  Despite the utility of neural networks (NNs) for astronomical time-series
classification, the proliferation of learning architectures applied to diverse
datasets has thus far hampered a direct intercomparison of different
approaches. Here we perform the first comprehensive study of variants of
NN-based learning and inference for astronomical time-series, aiming to provide
the community with an overview on relative performance and, hopefully, a set of
best-in-class choices for practical implementations. In both supervised and
self-supervised contexts, we study the effects of different
time-series-compatible layer choices, namely the dilated temporal convolutional
neural network (dTCNs), Long-Short Term Memory (LSTM) NNs, Gated Recurrent
Units (GRUs) and temporal convolutional NNs (tCNNs). We also study the efficacy
and performance of encoder-decoder (i.e., autoencoder) networks compared to
direct classification networks, different pathways to include auxiliary
(non-time-series) metadata, and different approaches to incorporate
multi-passband data (i.e., multiple time-series per source).
Performance---applied to a sample of 17,604 variable stars from the MACHO
survey across 10 imbalanced classes---is measured in training convergence time,
classification accuracy, reconstruction error, and generated latent variables.
We find that networks with Recurrent NN (RNNs) generally outperform dTCNs and,
in many scenarios, yield to similar accuracy as tCNNs. In learning time and
memory requirements, convolution-based layers are more performant. We conclude
by discussing the advantages and limitations of deep architectures for variable
star classification, with a particular eye towards next-generation surveys such
as LSST, WFIRST and ZTF2.
