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cs.LG2023★ 1 cited
Density Uncertainty Layers for Reliable Uncertainty Estimation
Yookoon Park, David M. Blei
Assessing the predictive uncertainty of deep neural networks is crucial for safety-related applications of deep learning. Although Bayesian deep learning offers a principled framew…
cs.LG2022★ 12 cited
Variational Laplace Autoencoders
Yookoon Park, Chris Dongjoo Kim, Gunhee Kim
Variational autoencoders employ an amortized inference model to approximate the posterior of latent variables. However, such amortized variational inference faces two challenges: (…
cs.LG2021
Unsupervised Representation Learning via Neural Activation Coding
Yookoon Park, Sangho Lee, Gunhee Kim +1
We present neural activation coding (NAC) as a novel approach for learning deep representations from unlabeled data for downstream applications. We argue that the deep encoder shou…