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Found 8 papers, 3 papers with code
Overcoming the Paradox of Certified Training with Gaussian Smoothing
Training neural networks with high certified accuracy against adversarial examples remains an open problem despite significant efforts.
Expressivity of ReLU-Networks under Convex Relaxations
We show that: (i) more advanced relaxations allow a larger class of univariate functions to be expressed as precisely analyzable ReLU networks, (ii) more precise relaxations can allow exponentially larger solution spaces of ReLU networks encoding the same functions, and (iii) even using the most precise single-neuron relaxations, it is impossible to construct precisely analyzable ReLU networks that express multivariate, convex, monotone CPWL functions.
Understanding Certified Training with Interval Bound Propagation
We, then, derive sufficient and necessary conditions on weight matrices for IBP bounds to become exact and demonstrate that these impose strong regularization, explaining the empirically observed trade-off between robustness and accuracy in certified training.
SMAP: A Novel Heterogeneous Information Framework for Scenario-based Optimal Model Assignment
To address this challenge, a new problem named the Scenario-based Optimal Model Assignment (SOMA) problem is introduced and a novel framework entitled Scenario and Model Associative percepts (SMAP) is developed.
TAPS: Connecting Certified and Adversarial Training
Training certifiably robust neural networks remains a notoriously hard problem.
"Is your explanation stable?": A Robustness Evaluation Framework for Feature Attribution
Experiment results show that the MeTFA-smoothed explanation can significantly increase the robust faithfulness.
Transfer Attacks Revisited: A Large-Scale Empirical Study in Real Computer Vision Settings
To bridge this critical gap, we conduct the first large-scale systematic empirical study of transfer attacks against major cloud-based MLaaS platforms, taking the components of a real transfer attack into account.
Application of Knowledge Graphs to Provide Side Information for Improved Recommendation Accuracy
Information about products and preferences of different users are primarily used to infer preferences for a specific user.
