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Found 5 papers, 1 papers with code
Learning with distributional inverters
- If there is a strongly useful natural property in the sense of Razborov & Rudich 1997 -- an efficient algorithm that can distinguish between random strings and strings of non-trivial circuit complexity -- then general polynomial-sized Boolean circuits are learnable over any efficiently samplable distribution in randomized polynomial time, given membership queries to the target function
Learning Rules with Stratified Negation in Differentiable ILP.
Differentiable methods to learn rules (logic programs) have the potential to integrate the interpretability, transferability and low data requirements of inductive logic programming with the noise tolerance of non-symbolic learning.
Mind the Gap: On Bridging the Semantic Gap between Machine Learning and Information Security
Specifically, current datasets and representations used by ML are not suitable for learning the behaviors of an executable and differ significantly from those used by the InfoSec community.
Biologically inspired sleep algorithm for increased generalization and adversarial robustness in deep neural networks
In this work, we utilize a biologically inspired sleep phase in ANNs and demonstrate the benefit of sleep on defending against adversarial attacks as well as in increasing ANN classification robustness.
Biologically inspired sleep algorithm for artificial neural networks
First, in an incremental learning framework, sleep is able to recover older tasks that were otherwise forgotten in the ANN without sleep phase due to catastrophic forgetting.
