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Two arXiv papers review probabilistic generative models and introduce new quality assessment methods

By PulseAugur Editorial · [2 sources] ·

Two new arXiv papers explore advancements in probabilistic generative models. The first, a review, focuses on Probabilistic Circuits (PCs) and their trade-offs between expressivity and tractability, discussing hybrid deep and PC models. The second paper introduces PQMass, a likelihood-free method using probability mass estimation and chi-squared tests to assess generative model quality without density assumptions or auxiliary models. AI

IMPACT These papers contribute to the theoretical understanding and evaluation methods for generative models, potentially improving their development and application.

RANK_REASON Two academic papers published on arXiv detailing new research in generative models.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Two arXiv papers review probabilistic generative models and introduce new quality assessment methods

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Two academic papers published on arXiv detailing new research in generative models.
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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Sahil Sidheekh, Sriraam Natarajan ·

    Building Expressive and Tractable Probabilistic Generative Models: A Review

    arXiv:2402.00759v4 Announce Type: replace-cross Abstract: We present a comprehensive survey of the advancements and techniques in the field of tractable probabilistic generative modeling, primarily focusing on Probabilistic Circuits (PCs). We provide a unified perspective on the …

  2. arXiv cs.AI TIER_1 English(EN) · Pablo Lemos, Sammy Sharief, Esmeralda S. Whitammer, Salma Salhi, Connor Stone, Laurence Perreault-Levasseur, Yashar Hezaveh ·

    PQMass: Probabilistic Assessment of the Quality of Generative Models using Probability Mass Estimation

    arXiv:2402.04355v4 Announce Type: replace-cross Abstract: We propose a likelihood-free method for comparing two distributions given samples from each, with the goal of assessing the quality of generative models. The proposed approach, PQMass, provides a statistically rigorous met…

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