#tensors — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #tensors, aggregated by home.social.
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🤔 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. 🚀🙄
https://zserge.com/posts/tensor/ #mathproblems #tensors #neuralnetworks #programming #Clanguage #innovation #HackerNews #ngated -
🚀 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. 🤓
https://bartoszmilewski.com/2026/06/30/actegories/ #Actegories #MonoidalCategories #HackerNews #ngated -
How to Train Your First TensorFlow Model in PyCharm
#Python #Pycharm #Datascience #Tutorials #Tensorflow #Tensorshttps://blog.jetbrains.com/pycharm/2026/04/how-to-train-your-first-tensorflow-model/
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Run a 1T parameter model on a 32gb Mac by streaming tensors from NVMe
#HackerNews #Run #a #1T #parameter #model #on #a #32gb #Mac #by #streaming #tensors #from #NVMe #https://github.com/t8/hypura #MachineLearning #Tensors #NVMe #Mac #Optimization
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All in on MatMul? Don’t Put All Your Tensors in One Basket!
https://www.sigarch.org/dont-put-all-your-tensors-in-one-basket-hardware-lottery/
#HackerNews #MatMul #Tensors #HardwareLottery #AIResearch #DataScience
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Have a quick explanation of tensors, complete with visual aids.
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Learn how to slice, extract, and insert data in tensors using TensorFlow APIs—essential skills for efficient ML and NLP model development. https://hackernoon.com/tensor-slicing-and-data-insertion-made-easy-with-tensorflow #tensors
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No Tension for Tensors? https://hackaday.com/2025/07/09/no-tension-for-tensors/ #Science #tensors #tensor #math
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This is an example of amortized complexity and Strassen's "asymptotic spectra" (nice monograph by Zuiddam & Wigderson: https://www.math.ias.edu/~avi/PUBLICATIONS/WigdersonZu_Final_Draft_Oct2023.pdf)
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 https://en.wikipedia.org/wiki/Communication_complexity#Information_Complexity
- Circuit complexity (Robere & Zuiddam https://eccc.weizmann.ac.il/report/2021/035)#math #probability #ComputationalComplexity #TCS #InformationTheory
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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
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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. 🤓
https://blog.ezyang.com/2019/05/pytorch-internals/ #CCodebase #DeepDive #TechHumor #ProgrammingInsights #HackerNews #ngated -
🚀 **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: https://smsk.dev/2025/02/14/introducing-devbytes-quick-dives-into-software-engineering/#DevBytes #SoftwareEngineering #CodingTips #Programming #HardwareEngineering #ai #LLM #Tensors #ShortFormLearning
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'Guaranteed Nonconvex Factorization Approach for Tensor Train Recovery', by Zhen Qin, Michael B. Wakin, Zhihui Zhu.
http://jmlr.org/papers/v25/24-0029.html
#tensor #tensors #factorization -
'A tensor factorization model of multilayer network interdependence', by Izabel Aguiar, Dane Taylor, Johan Ugander.
http://jmlr.org/papers/v25/23-0205.html
#tensors #tensor #multilayer -
#Pytorch is really good supporting #tensors | Can run across multiple clusters and multiple platforms #allthingsopen #AllThingsOpen2024
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#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: https://events.iu.edu/siceiub/event/1663944-tensors-everywhere-in-complexity
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Good high level overview of what #tensors are
https://www.quantamagazine.org/the-geometric-tool-that-solved-einsteins-relativity-problem-20240812/ -
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. #ITeachPhysics #ElectromagneticField #Tensors
https://youtube.com/playlist?list=PL2aHrV9pFqNTEMuDFre16Wx2SwBCNiR7j&feature=shared
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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)
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How the matrix transforms is "equivalent data" to "what kind of multilinear thing the matrix represents."
(3/4)
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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)
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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 matricesSimilarly, 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)
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A Counter-Counterexample!!
Comon’s Conjecture is undead now.
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#Spinors can be viewed as "square roots" of #sections of #vector #bundles.
https://en.m.wikipedia.org/wiki/Spinor
#Spinors are mathematical entities somewhat like #tensors, that allow a more general treatment of the notion of #invariance under #rotation and #Lorentz boosts.
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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: https://aalmada.github.io/A-generic-tensor-library-for-dotnet.html
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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
https://www.youtube.com/playlist?list=PLJHszsWbB6hrkmmq57lX8BV-o-YIOFsiG