mlsanityData to 14 Oct 2026

SAVER: A Toolbox for Sampling-Based, Probabilistic Verification of Neural Networks

arXiv 2412.02940 ↗First posted 4 Dec 2024cs.LG cs.AIcited by 2

Not on the map. Why?

Abstract (arXiv)

We present a neural network verification toolbox to 1) assess the probability of satisfaction of a constraint, and 2) synthesize a set expansion factor to achieve the probability of satisfaction. Specifically, the tool box establishes with a user-specified level of confidence whether the output of the neural network for a given input distribution is likely to be contained within a given set. Should the tool determine that the given set cannot satisfy the likelihood constraint, the tool also implements an approach outlined in this paper to alter the constraint set to ensure that the user-defined satisfaction probability is achieved. The toolbox is comprised of sampling-based approaches which exploit the properties of signed distance function to define set containment.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 14 of its 39 references that are in the map. First posted on arXiv 4 Dec 2024.

Builds on

14 of 39 references.

References · 14

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  2. 2016Universal adversarial perturbationscited by 784
  3. 2017Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networkscited by 744
  4. 2017One pixel attack for fooling deep neural networkscited by 582
  5. 2017An approach to reachability analysis for feed-forward ReLU neural networkscited by 127
  6. 2019Safety Verification and Robustness Analysis of Neural Networks via Quadratic Constraints and Semidefinite Programmingcited by 101
  7. 2017Maximum Resilience of Artificial Neural Networkscited by 97
  8. 2018Neural Lander: Stable Drone Landing Control using Learned Dynamicscited by 85
  9. 2020Enabling certification of verification-agnostic networks via memory-efficient semidefinite programmingcited by 66
  10. 2021CC-Cert: A Probabilistic Approach to Certify General Robustness of Neural Networkscited by 19
  11. 2022A Unified View of SDP-based Neural Network Verification through Completely Positive Programmingcited by 12
  12. 2022Probabilistic Verification of ReLU Neural Networks via Characteristic Functionscited by 5
2 more
  1. 2024Solving Probabilistic Verification Problems of Neural Networks using Branch and Boundcited by 4
  2. 2018Is Ordered Weighted ℓ1 Regularized Regression Robust to Adversarial Perturbation? A Case Study on OSCARcited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2026GenCtrl -- A Formal Controllability Toolkit for Generative Modelscited by 2
  2. 2025BaB-prob: Branch and Bound with Preactivation Splitting for Probabilistic Verification of Neural Networkscited by 0