Training Deep Energy-Based Models with f-Divergence Minimization

ICML 2020 Lantao YuYang SongJiaming SongStefano Ermon

Deep energy-based models (EBMs) are very flexible in distribution parametrization but computationally challenging because of the intractable partition function. They are typically trained via maximum likelihood, using contrastive divergence to approximate the gradient of the KL divergence between data and model distribution... (read more)

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