Set based stochastic subsampling

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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 arbitrary downstream task network (e.g. classifier). In the first stage, we efficiently subsample candidate elements using conditionally independent Bernoulli random variables by capturing coarse grained global information using set encoding functions, followed by conditionally dependent autoregressive subsampling of the candidate elements using Categorical random variables by modeling pair-wise interactions using set attention networks in the second stage. We apply our method to feature and instance selection and show that it outperforms the relevant baselines under low subsampling rates on a variety of tasks including image classification, image reconstruction, function reconstruction and few-shot classification. Additionally, for nonparametric models such as Neural Processes that require to leverage the whole training data at inference time, we show that our method enhances the scalability of these models.
Publisher
International Conference on Machine Learning
Issue Date
2022-07-17
Language
English
Citation

The 39th International Conference on Machine Learning, ICML 2022, pp.619 - 638

ISSN
2640-3498
URI
http://hdl.handle.net/10203/301718
Appears in Collection
AI-Conference Papers(학술대회논문)
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