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Jaehong Yoon

Nanyang Technological University, Singapore

4 papers hereh-index 224.6k citations79 works total

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

author position
  • first author1
  • middle author3

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • cs.LG3
  • stat.ML1
affiliations
  • Nanyang Technological University, Singapore
Homepage
same name
  • Jaehong Yoon — 3 papers
  • Jaehong Yoon — 2 papers

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
20182020
most citedRapid Structural Pruning of Neural Networks with Set-based Task-Adaptive Meta-Pruning

1 citations · 1 across the 1 of their papers we have counts for

collaborators

4 papers

cs.LG2020★ 1 cited

Rapid Structural Pruning of Neural Networks with Set-based Task-Adaptive Meta-Pruning

Minyoung Song, Jaehong Yoon, Eunho Yang +1

As deep neural networks are growing in size and being increasingly deployed to more resource-limited devices, there has been a recent surge of interest in network pruning methods,…

cs.LG2020

Federated Semi-Supervised Learning with Inter-Client Consistency & Disjoint Learning

Wonyong Jeong, Jaehong Yoon, Eunho Yang +1

While existing federated learning approaches mostly require that clients have fully-labeled data to train on, in realistic settings, data obtained at the client-side often comes wi…

cs.LG2019

Scalable and Order-robust Continual Learning with Additive Parameter Decomposition

Jaehong Yoon, Saehoon Kim, Eunho Yang +1

While recent continual learning methods largely alleviate the catastrophic problem on toy-sized datasets, some issues remain to be tackled to apply them to real-world problem domai…

stat.ML2018

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…

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