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20132021
most citedtorchgpipe: On-the-fly Pipeline Parallelism for Training Giant Models

32 citations · 73 across the 12 of their papers we have counts for

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Showing 2020Show all

6 papers · 1 filter

math.PR2020

An -maximal regularity estimate of moments of solutions to second-order stochastic partial differential equations

Ildoo Kim

We obtain uniqueness and existence of a solution to the following second-order stochastic partial differential equation (SPDE) : \begin{align} \label{abs eqn} du= \left( \bar a…

cs.CV2020

Learning Loss for Test-Time Augmentation

Ildoo Kim, Younghoon Kim, Sungwoong Kim

Data augmentation has been actively studied for robust neural networks. Most of the recent data augmentation methods focus on augmenting datasets during the training phase. At the…

math.AP2020

A well-posedness theory in Sobolev spaces for the stochastic magnetohydrodynamic equations in the whole space

Ildoo Kim, Minsuk Yang

We prove the existence of a mild solution to the three dimensional incompressible stochastic magnetohydrodynamic equations in the whole space with the initial data which belong to…

cs.LG2020

AutoCLINT: The Winning Method in AutoCV Challenge 2019

Woonhyuk Baek, Ildoo Kim, Sungwoong Kim +1

NeurIPS 2019 AutoDL challenge is a series of six automated machine learning competitions. Particularly, AutoCV challenges mainly focused on classification tasks on visual domain. I…

cs.DC202032 cited

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,…

cs.CV2020

Spatially Attentive Output Layer for Image Classification

Ildoo Kim, Woonhyuk Baek, Sungwoong Kim

Most convolutional neural networks (CNNs) for image classification use a global average pooling (GAP) followed by a fully-connected (FC) layer for output logits. However, this spat…