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Researchers propose new framework for learning multimodal energy-based models

作者 PulseAugur 编辑部 · [2 个来源] ·

Researchers have developed a new framework for learning multimodal energy-based models (EBMs) by integrating them with multimodal variational autoencoders (VAEs). This approach addresses limitations in existing methods where Markov Chain Monte Carlo (MCMC) sampling struggles with poor mixing and discovering inter-modal relationships. The proposed framework interweaves maximum likelihood estimation (MLE) updates with MCMC refinements in both data and latent spaces, enabling more effective sampling and learning of coherent multimodal data. AI

影响 Introduces a novel method for improving multimodal generative model training and sample coherence.

排序理由 Academic paper detailing a new learning framework for multimodal energy-based models.

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

Researchers propose new framework for learning multimodal energy-based models

报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Jiali Cui, Zhiqiang Lao, Heather Yu ·

    Learning Multimodal Energy-Based Model with Multimodal Variational Auto-Encoder via MCMC Revision

    arXiv:2605.00644v1 Announce Type: new Abstract: Energy-based models (EBMs) are a flexible class of deep generative models and are well-suited to capture complex dependencies in multimodal data. However, learning multimodal EBM by maximum likelihood requires Markov Chain Monte Car…

  2. arXiv cs.AI TIER_1 English(EN) · Heather Yu ·

    Learning Multimodal Energy-Based Model with Multimodal Variational Auto-Encoder via MCMC Revision

    Energy-based models (EBMs) are a flexible class of deep generative models and are well-suited to capture complex dependencies in multimodal data. However, learning multimodal EBM by maximum likelihood requires Markov Chain Monte Carlo (MCMC) sampling in the joint data space, wher…

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