NIPS Proceedingsβ

Ricardo Henao

13 Papers

  • Improving Textual Network Learning with Variational Homophilic Embeddings (2019)
  • Kernel-Based Approaches for Sequence Modeling: Connections to Neural Methods (2019)
  • Adversarial Symmetric Variational Autoencoder (2017)
  • ALICE: Towards Understanding Adversarial Learning for Joint Distribution Matching (2017)
  • Deconvolutional Paragraph Representation Learning (2017)
  • VAE Learning via Stein Variational Gradient Descent (2017)
  • Towards Unifying Hamiltonian Monte Carlo and Slice Sampling (2016)
  • Variational Autoencoder for Deep Learning of Images, Labels and Captions (2016)
  • Deep Poisson Factor Modeling (2015)
  • Deep Temporal Sigmoid Belief Networks for Sequence Modeling (2015)
  • Large-Scale Bayesian Multi-Label Learning via Topic-Based Label Embeddings (2015)
  • Bayesian Nonlinear Support Vector Machines and Discriminative Factor Modeling (2014)
  • Bayesian Sparse Factor Models and DAGs Inference and Comparison (2009)