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. As a …

Sentiment Analysis can help to extract knowledge related to opinions and emotions from
user generated text information. It can be applied in medical field for patients monitoring …

Interpretability of deep neural networks is a recently emerging area of machine learning
research targeting a better understanding of how models perform feature selection and …

We present a novel algorithm that uses exact learning and abstraction to extract a
deterministic finite automaton describing the state dynamics of a given trained RNN. We do …

The behavior of deep neural networks (DNNs) is hard to understand. This makes it
necessary to explore post hoc explanation methods. We conduct the first comprehensive …

Deep learning models have achieved remarkable success in natural language inference
(NLI) tasks. While these models are widely explored, they are hard to interpret and it is often …

In typical data analysis projects in biology and healthcare, simpler predictive models, such
as regressions and decision trees, enjoy more popularity than more complex and expressive …

Recurrent neural networks (RNNs) are an effective representation of control policies for a
wide range of reinforcement and imitation learning problems. RNN policies, however, are …

Arguing without committing a fallacy is one of the main requirements of an ideal debate. But
even when debating rules are strictly enforced and fallacious arguments punished, arguers …

We introduce an adversarial method for producing high-recall explanations of neural text
classifier decisions. Building on an existing architecture for extractive explanations via hard …