activity
20172020
most citedHuman Understandable Explanation Extraction for Black-box Classification Models Based on Matrix Factorization

4 citations · 4 across the 3 of their papers we have counts for

collaborators

5 papers

cs.LG2020

Consistency of a Recurrent Language Model With Respect to Incomplete Decoding

Sean Welleck, Ilia Kulikov, Jaedeok Kim +2

Despite strong performance on a variety of tasks, neural sequence models trained with maximum likelihood have been shown to exhibit issues such as length bias and degenerate repeti…

cs.LG2019

Plug-in, Trainable Gate for Streamlining Arbitrary Neural Networks

Jaedeok Kim, Chiyoun Park, Hyun-Joo Jung +1

Architecture optimization, which is a technique for finding an efficient neural network that meets certain requirements, generally reduces to a set of multiple-choice selection pro…

cs.LG2019

How Compact?: Assessing Compactness of Representations through Layer-Wise Pruning

Hyun-Joo Jung, Jaedeok Kim, Yoonsuck Choe

Various forms of representations may arise in the many layers embedded in deep neural networks (DNNs). Of these, where can we find the most compact representation? We propose to us…

cs.LG2019

Comparing Sample-wise Learnability Across Deep Neural Network Models

Seung-Geon Lee, Jaedeok Kim, Hyun-Joo Jung +1

Estimating the relative importance of each sample in a training set has important practical and theoretical value, such as in importance sampling or curriculum learning. This kind…

cs.AI20174 cited

Human Understandable Explanation Extraction for Black-box Classification Models Based on Matrix Factorization

Jaedeok Kim, Jingoo Seo

In recent years, a number of artificial intelligent services have been developed such as defect detection system or diagnosis system for customer services. Unfortunately, the core…