982 citations · 2.8k across the 47 of their papers we have counts for
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3 papers · 1 filter
stat.ML2017★ 113 cited
Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples
Kimin Lee, Honglak Lee, Kibok Lee +1
The problem of detecting whether a test sample is from in-distribution (i.e., training distribution by a classifier) or out-of-distribution sufficiently different from it arises in…
cs.LG2017★ 18 cited
Confident Multiple Choice Learning
Kimin Lee, Changho Hwang, KyoungSoo Park +1
Ensemble methods are arguably the most trustworthy techniques for boosting the performance of machine learning models. Popular independent ensembles (IE) relying on naive averaging…
cs.LG2017★ 4 cited
Simplified Stochastic Feedforward Neural Networks
Kimin Lee, Jaehyung Kim, Song Chong +1
It has been believed that stochastic feedforward neural networks (SFNNs) have several advantages beyond deterministic deep neural networks (DNNs): they have more expressive power a…