Autoencoders in Deep Learning – ICLR 2017 Discoveries

This blog post gives an overview of papers related to autoencoders submitted to ICLR 2017, see underneath for the list of papers. If you want to learn about autoencoders check out the Stanford (UFLDL) tutorial about Autoencoders, Carl Doersch’ Tutorial on Variational Autoencoders, DeepLearning.TV’s Video tutorial on Autoencoders, or Goodfellow, Bengio and Courville’s Deep Learning book’s chapter on Autencoders.

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Amund Tveit

  1. Revisiting Denoising Auto-Encoders – Authors: Luis Gonzalo Sanchez Giraldo
  2. Epitomic Variational Autoencoders – Authors: Serena Yeung, Anitha Kannan, Yann Dauphin, Li Fei-Fei
  3. Unsupervised sentence representation learning with adversarial auto-encoder – Authors: Shuai Tang, Hailin Jin, Chen Fang, Zhaowen Wang
  4. Tree-Structured Variational Autoencoder – Authors: Richard Shin, Alexander A. Alemi, Geoffrey Irving, Oriol Vinyals
  5. Lossy Image Compression with Compressive Autoencoders – Authors: Lucas Theis, Wenzhe Shi, Andrew Cunningham, Ferenc Huszár
  6. Variational Lossy Autoencoder – Authors: Xi Chen, Diederik P. Kingma, Tim Salimans, Yan Duan, Prafulla Dhariwal, John Schulman, Ilya Sutskever, Pieter Abbeel
  7. Stick-Breaking Variational Autoencoders – Authors: Eric Nalisnick, Padhraic Smyth
  8. ParMAC: distributed optimisation of nested functions, with application to binary autoencoders – Authors: Miguel A. Carreira-Perpinan, Mehdi Alizadeh
  9. Discrete Variational Autoencoders – Authors: Jason Tyler Rolfe
  10. Deep Unsupervised Clustering with Gaussian Mixture Variational Autoencoders – Authors: Nat Dilokthanakul, Pedro A. M. Mediano, Marta Garnelo, Matthew C.H. Lee, Hugh Salimbeni, Kai Arulkumaran, Murray Shanahan
  11. Improving Sampling from Generative Autoencoders with Markov Chains – Authors: Kai Arulkumaran, Antonia Creswell, Anil Anthony Bharath

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