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Christopher Bender

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2

AAAI Conference 2020 Conference Paper

Exchangeable Generative Models with Flow Scans

  • Christopher Bender
  • Kevin O'Connor
  • Yang Li
  • Juan Garcia
  • Junier Oliva
  • Manzil Zaheer

In this work, we develop a new approach to generative density estimation for exchangeable, non-i. i. d. data. The proposed framework, FlowScan, combines invertible flow transformations with a sorted scan to flexibly model the data while preserving exchangeability. Unlike most existing methods, FlowScan exploits the intradependencies within sets to learn both global and local structure. FlowScan represents the first approach that is able to apply sequential methods to exchangeable density estimation without resorting to averaging over all possible permutations. We achieve new state-of-the-art performance on point cloud and image set modeling.

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NeurIPS Conference 2020 Conference Paper

Exchangeable Neural ODE for Set Modeling

  • Yang Li
  • Haidong Yi
  • Christopher Bender
  • Siyuan Shan
  • Junier B. Oliva

Reasoning over an instance composed of a set of vectors, like a point cloud, requires that one accounts for intra-set dependent features among elements. However, since such instances are unordered, the elements' features should remain unchanged when the input's order is permuted. This property, permutation equivariance, is a challenging constraint for most neural architectures. While recent work has proposed global pooling and attention-based solutions, these may be limited in the way that intradependencies are captured in practice. In this work we propose a more general formulation to achieve permutation equivariance through ordinary differential equations (ODE). Our proposed module, Exchangeable Neural ODE (ExNODE), can be seamlessly applied for both discriminative and generative tasks. We also extend set modeling in the temporal dimension and propose a VAE based model for temporal set modeling. Extensive experiments demonstrate the efficacy of our method over strong baselines.

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