SDE-Net Paper Summary & Analysis

Objectives of the Paper

Three sentence paper summary: Uncertainty quantification is a fundamental yet unsolved problem for deep learning. The Bayesian framework provides a principled way of uncertainty estimation but is often not scalable to modern deep neural nets. We propose a new method for quantifying uncertainties of DNNs from a dynamical system perspective.

Paper Contributions

Kong et. al. approached the problem of distinguishing between aleatoric and epistemic uncertainty by implementing an uncertainty-aware neural network. This new neural net models the passing of samples through the hidden layers as a dynamic process akin to that of a moving liquid at a molecular level, hence the application of the Brownian motion formula to this problem. If a model approaches something it’s seen before, the variance of the Brownian motion will be fairly small. Vice versa, if a model approaches a region it is unfamiliar with, the variance of the Brownian motion will be rather large.

Paper Limitations, Further Research, and/or Potential Applications

SDE-Net provides future directions for modeling uncertainties with neural nets.

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Cornell Data Science

Cornell Data Science

Cornell Data Science is an engineering project team @Cornell that seeks to prepare students for a career in data science.