Learning the Stein Discrepancy for Training and Evaluating Energy-Based Models without Sampling

• URL: http://arxiv.org/abs/2002.05616v4
• Date: Fri, 14 Aug 2020 16:32:47 GMT
• Title: Learning the Stein Discrepancy for Training and Evaluating Energy-Based Models without Sampling
• Authors: Will Grathwohl, Kuan-Chieh Wang, Jorn-Henrik Jacobsen, David Duvenaud, Richard Zemel
• Abstract summary: We present a new method for evaluating and training unnormalized density models. We estimate the Stein discrepancy between the data density $p(x)$ and the model density $q(x)$ defined by a vector function of the data. This yields a novel goodness-of-fit test which outperforms existing methods on high dimensional data.
• Score: 30.406623987492726
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a new method for evaluating and training unnormalized density models. Our approach only requires access to the gradient of the unnormalized model's log-density. We estimate the Stein discrepancy between the data density $p(x)$ and the model density $q(x)$ defined by a vector function of the data. We parameterize this function with a neural network and fit its parameters to maximize the discrepancy. This yields a novel goodness-of-fit test which outperforms existing methods on high dimensional data. Furthermore, optimizing $q(x)$ to minimize this discrepancy produces a novel method for training unnormalized models which scales more gracefully than existing methods. The ability to both learn and compare models is a unique feature of the proposed method.

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