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researcher

K. Kim

21 papers hereh-index 273.8k citations71 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author6
  • middle author13
  • last author2

Across the 21 of 21 papers where every author was matched, so the position is known.

fields
  • cs.CV17
  • cs.LG4
same name
  • K. Kim — 94 papers, h 44
  • K. Kim — 71 papers, h 43
  • K. Kim — 45 papers
  • K. Kim — 19 papers, h 43
  • K. Kim — 16 papers, h 16
  • K. Kim — 14 papers, h 31

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20162022
most citedCollaborative Learning for Hand and Object Reconstruction with Attention-guided Graph Convolution

18 citations · 24 across the 10 of their papers we have counts for

collaborators
Showing cs.LGShow all

4 papers · 1 filter

cs.LG2021

Testing using Privileged Information by Adapting Features with Statistical Dependence

Kwang In Kim, James Tompkin

Given an imperfect predictor, we exploit additional features at test time to improve the predictions made, without retraining and without knowledge of the prediction function. This…

cs.LG2020

Combining Task Predictors via Enhancing Joint Predictability

Kwang In Kim, Christian Richardt, Hyung Jin Chang

Predictor combination aims to improve a (target) predictor of a learning task based on the (reference) predictors of potentially relevant tasks, without having access to the intern…

cs.LG2019★ 1 cited

Implicit Filter Sparsification In Convolutional Neural Networks

Dushyant Mehta, Kwang In Kim, Christian Theobalt

We show implicit filter level sparsity manifests in convolutional neural networks (CNNs) which employ Batch Normalization and ReLU activation, and are trained with adaptive gradien…

cs.LG2018

On Implicit Filter Level Sparsity in Convolutional Neural Networks

Dushyant Mehta, Kwang In Kim, Christian Theobalt

We investigate filter level sparsity that emerges in convolutional neural networks (CNNs) which employ Batch Normalization and ReLU activation, and are trained with adaptive gradie…

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