Pix2tex: Using a ViT to convert images of equations into LaTeX code

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  • LaTeX-OCR

    pix2tex: Using a ViT to convert images of equations into LaTeX code.

  • LIMoE-pytorch

    PyTorch implementation of LIMoE

  • Makes me wonder what the SOTA is for open source efforts along these lines.

    I have heard about "mixture of experts" as being a potentially important advance, and also of course about multimodality. So I found this: https://github.com/YeonwooSung/LIMoE-pytorch

  • InfluxDB

    Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

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  • nougat

    Implementation of Nougat Neural Optical Understanding for Academic Documents

  • If you're looking for more e2e math / latex aware OCR checkout https://github.com/facebookresearch/nougat

  • latex2sympy

    Discontinued Parse LaTeX math expressions

  • There was something like that, but it's been abandoned for a while now... https://github.com/augustt198/latex2sympy

  • hypothesis

    Hypothesis is a powerful, flexible, and easy to use library for property-based testing.

  • But then add tests! Tests for LaTeX equations that had never been executable as code.

    https://github.com/HypothesisWorks/hypothesis :

    > Hypothesis is a family of testing libraries which let you write tests parametrized by a source of examples. A Hypothesis implementation then generates simple and comprehensible examples that make your tests fail. This simplifies writing your tests and makes them more powerful at the same time, by letting software automate the boring bits and do them to a higher standard than a human would, freeing you to focus on the higher level test logic.

    > This sort of testing is often called "property-based testing", and the most widely known implementation of the concept is the Haskell library QuickCheck, but Hypothesis differs significantly from QuickCheck and is designed to fit idiomatically and easily into existing styles of testing that you are used to, with absolutely no familiarity with Haskell or functional programming needed.

  • SaaSHub

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