32 citations · 122 across the 21 of their papers we have counts for
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stat.ML2021
Meta Learning Low Rank Covariance Factors for Energy-Based Deterministic Uncertainty
Jeffrey Willette, Hae Beom Lee, Juho Lee +1
Numerous recent works utilize bi-Lipschitz regularization of neural network layers to preserve relative distances between data instances in the feature spaces of each layer. This d…
stat.ML2018★ 32 cited
Uncertainty-Aware Attention for Reliable Interpretation and Prediction
Jay Heo, Hae Beom Lee, Saehoon Kim +4
Attention mechanism is effective in both focusing the deep learning models on relevant features and interpreting them. However, attentions may be unreliable since the networks that…
stat.ML2018★ 2 cited
Adaptive Network Sparsification with Dependent Variational Beta-Bernoulli Dropout
Juho Lee, Saehoon Kim, Jaehong Yoon +3
While variational dropout approaches have been shown to be effective for network sparsification, they are still suboptimal in the sense that they set the dropout rate for each neur…