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Irene Kim

2 accepted papers

2022

Predicting the Generalization Gap in Deep Models using Anchoring

ICASSP 2022accepted

We address the problem of predicting the generalization gap of deep neural networks under large, natural, and synthetic distribution shifts between source and target domains. This is crucial in understanding how models behave in uncontrollable ‘in-the-wild’ scenarios, but existing techniques fail wh…

Cited by 0SourceScholar
2019

Understanding Deep Neural Networks through Input Uncertainties

ICASSP 2019accepted

Techniques for understanding the functioning of complex machine learning models are becoming increasingly popular, not only to improve the validation process, but also to extract new insights about the data via exploratory analysis. Though a large class of such tools currently exists, most assume th…

Cited by 0SourceScholar