4 citations · 8 across the 3 of their papers we have counts for
3 papers
eess.SP2023★ 3 cited
ECGBERT: Understanding Hidden Language of ECGs with Self-Supervised Representation Learning
Seokmin Choi, Sajad Mousavi, Phillip Si +3
In the medical field, current ECG signal analysis approaches rely on supervised deep neural networks trained for specific tasks that require substantial amounts of labeled data. Ho…
cs.LG2022★ 1 cited
Semi-Autoregressive Energy Flows: Exploring Likelihood-Free Training of Normalizing Flows
Phillip Si, Zeyi Chen, Subham Sekhar Sahoo +2
Training normalizing flow generative models can be challenging due to the need to calculate computationally expensive determinants of Jacobians. This paper studies the likelihood-f…
cs.LG2021★ 4 cited
Autoregressive Quantile Flows for Predictive Uncertainty Estimation
Phillip Si, Allan Bishop, Volodymyr Kuleshov
Numerous applications of machine learning involve representing probability distributions over high-dimensional data. We propose autoregressive quantile flows, a flexible class of n…