Model-Based Multiple Instance Learning

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Learn to Predict Sets Using Feed-Forward Neural Networks (2020, background): “Approaches such as mixture models [31], [39], [40], learning distributions from a set of samples [41], [42], model-based multiple instance learning [43], and novelty detection from point pattern data [44], can be counted as few out many examples that use point patterns or sets as input or output and”
Joint Learning of Set Cardinality and State Distribution (2017, methodology): “The normalisation factor m! = ∏m k=1 k between pm(Y) and pm(Y ) appears because the probability density for a set with known cardinality Ym must be equally distributed among all the m! possible permutations of the corresponding vector Y (Mahler 2007; Vo et al. 2017).”