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Loss Functions for Multiset Prediction
Sean Welleck, Zixin Yao, Yu Gai, Jialin Mao, Zheng Zhang, Kyunghyun Cho
Feb 15, 2018 (modified: Feb 15, 2018)ICLR 2018 Conference Blind Submissionreaders: everyoneShow Bibtex
Abstract:We study the problem of multiset prediction. The goal of multiset prediction is to train a predictor that maps an input to a multiset consisting of multiple items. Unlike existing problems in supervised learning, such as classification, ranking and sequence generation, there is no known order among items in a target multiset, and each item in the multiset may appear more than once, making this problem extremely challenging. In this paper, we propose a novel multiset loss function by viewing this problem from the perspective of sequential decision making. The proposed multiset loss function is empirically evaluated on two families of datasets, one synthetic and the other real, with varying levels of difficulty, against various baseline loss functions including reinforcement learning, sequence, and aggregated distribution matching loss functions. The experiments reveal the effectiveness of the proposed loss function over the others.
TL;DR:We study the problem of multiset prediction and propose a novel multiset loss function, providing analysis and empirical evidence that demonstrates its effectiveness.
Keywords:machine learning, deep learning, structured prediction, sequential prediction
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