Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations

Neural networks are among the most accurate supervised learning methods in use today, but their opacity makes them difficult to trust in critical applications, especially when conditions in training differ from those in test. Recent work on explanations for black-box models has produced tools (e.g. LIME) to show the implicit rules behind predictions, which can help us identify when models are right for the wrong reasons. However, these methods do not scale to explaining entire datasets and cannot correct the problems they reveal. We introduce a method for efficiently explaining and regularizing differentiable models by examining and selectively penalizing their input gradients, which provide a normal to the decision boundary. We apply these penalties both based on expert annotation and in an unsupervised fashion that encourages diverse models with qualitatively different decision boundaries for the same classification problem. On multiple datasets, we show our approach generates faithful explanations and models that generalize much better when conditions differ between training and test

Interpretation of partial discharge activity in the presence of harmonics

Recent work has identified that circumstances of equipment operation can radically change condition monitoring data. This contribution investigates the significance of considering circumstance monitoring on the diagnostic interpretation of such condition monitoring data. Electrical treeing partial discharge data have been subjected to a data mining investigation, providing a platform for classification of harmonic influenced partial discharge patterns. The Total Harmonic Distortion (THD) index was varied to a maximum of 40%. The results show progressive development for interpretation of condition monitoring data, improving the asset manager's holistic view of an asset's health

Hierarchical Temporal Representation in Linear Reservoir Computing

Recently, studies on deep Reservoir Computing (RC) highlighted the role of layering in deep recurrent neural networks (RNNs). In this paper, the use of linear recurrent units allows us to bring more evidence on the intrinsic hierarchical temporal representation in deep RNNs through frequency analysis applied to the state signals. The potentiality of our approach is assessed on the class of Multiple Superimposed Oscillator tasks. Furthermore, our investigation provides useful insights to open a discussion on the main aspects that characterize the deep learning framework in the temporal domain.Comment: This is a pre-print of the paper submitted to the 27th Italian Workshop on Neural Networks, WIRN 201