2024-08-14-malherbe24a.md

title	abstract	openreview	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
Implicit Neural Representation as vectorizer for classification task applied to diverse data structures	Implicit neural representations have recently emerged as a promising tool in data science research for their ability to learn complex, high-dimensional functions without requiring explicit equations or hand-crafted features. Here we aim to use these implicit neural representations weights to represent batch of data and use it to classify these batch based only on these weights, without any feature engineering on the raw data. In this study, we demonstrate that this method yields very promising results in data classification of several type of data, such as sound, images, videos or human activities, without any prior knowledge in the relative field.	VTvytANXYq	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	malherbe24a	0	Implicit Neural Representation as vectorizer for classification task applied to diverse data structures	62	76	62-76	62	false	Malherbe, Thibault	given family Thibault Malherbe	2024-08-14		Proceedings of the 1st ContinualAI Unconference, 2023	249	inproceedings	date-parts 2024 8 14	https://raw.githubusercontent.com/mlresearch/v249/main/assets/malherbe24a/malherbe24a.pdf