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- W4288054974 abstract "How to aggregate information from multiple instances is a key question multiple instance learning. Prior neural models implement different variants of the well-known encoder-decoder strategy according to which all input features are encoded a single, high-dimensional embedding which is then decoded to generate an output. In this work, inspired by Choquet capacities, we propose Capacity networks. Unlike encoder-decoders, Capacity networks generate multiple interpretable intermediate results which can be aggregated in a semantically meaningful space to obtain the final output. Our experiments show that implementing this simple inductive bias leads to improvements over different encoder-decoder architectures in a wide range of experiments. Moreover, the interpretable intermediate results make Capacity networks interpretable by design, which allows a semantically meaningful inspection, evaluation, and regularization of the network internals." @default.
- W4288054974 created "2022-07-28" @default.
- W4288054974 creator A5068310484 @default.
- W4288054974 date "2022-07-25" @default.
- W4288054974 modified "2023-10-17" @default.
- W4288054974 title "Effective and Interpretable Information Aggregation with Capacity Networks" @default.
- W4288054974 doi "https://doi.org/10.48550/arxiv.2207.12013" @default.
- W4288054974 hasPublicationYear "2022" @default.
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