News

  • December 2018: Two papers accepted to ICLR 2019, both related to Deep InfoMax (DIM)! Learning Deep Representations by Mutual information Estimation and Maximization (a.k.a. Deep InfoMax or DIM) was accepted as an oral presentation (top 1.5% of submissions). Deep Graph InfoMax was accepted as a conference poster.
  • October 2018: A paper accepted to AAAI on better mode coverage for generative adversarial networks (GANS) called Online Adaptive Curriculum Learning for GANs.
  • September 2018: Two workshops papers accepted to NeurIPS 2018. One is on learning representations.

Recent Publications

More Publications

(2019). Deep Graph Infomax. International Conference on Learning Representations.

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(2019). Learning deep representations by mutual information estimation and maximization. International Conference on Learning Representations.

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(2019). Online Adaptative Curriculum Learning for GANs. Association for the Advancement of Artificial Intelligence (to appear).

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(2018). Boundary Seeking GANs. International Conference on Learning Representations.

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(2018). Mutual Information Neural Estimation. Proceedings of the 35th International Conference on Machine Learning.

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(2018). Spatio-Temporal Dynamics of Intrinsic Networks in Functional Magnetic Imaging Data Using Recurrent Neural Networks. Frontiers in neuroscience.

(2017). GibbsNet: Iterative Adversarial Inference for Deep Graphical Models. Advances in Neural Information Processing Systems 30.

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(2017). Maximum-likelihood augmented discrete generative adversarial networks. arXiv preprint arXiv:1702.07983.

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(2016). Deep independence network analysis of structural brain imaging: application to schizophrenia. IEEE transactions on medical imaging.

(2016). Iterative Refinement of the Approximate Posterior for Directed Belief Networks. Advances in Neural Information Processing Systems 29.

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