@inproceedings{
yu2023actively,
title={Actively learning a Bayesian matrix fusion model with deep side information},
author={Yangyang Yu and Jordan W. Suchow},
booktitle={NeurIPS 2023 Workshop on Adaptive Experimental Design and Active Learning in the Real World},
year={2023},
url={https://openreview.net/forum?id=7R23KXdqGV}
}
High-dimensional deep neural network representations of images and concepts can be
aligned to predict human annotations of diverse stimuli. However, such alignment requires
the costly collection of behavioral responses, such that, in practice, the deep-feature
spaces are only ever sparsely sampled. Here, we propose an active learning approach to
adaptively sample experimental stimuli to efficiently learn a Bayesian matrix factorization
model with deep side information. We observe a significant efficiency gain over a passive
baseline. Furthermore, with a sequential batched sampling strategy, the algorithm is applicable
not only to small datasets collected from traditional laboratory experiments but
also to settings where large-scale crowdsourced data collection is needed to accurately align
the high-dimensional deep feature representations derived from pre-trained networks. This
provides cost-effective solutions for collecting and generating quality-assured predictions in
large-scale behavioral and cognitive studies.
Actively learning a Bayesian matrix fusion model with deep side information
Yangyang Yu Jordan W. Suchow