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Found 4 papers, 0 papers with code
LinSyn: Synthesizing Tight Linear Bounds for Arbitrary Neural Network Activation Functions
The most scalable approaches to certifying neural network robustness depend on computing sound linear lower and upper bounds for the network's activation functions.
NeuroDiff: Scalable Differential Verification of Neural Networks using Fine-Grained Approximation
Unfortunately, existing methods either focus on verifying a single network or rely on loose approximations to prove the equivalence of two networks.
DiffRNN: Differential Verification of Recurrent Neural Networks
As the memory footprint and energy consumption of such components become a bottleneck, there is interest in compressing and optimizing such networks using a range of heuristic techniques.
ReluDiff: Differential Verification of Deep Neural Networks
Existing verification techniques such as Reluplex, ReluVal, and DeepPoly provide formal guarantees, but they are designed for analyzing a single network instead of the relationship between two networks.
