Discrete Markov Bridge
Discrete diffusion has recently emerged as a promising paradigm in discrete
data modeling. However, existing methods typically rely on a fixed rate
transition matrix during training, which not only limits the expressiveness of
latent representations, a fundamental strength of variational methods, but also
constrains the overall design space. To address these limitations, we propose
Discrete Markov Bridge, a novel framework specifically designed for discrete
representation learning. Our approach is built upon two key components: Matrix
Learning and Score Learning. We conduct a rigorous theoretical analysis,
establishing formal performance guarantees for Matrix Learning and proving the
convergence of the overall framework. Furthermore, we analyze the space
complexity of our method, addressing practical constraints identified in prior
studies. Extensive empirical evaluations validate the effectiveness of the
proposed Discrete Markov Bridge, which achieves an Evidence Lower Bound (ELBO)
of 1.38 on the Text8 dataset, outperforming established baselines. Moreover,
the proposed model demonstrates competitive performance on the CIFAR-10
dataset, achieving results comparable to those obtained by image-specific
generation approaches.
683527ba3762eb8b3ea1de34
Hengli Li
claimed_verified
2025-05-27T07:51:37.403Z
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Li
user
henry12348
683527ba3762eb8b3ea1de35
Yuxuan Wang
extracted_pending
2025-05-27T02:47:22.991Z
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/avatars/a711a6aa35757dfd7b78b26098a964fc.svg
Yuxuan Wang
user
ColorfulAI
683527ba3762eb8b3ea1de36
Song-Chun Zhu
683527ba3762eb8b3ea1de37
Ying Nian Wu
683527ba3762eb8b3ea1de38
Zilong Zheng
extracted_pending
2025-05-27T02:47:22.991Z
63a95a6a7930fa8c7dd63d4e
/avatars/d9d0420f7ddfe2f3a7e029fb05f1c89f.svg
Zilong Zheng
user
zlzheng
Discrete Markov Bridge
Discrete diffusion has recently emerged as a promising paradigm in discrete
data modeling. However, existing methods typically rely on a fixed rate
transition matrix during training, which not only limits the expressiveness of
latent representations, a fundamental strength of variational methods, but also
constrains the overall design space. To address these limitations, we propose
Discrete Markov Bridge, a novel framework specifically designed for discrete
representation learning. Our approach is built upon two key components: Matrix
Learning and Score Learning. We conduct a rigorous theoretical analysis,
establishing formal performance guarantees for Matrix Learning and proving the
convergence of the overall framework. Furthermore, we analyze the space
complexity of our method, addressing practical constraints identified in prior
studies. Extensive empirical evaluations validate the effectiveness of the
proposed Discrete Markov Bridge, which achieves an Evidence Lower Bound (ELBO)
of 1.38 on the Text8 dataset, outperforming established baselines. Moreover,
the proposed model demonstrates competitive performance on the CIFAR-10
dataset, achieving results comparable to those obtained by image-specific
generation approaches.
A novel framework, Discrete Markov Bridge, is introduced for discrete data modeling with Matrix Learning and Score Learning components, demonstrating superior performance compared to existing methods on Text8 and CIFAR-10 datasets.
discrete diffusion variational methods Discrete Markov Bridge Matrix Learning Score Learning Evidence Lower Bound ELBO