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Canonical JEM models show indistinguishable performance between PC and SGLD samplers

By PulseAugur Editorial · [1 sources] ·

Researchers have investigated the performance of two sampling methods, Predictor-Corrector (PC) and Stochastic Gradient Langevin Dynamics (SGLD), when applied to Canonical Joint Energy-Based Models (JEM) on the CIFAR-10 dataset. The study aimed to determine if the PC sampler could replicate the theoretical advantages of SGLD in unifying classification and generation tasks. Findings indicated that both methods performed nearly identically in terms of test accuracy and out-of-distribution detection, with no clear advantage for PC over SGLD. The research also identified two failure modes: late-training divergence and run-dependent dynamics in OOD discrimination. AI

IMPACT This research provides insights into the practical performance of different sampling techniques for generative models, potentially influencing future model development and evaluation.

RANK_REASON The cluster contains an academic paper detailing a study on sampling methods for energy-based models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Canonical JEM models show indistinguishable performance between PC and SGLD samplers

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The cluster contains an academic paper detailing a study on sampling methods for energy-based models. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Dmytro Knopov ·

    Canonical Joint Energy-Based Model on CIFAR-10: failure modes and practical indistinguishability of Predictor-Corrector and SGLD samplers

    arXiv:2608.05025v1 Announce Type: cross Abstract: Joint Energy-Based Models (JEM) unify classification and generation within a single network and support out-of-distribution (OOD) detection. Canonical JEM training relies on stochastic gradient Langevin dynamics (SGLD); a theoreti…

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