While t-SNE is powerful for capturing non-linear relationships, in practical business settings, there is a strong demand for explainability (business justification) regarding "why it was reduced to ...

Some documents have the constraint that "not a single character or formatting detail can be changed." Variations in fonts, line breaks, highlighter ranges, or the position of red text—all of these ...

Electrochemical impedance spectroscopy (EIS) is entering an exciting stage of development as machine learning–driven (ML) spectral analysis begins to complement traditional equivalent-circuit fitting ...

Sensory neurons must remain selective for specific features in a scene, even when many stimuli fall within their receptive fields (RFs). In natural vision, this selectivity is preserved by a process ...

werpy is an ultra-fast, lightweight Python package for calculating and analyzing Word Error Rate (WER) between two sets of text. Built for flexibility and ease of use ...

Hey everyone! 👋Welcome back to our NLP journey! 🎉 Today, we're going to dive into another essential text-processing technique: Normalization. Imagine you have a bunch of books, but some are in ...

Abstract: If diabetic retinopathy is not identified and treated right away, it is a dangerous side effect of diabetes that will cause blindness. In this work, we investigate novel deep learning (DL) ...

Inverse Text Normalization (ITN) is a pivotal process in natural language processing (NLP), especially in the context of converting speech to text. This article delves into the concept of ITN, its ...

Non-negative matrix factorization (NMF) is an unsupervised learning method well suited to high-throughput biology. However, inferring biological processes from an NMF result still requires additional ...