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Generative Representation Learning for Scalable Latent Tokenization

Paper recorded by Signals 4 on 2026-10-14 in cs.LG. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-14 on arXiv · recorded by Signals 4 on 2026-10-15

Category: cs.LG · 机器学习 · first seen 2026-10-15

Abstract

We introduce an approach for self-supervised representation learning designed to be used as a tokenizer for generative modeling. Our objective supervises a representation loss by factorizing the pointwise mutual information of the data, which we estimate by taking information differences between conditional predictions from a learned generative model. We justify our algorithm theoretically and emp

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#282 most recent of 749 cs.LG papers we have recorded · ↑ newer: Revisiting Simple Baselines for Relational Deep Learning · ↓ older: The Physics Is the Network: Fast, Training-Free Neural Emulation
Cite this page: Generative Representation Learning for Scalable Latent Tokenization: the #282 most recent of 749 cs.LG papers we have recorded (as of 2026-10-14). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/generative-representation-learning-for-scalable-latent-tokenization.html
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