Search Results for author:
Found 2 papers, 2 papers with code
Addressing Parameter Choice Issues in Unsupervised Domain Adaptation by Aggregation
Our method outperforms deep embedded validation (DEV) and importance weighted validation (IWV) on all datasets, setting a new state-of-the-art performance for solving parameter choice issues in unsupervised domain adaptation with theoretical error guarantees.
Few-Shot Learning by Dimensionality Reduction in Gradient Space
We introduce SubGD, a novel few-shot learning method which is based on the recent finding that stochastic gradient descent updates tend to live in a low-dimensional parameter subspace.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
