[QA] I-Con: A Unifying Framework for Representation Learning

from Arxiv Papers · host Igor Melnyk

This paper presents a unified information-theoretic framework for loss functions in machine learning, improving unsupervised image classification and enabling new debiasing methods for contrastive learners. https://arxiv.org/abs//2504.16929 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016 Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers

Episode metadata supplied by the publisher feed · Published Apr 24, 2025

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[QA] I-Con: A Unifying Framework for Representation Learning

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