ICML 2022spotlight0 citations
Set Based Stochastic Subsampling
Bruno Andreis, Seanie Lee, A. Tuan Nguyen, Juho Lee, Eunho Yang, Sung Ju Hwang
Abstract
Deep models are designed to operate on huge volumes of high dimensional data such as images. In order to reduce the volume of data these models must process, we propose a set-based two-stage end-to-end neural subsampling model that is jointly optimized with an
BibTeX
@InProceedings{pmlr-v162-andreis22a,
title = {Set Based Stochastic Subsampling},
author = {Andreis, Bruno and Lee, Seanie and Nguyen, A. Tuan and Lee, Juho and Yang, Eunho and Hwang, Sung Ju},
booktitle = {Proceedings of the 39th International Conference on Machine Learning},
pages = {619--638},
year = {2022},
editor = {Chaudhuri, Kamalika and Jegelka, Stefanie and Song, Le and Szepesvari, Csaba and Niu, Gang and Sabato, Sivan},
volume = {162},
series = {Proceedings of Machine Learning Research},
month = {17--23 Jul},
publisher = {PMLR},
pdf = {https://proceedings.mlr.press/v162/andreis22a/andreis22a.pdf},
url = {https://proceedings.mlr.press/v162/andreis22a.html},
abstract = {Deep models are designed to operate on huge volumes of high dimensional data such as images. In order to reduce the volume of data these models must process, we propose a set-based two-stage end-to-end neural subsampling model that is jointly optimized with an