Daniel Johnson — accepted AI-conference papers

2020

Learning Graph Structure With A Finite-State Automaton Layer

Daniel Johnson, Hugo Larochelle, Daniel Tarlow

NeurIPS 2020spotlight

Graph-based neural network models are producing strong results in a number of domains, in part because graphs provide flexibility to encode domain knowledge in the form of relational structure (edges) between nodes in the graph. In practice, edges are used both to represent intrinsic structure (e.g.…

Cited by 20 Source ↗PDF ↗code ↗Scholar ↗

2018

Latent Gaussian Activity Propagation: Using Smoothness and Structure to Separate and Localize Sounds in Large Noisy Environments

Daniel Johnson, Daniel Gorelik, Ross E Mawhorter, Kyle Suver, Weiqing Gu, Steven Xing +2

NeurIPS 2018poster

We present an approach for simultaneously separating and localizing multiple sound sources using recorded microphone data. Inspired by topic models, our approach is based on a probabilistic model of inter-microphone phase differences, and poses separation and localization as a Bayesian inference pro…

Cited by 2 Source ↗PDF ↗Scholar ↗