  Much recent machine learning research has been directed towards leveraging
shared statistics among labels, instances and data views, commonly referred to
as multi-label, multi-instance and multi-view learning. The underlying premises
are that there exist correlations among input parts and among output targets,
and the predictive performance would increase when the correlations are
incorporated. In this paper, we propose Column Bundle (CLB), a novel deep
neural network for capturing the shared statistics in data. CLB is generic that
the same architecture can be applied for various types of shared statistics by
changing only input and output handling. CLB is capable of scaling to thousands
of input parts and output labels by avoiding explicit modeling of pairwise
relations. We evaluate CLB on different types of data: (a) multi-label, (b)
multi-view, (c) multi-view/multi-label and (d) multi-instance. CLB demonstrates
a comparable and competitive performance in all datasets against
state-of-the-art methods designed specifically for each type.
