Contextual HyperNetworks for Novel Feature Adaptation

• URL: http://arxiv.org/abs/2104.05860v1
• Date: Mon, 12 Apr 2021 23:19:49 GMT
• Title: Contextual HyperNetworks for Novel Feature Adaptation
• Authors: Angus Lamb, Evgeny Saveliev, Yingzhen Li, Sebastian Tschiatschek, Camilla Longden, Simon Woodhead, Jos\'e Miguel Hern\'andez-Lobato, Richard E. Turner, Pashmina Cameron, Cheng Zhang
• Abstract summary: Contextual HyperNetwork (CHN) generates parameters for extending the base model to a new feature. At prediction time, the CHN requires only a single forward pass through a neural network, yielding a significant speed-up. We show that this system obtains improved few-shot learning performance for novel features over existing imputation and meta-learning baselines.
• Score: 43.49619456740745
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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