32 citations · 37 across the 4 of their papers we have counts for
5 papers
torchgpipe: On-the-fly Pipeline Parallelism for Training Giant Models
Chiheon Kim, Heungsub Lee, Myungryong Jeong +5
We design and implement a ready-to-use library in PyTorch for performing micro-batch pipeline parallelism with checkpointing proposed by GPipe (Huang et al., 2019). In particular,…
Mining GOLD Samples for Conditional GANs
Sangwoo Mo, Chiheon Kim, Sungwoong Kim +2
Conditional generative adversarial networks (cGANs) have gained a considerable attention in recent years due to its class-wise controllability and superior quality for complex gene…
Fast AutoAugment
Sungbin Lim, Ildoo Kim, Taesup Kim +2
Data augmentation is an essential technique for improving generalization ability of deep learning models. Recently, AutoAugment has been proposed as an algorithm to automatically s…
New Classes of Set-Sequential Trees
Louis Golowich, Chiheon Kim
A graph is called set-sequential if its vertices can be labeled with distinct nonzero vectors in such that when each edge is labeled with the sum of its…
Maximum Size of a Family of Pairwise Graph-Different Permutations
Louis Golowich, Chiheon Kim, Richard Zhou
Two permutations of the vertices of a graph are called -different if there exists an index such that -th entry of the two permutations form an edge in . We bound o…