# Pruning and Slicing Neural Networks using Formal Verification

@article{Lahav2021PruningAS, title={Pruning and Slicing Neural Networks using Formal Verification}, author={Ori Lahav and Guy Katz}, journal={2021 Formal Methods in Computer Aided Design (FMCAD)}, year={2021}, pages={1-10} }

Deep neural networks (DNNs) play an increasingly important role in various computer systems. In order to create these networks, engineers typically specify a desired topology, and then use an automated training algorithm to select the network’s weights. While training algorithms have been studied extensively and are well understood, the selection of topology remains a form of art, and can often result in networks that are unnecessarily large — and consequently are incompatible with end devices…

## 11 Citations

### On Optimizing Back-Substitution Methods for Neural Network Verification

- Computer ScienceArXiv
- 2022

An approach for making back-substitution produce tighter bounds, and can be integrated into numerous existing symbolic-bound propagation techniques, with only minor modiﬁcations.

### Neural Network Verification with Proof Production

- Computer ScienceArXiv
- 2022

This work presents a novel mechanism for enhancing Simplex-based DNN veriﬁers with proof production capabilities: the generation of an easy-to-check witness of unsatis ﬁability, which attests to the absence of errors.

### Tighter Abstract Queries in Neural Network Verification

- Computer ScienceArXiv
- 2022

CEGARETTE is presented, a novel veriﬁcation mechanism where both the system and the property are abstracted and re ﬁned simultaneously, allowing for quick veri-cation times while avoiding a large number of reﬂnement steps.

### Minimal Multi-Layer Modifications of Deep Neural Networks

- Computer ScienceNSV/FoMLAS@CAV
- 2022

The novel repair procedure implemented in 3M-DNN computes a modification to the network’s weights that corrects its behavior, and attempts to minimize this change via a sequence of calls to a backend, black-box DNN verification engine.

### Towards Formal Approximated Minimal Explanations of Neural Networks

- Computer ScienceArXiv
- 2022

This work considers this work as a step toward leveraging veriﬁcation technology in producing DNNs that are more reliable and comprehensible, and recommends the use of bundles, which allows us to arrive at more succinct and interpretable explanations.

### Towards Scalable Verification of Deep Reinforcement Learning

- Computer Science2021 Formal Methods in Computer Aided Design (FMCAD)
- 2021

This work presents the whiRL 2.0 tool, which implements a new approach for verifying complex properties of interest for DRL systems, and proposes techniques for performing k-induction and semi-automated invariant inference on such systems.

### CheckINN: Wide Range Neural Network Verification in Imandra

- Computer SciencePPDP
- 2022

Imandra, a functional programming language and a theorem prover originally designed for verification, validation and simulation of financial infrastructure can offer a holistic infrastructure for neural network verification.

### An Abstraction-Refinement Approach to Verifying Convolutional Neural Networks

- Computer Science
- 2022

The core of Cnn-Abs is an abstraction-refinement technique, which simplifies the verification problem through the removal of convolutional connections in a way that soundly creates an over-approximation of the original problem; and which restores these connections if the resulting problem becomes too abstract.

### Verification-Aided Deep Ensemble Selection

- Computer ScienceArXiv
- 2022

This case study harnesses recent advances in DNN veriﬁcation to devise a methodology for identifying ensemble compositions that are less prone to simultaneous errors, even when the input is adversarially perturbed — resulting in more robustly-accurate ensemble-based classiﷁcation.

### Verifying learning-augmented systems

- Computer ScienceSIGCOMM
- 2021

WhiRL is presented, a platform for verifying DRL policies for systems, which combines recent advances in the verification of deep neural networks with scalable model checking techniques, and is capable of guaranteeing that natural requirements from recently introduced learning-augmented systems are satisfied, and of exposing specific scenarios in which other basic requirements are not.

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