  While deep learning has obtained state-of-the-art results in many
applications, the adaptation of neural network architectures to incorporate new
output features remains a challenge, as neural networks are commonly trained to
produce a fixed output dimension. This issue is particularly severe in online
learning settings, where new output features, such as items in a recommender
system, are added continually with few or no associated observations. As such,
methods for adapting neural networks to novel features which are both time and
data-efficient are desired. To address this, we propose the Contextual
HyperNetwork (CHN), an auxiliary model which generates parameters for extending
the base model to a new feature, by utilizing both existing data as well as any
observations and/or metadata associated with the new feature. At prediction
time, the CHN requires only a single forward pass through a neural network,
yielding a significant speed-up when compared to re-training and fine-tuning
approaches.
  To assess the performance of CHNs, we use a CHN to augment a partial
variational autoencoder (P-VAE), a deep generative model which can impute the
values of missing features in sparsely-observed data. We show that this system
obtains improved few-shot learning performance for novel features over existing
imputation and meta-learning baselines across recommender systems, e-learning,
and healthcare tasks.
