Variational (Gradient) Estimate of the Score Function in Energy-based Latent Variable Models
By Fan Bao, Kun Xu, Chongxuan Li, Lanqing Hong, Jun Zhu. 2020
10 citations recorded by the source corpus.
The learning and evaluation of energy-based latent variable models (EBLVMs) without any structural assumptions are highly challenging, because the true posteriors and the partition functions in such models are generally intractable. This paper presents variational estimates of the score function and its gradient with respect to the model parameters in a general EBLVM, referred to as VaES and VaGES respectively. The variational posterior is trained to minimize a certain divergence to the true model posterior and the bias in both estimates can be bounded by the divergence theoretically. With a minimal model assumption, VaES and VaGES can be applied to the kernelized Stein discrepancy (KSD) and score matching (SM)-based methods to learn EBLVMs. Besides, VaES can also be used to estimate the exact Fisher divergence between the data and general EBLVMs.
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- Yoshua Bengio
Citations in the corpus
- Offline Reinforcement Learning with Fisher Divergence Critic Regularization (2021, background): “…derivations establishing a connection between this gradient penalty and the Fisher divergence , which appears in the score matching and energy-based generative model literature (Lyu, 2012; Bao et al., 2020), interpreting the critic values as the energy function of a Boltzmann distribution.”