…
admin
Introduction to Table extraction Extracting tables from documents may sound straightforward, but in reality, it is a complex pipeline involving parsing text, recognizing structure, and preserving the precise spatial relationships between cells. Tables carry a wealth of information compacted into a grid of rows and columns, where each cell holds context based on its neighboring…
Document Visual Question Answering (DocVQA) represents a rapidly advancing field aimed at improving AI’s ability to interpret, analyze, and respond to questions based on complex documents that integrate text, images, tables, and other visual elements. This capability is increasingly valuable in finance, healthcare, and law settings, as it can streamline and support decision-making processes that…
In recent years, there has been significant development in the field of large pre-trained models for learning robot policies. The term “policy representation” here refers to the different ways of interfacing with the decision-making mechanisms of robots, which can potentially facilitate generalization to new tasks and environments. Vision-language-action (VLA) models are pre-trained with large-scale robot…
Decoding One-Hot Encoding: A Beginner’s Guide to Categorical Data | by Vyacheslav Efimov | Nov, 2024
Learning to transform categorical data into a format that a machine learning model can understand When studying machine learning, it is essential to understand the inner workings of the most basic algorithms. Doing so helps in understanding how algorithms operate in popular libraries and frameworks, how to debug them, choose better hyperparameters more easily, and…