9 citations · 9 across the 4 of their papers we have counts for
4 papers
Mitigating Staleness in Asynchronous Pipeline Parallelism via Basis Rotation
Hyunji Jung, Sungbin Shin, Namhoon Lee
Asynchronous pipeline parallelism maximizes hardware utilization by eliminating the pipeline bubbles inherent in synchronous execution, offering a path toward efficient large-scale…
Rethinking Pruning Large Language Models: Benefits and Pitfalls of Reconstruction Error Minimization
Sungbin Shin, Wonpyo Park, Jaeho Lee +1
This work suggests fundamentally rethinking the current practice of pruning large language models (LLMs). The way it is done is by divide and conquer: split the model into submodel…
Critical Influence of Overparameterization on Sharpness-aware Minimization
Sungbin Shin, Dongyeop Lee, Maksym Andriushchenko +1
Sharpness-Aware Minimization (SAM) has attracted considerable attention for its effectiveness in improving generalization in deep neural network training by explicitly minimizing s…
A Closer Look at the Intervention Procedure of Concept Bottleneck Models
Sungbin Shin, Yohan Jo, Sungsoo Ahn +1
Concept bottleneck models (CBMs) are a class of interpretable neural network models that predict the target response of a given input based on its high-level concepts. Unlike the s…