  For reliability, it is important that the predictions made by machine
learning methods are interpretable by human. In general, deep neural networks
(DNNs) can provide accurate predictions, although it is difficult to interpret
why such predictions are obtained by DNNs. On the other hand, interpretation of
linear models is easy, although their predictive performance would be low since
real-world data is often intrinsically non-linear. To combine both the benefits
of the high predictive performance of DNNs and high interpretability of linear
models into a single model, we propose neural generators of sparse local linear
models (NGSLLs). The sparse local linear models have high flexibility as they
can approximate non-linear functions. The NGSLL generates sparse linear weights
for each sample using DNNs that take original representations of each sample
(e.g., word sequence) and their simplified representations (e.g., bag-of-words)
as input. By extracting features from the original representations, the weights
can contain rich information to achieve high predictive performance.
Additionally, the prediction is interpretable because it is obtained by the
inner product between the simplified representations and the sparse weights,
where only a small number of weights are selected by our gate module in the
NGSLL. In experiments with real-world datasets, we demonstrate the
effectiveness of the NGSLL quantitatively and qualitatively by evaluating
prediction performance and visualizing generated weights on image and text
classification tasks.
