors in LLM models** 🧠 to quick coding fixes, there’s something for everyone.
    Get ready to explore theories, best practices, and more—delivered in minutes.⏳
    👉 Check it out here: smsk.dev/2025/02/14/introducin

    #DevBytes #SoftwareEngineering #CodingTips #Programming #HardwareEngineering #ai #LLM #Tensors #ShortFormLearning

  9. 'Guaranteed Nonconvex Factorization Approach for Tensor Train Recovery', by Zhen Qin, Michael B. Wakin, Zhihui Zhu.

    jmlr.org/papers/v25/24-0029.ht

    #tensor #tensors #factorization

  10. 'A tensor factorization model of multilayer network interdependence', by Izabel Aguiar, Dane Taylor, Johan Ugander.

    jmlr.org/papers/v25/23-0205.ht

    #tensors #tensor #multilayer

  11. is really good supporting | Can run across multiple clusters and multiple platforms

  12. #Tensors Everywhere in #Complexity

    Me! At the colloquium.

    With connections to geometric complexity theory, graph isomorphism, group isomorphism, #quantum entanglement, and post-quantum-secure cryptosystems.

    Online Fri Oct 25 2024 at IU Bloomington CS: events.iu.edu/siceiub/event/16

    #math #ComputationalComplexity #CS

  13. Just stumbled onto this playlist showing how to visualize tensors, specifically F𝝁𝝂, the electromagnetic field tensor. Looks like the animation was made with manim but I bet it could be done in (Web) VPython.

    youtube.com/playlist?list=PL2a

  14. e.g. if I tell you I have a matrix M and under change of basis it transforms as A^t M A, then I know it's representing a bilinear map of the form V⊗V→F. etc.

    Similarly, if I tell you what kind of multilinear "thing" a tensor T is representing, then that tells you how it transforms under change of basis, and vice versa. For 3-tensors, there are several natural possibilities (up to permuting indices):

    U⊗V⊗W→F
    U⊗U⊗V→F
    U⊗U⊗U→F (trilinear map)
    U⊗V→W (bilinear map)
    U⊗U→V (bilinear map)
    U⊗V→U (linear action of V on U)
    U⊗U→U (algebra, not nec. associative)
    U→V⊗W
    U→U⊗V (coaction)
    U→U⊗U (coalgebra, not nec. coassociative)
    F→U⊗V⊗W
    F→U⊗U⊗V
    F→U⊗U⊗U

    (4/4)

    #tensors #matrix #algebra

  15. How the matrix transforms is "equivalent data" to "what kind of multilinear thing the matrix represents."

    (3/4)

    #tensors #matrix #algebra

  16. Now, how do we get the whole "a tensor is a thing that transforms like a tensor"? Well, let's start with matrices. How a matrix changes under change of basis *tells you what kind of multilinear thing the matrix is representing*, and the same is true of tensors. Examples:

    If a matrix M represents a linear map L:V→W, then when we change basis in V by an invertible matrix A in GL(V), and change basis in W by an invertible B in GL(W), then M changes to B M A^{-1} (where I'm writing my inputs as column vectors on the right).

    In contrast, if a matrix M represents a linear endomorphism L:V→V, then when we change basis in V by an invertible matrix A in GL(V), M becomes AMA^{-1}.

    If a matrix M represents a bilinear map V⊗V→F (by (x,y)→x^t M y), then under change of basis A^{-1}, M becomes A^t M A.

    (2/4)

    #tensors #matrix #algebra

  17. No one defines a #matrix as "a thing that transforms like a matrix". Why define tensors that way?

    Array=numbers in a (possibly high-dim) grid
    Matrix=array representation of a linear map* in a chosen basis
    Tensor=array representation of a multilinear map in a chosen basis

    (* or linear endomorphism, or bilinear function, but we'll get there.)

    Vectors=1-tensors, but not all 1-index arrays are vectors
    Matrices=2-tensors, but not all 2-ary arrays are matrices

    Similarly, not all k-ary arrays are tensors. Some examples:

    Christoffel symbols aren't a tensor because they aren't (multi)linear in all of their arguments.

    Most "tensors" in #MachineLearning #AI aren't tensors b/c they aren't multilinear - they are *just* multi-dim arrays of numbers. To say an array is (or represents) a tensor is to endow it with additional multilinear structure, same as with arrays vs matrices vs linear structure.

    (1/4)

    #tensors #matrix #algebra

  18. I wrote about how I’m building a generic tensor library for .NET, using SIMD and value-type delegates to optimize performance and reduce code duplication.

    Check it out here: aalmada.github.io/A-generic-te

    #dotnet #csharp #simd #tensors

  19. Nerding out on #eigenchris ' wonderful math videos. Just finished the Tensor for Beginners series, going on to Tensor Calculus. Hugely important, as #tensors are the gateway to the theory of #relativity among others. #math #physics
    youtube.com/playlist?list=PLJH