Probabilistic Abstract Interpretation on Neural Networks via Grids Approximation
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Abstract (arXiv)
Probabilistic abstract interpretation is a theory used to extract particular properties of a computer program when it is infeasible to test every single inputs. In this paper we apply the theory on neural networks for the same purpose: to analyse density distribution flow of all possible inputs of a neural network when a network has uncountably many or countable but infinitely many inputs. We show how this theoretical framework works in neural networks and then discuss different abstract domains and corresponding Moore-Penrose pseudo-inverses together with abstract transformers used in the framework. We also present experimental examples to show how this framework helps to analyse real world problems.
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References · 11
- 2016Towards Evaluating the Robustness of Neural Networkscited by 2,998
- 2013Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Mapscited by 2,371
- 2013Visualizing and Understanding Convolutional Networkscited by 2,290
- 2017Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networkscited by 744
- 2017Network Dissection: Quantifying Interpretability of Deep Visual Representationscited by 708
- 2017Methods for Interpreting and Understanding Deep Neural Networkscited by 345
- 2016Synthesizing the preferred inputs for neurons in neural networks via deep generator networkscited by 181
- 2017Interpretable Convolutional Neural Networkscited by 162
- 2017Interpreting Deep Visual Representations via Network Dissectioncited by 103
- 2017Interpreting CNN Knowledge via an Explanatory Graphcited by 49
- 2016Rule Extraction Algorithm for Deep Neural Networks: A Reviewcited by 22
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