Methods for Interpreting and Understanding Deep Neural Networks

This paper provides an entry point to the problem of interpreting a deep neural network model and explaining its predictions. It is based on a tutorial given at ICASSP 2017. It introduces some recently proposed techniques of interpretation, along with theory, tricks and recommendations, to make most efficient use of these techniques on real data. It also discusses a number of practical applications.Comment: 14 pages, 10 figure

Explaining Recurrent Neural Network Predictions in Sentiment Analysis

Recently, a technique called Layer-wise Relevance Propagation (LRP) was shown to deliver insightful explanations in the form of input space relevances for understanding feed-forward neural network classification decisions. In the present work, we extend the usage of LRP to recurrent neural networks. We propose a specific propagation rule applicable to multiplicative connections as they arise in recurrent network architectures such as LSTMs and GRUs. We apply our technique to a word-based bi-directional LSTM model on a five-class sentiment prediction task, and evaluate the resulting LRP relevances both qualitatively and quantitatively, obtaining better results than a gradient-based related method which was used in previous work.Comment: 9 pages, 4 figures, accepted for EMNLP'17 Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis (WASSA

Schichtweise Repräsentationen in Tiefen Neuronalen Netzen

Es ist bekannt, dass tiefe neuronale Netze eine effiziente interne Repräsentation des Lernproblems bilden. Es ist jedoch unklar, wie sich diese effiziente Repräsentation über die Schichten verteilt und wie sie beim Lernen entsteht. In dieser Arbeit entwickeln wir eine Kernel-basierte Analyse für tiefe Netze. Diese Analyse quantifiziert die Repräsentation in jeder Schicht in Bezug auf Rauschen und Dimensionalität. Wir wenden die Analyse auf Backpropagation-Netze und tiefe Boltzmann-Maschinen an und messen die schichtweise Reduzierung von Rauschen und Dimensionalität. Die Analyse zeigt auch den störenden Einfluss des Lernrauschens: Dieses verhindert die Entstehung komplexer Strukturen in tiefen Modellen.It is well-known that deep neural networks are forming an efficient internal representation of the learning problem. However, it is unclear how this efficient representation is distributed layer-wise, and how it arises from learning. In this thesis, we develop a kernel-based analysis for deep networks that quantifies the representation at each layer in terms of noise and dimensionality. The analysis is applied to backpropagation networks and deep Boltzmann machines, and is able to capture the layer-wise reduction of noise and dimensionality. The analysis also reveals the disrupting effect of learning noise, and how it prevents the emergence of highly sophisticated deep models