Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Multimodal Generative Models for Scalable Weakly-Supervised Learning   Multiple modalities often co-occur when describing natural phenomena.
Learning a joint representation of these modalities should yield deeper and
more useful representations. Previous generative approaches to multi-modal
input either do not learn a joint distribution or require additional
computation to handle missing data. Here, we introduce a multimodal variational
autoencoder (MVAE) that uses a product-of-experts inference network and a
sub-sampled training paradigm to solve the multi-modal inference problem.
Notably, our model shares parameters to efficiently learn under any combination
of missing modalities. We apply the MVAE on four datasets and match
state-of-the-art performance using many fewer parameters. In addition, we show
that the MVAE is directly applicable to weakly-supervised learning, and is
robust to incomplete supervision. We then consider two case studies, one of
learning image transformations---edge detection, colorization,
segmentation---as a set of modalities, followed by one of machine translation
between two languages. We find appealing results across this range of tasks.

18
The categories of this paper are: econ
3
The categories of this paper are: physics
9
The categories of this paper are: nlin
24
The categories of this paper are: dg-ga
14
The categories of this paper are: math-ph
12
The categories of this paper are: nucl-th
23
The categories of this paper are: solv-int
29
The categories of this paper are: funct-an
13
The categories of this paper are: quant-ph
31
The categories of this paper are: ao-sci
7
The categories of this paper are: hep-th
30
The categories of this paper are: q-alg
22
The categories of this paper are: chao-dyn
33
The categories of this paper are: plasm-ph
11
The categories of this paper are: hep-lat
35
The categories of this paper are: mtrl-th