7 citations · 8 across the 13 of their papers we have counts for
3 papers · 1 filter
All models are wrong, some are useful: Model Selection with Limited Labels
Patrik Okanovic, Andreas Kirsch, Jannes Kasper +3
We introduce MODEL SELECTOR, a framework for label-efficient selection of pretrained classifiers. Given a pool of unlabeled target data, MODEL SELECTOR samples a small subset of hi…
EntryPrune: Neural Network Feature Selection using First Impressions
Felix Zimmer, Patrik Okanovic, Torsten Hoefler
There is an ongoing effort to develop feature selection algorithms to improve interpretability, reduce computational resources, and minimize overfitting in predictive models. Neura…
High Performance Unstructured SpMM Computation Using Tensor Cores
Patrik Okanovic, Grzegorz Kwasniewski, Paolo Sylos Labini +3
High-performance sparse matrix-matrix (SpMM) multiplication is paramount for science and industry, as the ever-increasing sizes of data prohibit using dense data structures. Yet, e…