Joseph Antognini

I am a Google AI Resident. Prior to my work at Google I worked in astronomy studying the dynamics of few-body systems. My current research interests are threefold: 1. Applying deep learning to the audio domain. In particular I am interested in the problem of fast spectrogram inversion. 2. Understanding the training dynamics of neural networks. 3. Applying deep learning to problems in astronomy.

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    Google
Audio Texture Synthesis with Random Neural Networks: Improving Diversity and Quality
Joseph Antognini
Matthew D. Hoffman
Ron J. Weiss
ICASSP (2019)
PCA of high dimensional random walks with comparison to neural network training
Joseph Antognini
Jascha Sohl-Dickstein
NeurIPS (2018)
Measuring the Effects of Data Parallelism on Neural Network Training
Chris Shallue
Jaehoon Lee
Joseph Antognini
Jascha Sohl-dickstein
Roy Frostig
George Dahl
Journal of Machine Learning Research (JMLR) (2018)
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