Maximum Entropy Generators for Energy-Based Models

24 Jan 2019Rithesh KumarSherjil OzairAnirudh GoyalAaron CourvilleYoshua Bengio

Maximum likelihood estimation of energy-based models is a challenging problem due to the intractability of the log-likelihood gradient. In this work, we propose learning both the energy function and an amortized approximate sampling mechanism using a neural generator network, which provides an efficient approximation of the log-likelihood gradient... (read more)

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