Hugging Face Trending Papers
$(\text{DNN})^2$: Doubly Non-Negative Relaxations for Deep Neural Networks
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arXiv Machine Learning
Aug 26
$(\text{DNN})^2$: Doubly Non-Negative Relaxations for Deep Neural Networks
arXiv:2608.24743v1 Announce Type: new Abstract: Existing linear program (LP) and semidefinite program (SDP) relaxations for rectified linear unit (ReLU) neural network (NN) verification yield overly-...
arXiv Machine Learning
Jul 1
ShardNet: Training Neural Controllers with Hard, Non-Convex Constraints
arXiv:2606. 30935v1 Announce Type: cross Abstract: While neural network control policies are powerful, their deployment on safety critical systems depends on ensuring that they obey strict constraints.
arXiv AI
Jun 3
Neural Network Verification using Partial Multi-Neuron Relaxation
arXiv:2605. 30155v3 Announce Type: replace-cross Abstract: The increasing integration of deep neural networks in critical systems has spawned a theoretical and practical interest in formally guaranteeing safety properties about their behavior.
arXiv AI
Jun 24
Are Safety Guarantees in Neural Networks Safe? How to Compute Trustworthy Robustness Certifications
arXiv:2606. 23858v1 Announce Type: cross Abstract: A primary challenge in AI safety is the existence of adversarial examples -- slightly distorted inputs that cause a neural network (NN) to misclassify.
arXiv Machine Learning
Oct 7
CLAD: Constrained Abstract Domain for Neural Network Verification
arXiv:2609.34628v2 Announce Type: replace-cross Abstract: Neural network verification (NNV) formally verifies that a network satisfies a specified property for all inputs within a defined region. Mod...
arXiv AI
Sep 15
L-Lipschitz Gershgorin ResNet Network
The paper introduces a method for constructing L-Lipschitz deep residual networks (ResNets) using a Linear Matrix Inequality (LMI) framework. By reformulating the ResNet architecture as a pseudo-tridiagonal LMI and applying the Gershgorin circle theorem, the authors derive closed‑form constraints on network parameters that guarantee Lipschitz continuity. The work also presents a compositional framework for handling recursive systems in hierarchical architectures, while noting that the Gershgorin-based approximations can over‑constrain the system, reducing expressive capacity.