REVIEW 5 cited by
Reachable Set Computation and Safety Verification for Neural Networks with ReLU Activations
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
read the original abstract
Neural networks have been widely used to solve complex real-world problems. Due to the complicate, nonlinear, non-convex nature of neural networks, formal safety guarantees for the output behaviors of neural networks will be crucial for their applications in safety-critical systems.In this paper, the output reachable set computation and safety verification problems for a class of neural networks consisting of Rectified Linear Unit (ReLU) activation functions are addressed. A layer-by-layer approach is developed to compute output reachable set. The computation is formulated in the form of a set of manipulations for a union of polyhedra, which can be efficiently applied with the aid of polyhedron computation tools. Based on the output reachable set computation results, the safety verification for a ReLU neural network can be performed by checking the intersections of unsafe regions and output reachable set described by a union of polyhedra. A numerical example of a randomly generated ReLU neural network is provided to show the effectiveness of the approach developed in this paper.
Forward citations
Cited by 5 Pith papers
-
A Symbolic Neural Network Representation and its Application to Understanding, Verifying, and Patching Networks
A symbolic representation that decomposes piecewise-linear neural networks into affine functions enables exact weakest-precondition visualization, bounded model checking, and weight-based patching of trained networks.
-
Computing Linear Restrictions of Neural Networks
A new primitive, ExactLine, partitions any line in the input space of a piecewise-linear neural network into segments where the network is affine, enabling exact decision-boundary analysis, exact integrated gradients,...
-
MLSkip: Data Skipping for ML Filters via Lightweight Metadata
MLSkip demonstrates that lightweight metadata enables data skipping for ReLU-based ML filters, with 27.4% average pruning using min-max and 38.31% using 2D convex hulls on TPC benchmarks, for a 1.07x end-to-end speedup.
-
Exact and Asymptotically Complete Robust Verifications of Neural Networks via Ising Solvers
A QUBO-based neural-network verification framework claims logarithmic spin complexity and asymptotically complete bounds, but the logarithmic encoding is not in the equations and the convergence theorem is unproved.
-
Observer-Based Safety Monitoring of Nonlinear Dynamical Systems with Neural Networks via Quadratic Constraint Approach
This paper gives LMI conditions for designing interval observers that monitor state safety bounds in neural-network-controlled systems, demonstrated on a lateral vehicle control simulation.
Discussion (0). Continue with ORCID to comment.