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XOR-CD: Linearly Convergent Constrained Structure Generation
Fan Ding · Jianzhu Ma · Jinbo Xu · Yexiang Xue

Wed Jul 21 05:30 AM -- 05:35 AM (PDT) @

We propose XOR-Contrastive Divergence learning (XOR-CD), a provable approach for constrained structure generation, which remains difficult for state-of-the-art neural network and constraint reasoning approaches. XOR-CD harnesses XOR-Sampling to generate samples from the model distribution in CD learning and is guaranteed to generate valid structures. In addition, XOR-CD has a linear convergence rate towards the global maximum of the likelihood function within a vanishing constant in learning exponential family models. Constraint satisfaction enabled by XOR-CD also boosts its learning performance. Our real-world experiments on data-driven experimental design, dispatching route generation, and sequence-based protein homology detection demonstrate the superior performance of XOR-CD compared to baseline approaches in generating valid structures as well as capturing the inductive bias in the training set.

Author Information

Fan Ding (Purdue University)
Jianzhu Ma (Institute for Artificial Intelligence)
Jinbo Xu (Toyota Technological Institute at Chicago)
Yexiang Xue (Purdue University)

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