Introduces first PPH for ℓ1-distance predicate with O(t²) runtime to force significant noise in adversarial image attacks.
Available: http://yann.lecun.com/exdb/mnist/
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
representative citing papers
Eventax enables exact-gradient training of arbitrary ODE-defined spiking neuron models in JAX via differentiable ODE solvers integrated with event detection.
citing papers explorer
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Property-Preserving Hashing for $\ell_1$-Distance Predicates: Applications to Countering Adversarial Input Attacks
Introduces first PPH for ℓ1-distance predicate with O(t²) runtime to force significant noise in adversarial image attacks.
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Training event-based neural networks with exact gradients via Differentiable ODE Solving in JAX
Eventax enables exact-gradient training of arbitrary ODE-defined spiking neuron models in JAX via differentiable ODE solvers integrated with event detection.