Popular GitHub Repositories for Boolean Formulas

An Open-Source Python3 tool with SMALL models for recognizing layouts, tables, math formulas (LaTeX), and text in images, converting them into Markdown format. A free alternative to Mathpix, empowering seamless conversion of visual content into text-based representations. 80+ languages are supported.

:art: 数学公式识别增强版：中英文手写印刷公式、支持初级符号推导（数据结构基于 LaTeX 抽象语法树）Math Formula OCR Pro, supports handwrite, Chinese-mixed formulas and simple symbol reasoning (based on LaTeX AST).

Use LLMs in Excel formulas

Chat with your tabular data. No need of remembering any formulas or learning pandas. Train machine learning models in natural language

A recurrent attention module consisting of an LSTM cell which can query its own past cell states by the means of windowed multi-head attention. The formulas are derived from the BN-LSTM and the Transformer Network. The LARNN cell with attention can be easily used inside a loop on the cell state, just like any other RNN. (LARNN)

OpenABC-D is a large-scale labeled dataset generated by synthesizing open source hardware IPs. This dataset can be used for various graph level prediction problems in chip design.

Cottontail DB is a column store vector database aimed at multimedia retrieval. It allows for classical boolean as well as vector-space retrieval (nearest neighbour search) used in similarity search using a unified data and query model.

Create OpenAI chat completions from Excel formulas

?B nets: learning discrete, boolean-valued functions by gradient descent

Logical Message Passing Networks with One-hop Inference in Atomic Formulas (ICLR 2023)

Thanks to digitization, we often have access to large databases, consisting of various fields of information, ranging from numbers to texts and even boolean values. Such databases lend themselves especially well to machine learning, classification and big data analysis tasks. We are able to train classifiers, using already existing data and use them for predicting the values of a certain field, given that we have information regarding the other fields. Most specifically, in this study, we look at the Electronic Health Records (EHRs) that are compiled by hospitals. These EHRs are convenient means of accessing data of individual patients, but there processing as a whole still remains a task. However, EHRs that are composed of coherent, well-tabulated structures lend themselves quite well to the application to machine language, via the usage

scBoolSeq: scRNA-Seq data binarisation and synthetic generation from Boolean dynamics

?✨ A next-level spreadsheet editor with ML integration, supporting real-time graphs and formulas.

Table with operator formulas and mappings at a glance, as much ML documentation is missing details

This Python project, was created during the CS50 Artificial Intelligence course. It allows defining knowledge with Boolean operators and extracting solutions based on queries.

? Classification of pathologies column using propositional logic satisfiability algorithms.

.NET, Spreadsheet, Formulas