|Chih-Kuan Yeh||Carnegie Mellon University|
|Joon Kim||Carnegie Mellon University|
|Ian En-Hsu Yen||Carnegie Mellon University|
|Pradeep Ravikumar||Carnegie Mellon University|
The authors propose to explain the predictions of a deep neural network, by pointing to the set of what the authors call representer points in the training set, for a given test point prediction.
We propose to explain the predictions of a deep neural network, by pointing to the set of what we call representer points in the training set, for a given test point prediction. Specifically, we show that we can decompose the pre-activation prediction of a neural network into a linear combination of activations of training points, with the weights corresponding to what we call representer values, which thus capture the importance of that training point on the learned parameters of the network. But it provides a deeper understanding of the network than simply training point influence: with positive representer values corresponding to excitatory training points, and negative values corresponding to inhibitory points, which as we show provides considerably more insight. Our method is also much more scalable, allowing for real-time feedback in a manner not feasible with influence functions.