Householder meets Sylvester: Normalizing Flows for Variational Inference

Jun 15, 2019

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Invertible neural networks have been a significant thread of research in the ICML community for several years. Such transformations can offer a range of unique benefits: (1) They preserve information, allowing perfect reconstruction (up to numerical limits) and obviating the need to store hidden activations in memory for backpropagation. (2) They are often designed to track the changes in probability density that applying the transformation induces (as in normalizing flows). (3) Like autoregressive models, normalizing flows can be powerful generative models which allow exact likelihood computations; with the right architecture, they can also allow for much cheaper sampling than autoregressive models. While many researchers are aware of these topics and intrigued by several high-profile papers, few are familiar enough with the technical details to easily follow new developments and contribute. Many may also be unaware of the wide range of applications of invertible neural networks, beyond generative modelling and variational inference.

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About ICML 2019

The International Conference on Machine Learning (ICML) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence known as machine learning. ICML is globally renowned for presenting and publishing cutting-edge research on all aspects of machine learning used in closely related areas like artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, and robotics. ICML is one of the fastest growing artificial intelligence conferences in the world. Participants at ICML span a wide range of backgrounds, from academic and industrial researchers, to entrepreneurs and engineers, to graduate students and postdocs.

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