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Seungwon Lee

3 accepted papers

2023

Benchmark of Machine Learning Force Fields for Semiconductor Simulations: Datasets, Metrics, and Comparative Analysis

NeurIPS 2023poster

As semiconductor devices become miniaturized and their structures become more complex, there is a growing need for large-scale atomic-level simulations as a less costly alternative to the trial-and-error approach during development. Although machine learning force fields (MLFFs) can meet the accurac…

2021

Sharing Less is More: Lifelong Learning in Deep Networks with Selective Layer Transfer

ICML 2021spotlight

Effective lifelong learning across diverse tasks requires the transfer of diverse knowledge, yet transferring irrelevant knowledge may lead to interference and catastrophic forgetting. In deep networks, transferring the appropriate granularity of knowledge is as important as the transfer mechanism,…

2017

Discrete-time dynamic modeling and calibration of differential-drive mobile robots with friction

ICRA 2017poster

Fast and high-fidelity dynamic model is very useful for planning, control, and estimation. Here, we present a fixed-time-step, discrete-time dynamic model of differential-drive vehicle with friction for reliable velocity prediction, which is fast, stable, and easy to calibrate. Unlike existing metho…

Cited by 14SourceScholar