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Normalizing Flows are generative models that directly maximize thelikelihood. Previously, the design of normalizing flows was largely constrainedby the need for analytical invertibility. We overcome this constraint by atraining procedure that uses an efficient estimator for the gradient of thechange of variables formula. This enables any dimension-preserving neuralnetwork to serve as a generative model through maximum likelihood training. Ourapproach allows placing the emphasis on tailoring inductive biases precisely tothe task at hand. Specifically, we achieve excellent results in moleculegeneration benchmarks utilizing $E(n)$-equivariant networks. Moreover, ourmethod is competitive in an inverse problem benchmark, while employingoff-the-shelf ResNet architectures.

Free-form Flows: Make Any Architecture a Normalizing Flow