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Found 33 papers, 13 papers with code
Neural Network Control Policy Verification With Persistent Adversarial Perturbation
In this paper, we show how to combine recent works on static neural network certification tools with robust control theory to certify a neural network policy in a control loop.
One step closer to unbiased aleatoric uncertainty estimation
Neural networks are powerful tools in various applications, and quantifying their uncertainty is crucial for reliable decision-making.
Rare Event Probability Learning by Normalizing Flows
Next, it estimates the rare event probability by utilizing importance sampling in conjunction with the last proposal.
PIFON-EPT: MR-Based Electrical Property Tomography Using Physics-Informed Fourier Networks
PIFON-EPT is the first method that can simultaneously reconstruct EP and transmit fields from incomplete noisy MR measurements, providing new opportunities for EPT research.
ConCerNet: A Contrastive Learning Based Framework for Automated Conservation Law Discovery and Trustworthy Dynamical System Prediction
Deep neural networks (DNN) have shown great capacity of modeling a dynamical system; nevertheless, they usually do not obey physics constraints such as conservation laws.
Certified Interpretability Robustness for Class Activation Mapping
Interpreting machine learning models is challenging but crucial for ensuring the safety of deep networks in autonomous driving systems.
MR-Based Electrical Property Reconstruction Using Physics-Informed Neural Networks
Electrical properties (EP), namely permittivity and electric conductivity, dictate the interactions between electromagnetic waves and biological tissue.
SynBench: Task-Agnostic Benchmarking of Pretrained Representations using Synthetic Data
Given a pretrained model, the representations of data synthesized from the Gaussian mixture are used to compare with our reference to infer the quality.
Learning from Multiple Annotator Noisy Labels via Sample-wise Label Fusion
Data lies at the core of modern deep learning.
Revisiting Contrastive Learning through the Lens of Neighborhood Component Analysis: an Integrated Framework
With the integrated framework, we achieve up to 6\% improvement on the standard accuracy and 17\% improvement on the robust accuracy.
Efficient Certification for Probabilistic Robustness
Recent developments on the robustness of neural networks have primarily emphasized the notion of worst-case adversarial robustness in both verification and robust training.
Tactics on Refining Decision Boundary for Improving Certification-based Robust Training
In verification-based robust training, existing methods utilize relaxation based methods to bound the worst case performance of neural networks given certain perturbation.
Adversarially Robust Conformal Prediction
By combining conformal prediction with randomized smoothing, our proposed method forms a prediction set with finite-sample coverage guarantee that holds for any data distribution with $\ell_2$-norm bounded adversarial noise, generated by any adversarial attack algorithm.
Fast Training of Provably Robust Neural Networks by SingleProp
Recent works have developed several methods of defending neural networks against adversarial attacks with certified guarantees.
Handling Initial Conditions in Vector Fitting for Real Time Modeling of Power System Dynamics
This paper develops a predictive modeling algorithm, denoted as Real-Time Vector Fitting (RTVF), which is capable of approximating the real-time linearized dynamics of multi-input multi-output (MIMO) dynamical systems via rational transfer function matrices.
Network Topology Invariant Stability Certificates for DC Microgrids with Arbitrary Load Dynamics
DC microgrids are prone to small-signal instabilities due to the presence of tightly regulated loads.
Accelerated Probabilistic Power Flow in Electrical Distribution Networks via Model Order Reduction and Neumann Series Expansion
This subspace is used to construct and update a reduced order model (ROM) of the full nonlinear system, resulting in a highly efficient simulation for future voltage profiles.
Higher-Order Certification for Randomized Smoothing
We also provide a framework that generalizes the calculation for certification using higher-order information.
Robust Deep Reinforcement Learning through Adversarial Loss
To address this issue, we propose RADIAL-RL, a principled framework to train reinforcement learning agents with improved robustness against $l_p$-norm bounded adversarial attacks.
Proper Network Interpretability Helps Adversarial Robustness in Classification
Recent works have empirically shown that there exist adversarial examples that can be hidden from neural network interpretability (namely, making network interpretation maps visually similar), or interpretability is itself susceptible to adversarial attacks.
Hidden Cost of Randomized Smoothing
The fragility of modern machine learning models has drawn a considerable amount of attention from both academia and the public.
Towards Verifying Robustness of Neural Networks Against Semantic Perturbations
Verifying robustness of neural networks given a specified threat model is a fundamental yet challenging task.
Fastened CROWN: Tightened Neural Network Robustness Certificates
We draw inspiration from such work and further demonstrate the optimality of deterministic CROWN (Zhang et al. 2018) solutions in a given linear programming problem under mild constraints.
Efficient Training of Robust and Verifiable Neural Networks
We propose that many common certified defenses can be viewed under a unified framework of regularization.
Visual Interpretability Alone Helps Adversarial Robustness
Recent works have empirically shown that there exist adversarial examples that can be hidden from neural network interpretability, and interpretability is itself susceptible to adversarial attacks.
Verification of Neural Network Control Policy Under Persistent Adversarial Perturbation
In this paper, we show how to combine recent works on neural network certification tools (which are mainly used in static settings such as image classification) with robust control theory to certify a neural network policy in a control loop.
POPQORN: Quantifying Robustness of Recurrent Neural Networks
The vulnerability to adversarial attacks has been a critical issue for deep neural networks.
PROVEN: Certifying Robustness of Neural Networks with a Probabilistic Approach
With deep neural networks providing state-of-the-art machine learning models for numerous machine learning tasks, quantifying the robustness of these models has become an important area of research.
CNN-Cert: An Efficient Framework for Certifying Robustness of Convolutional Neural Networks
This motivates us to propose a general and efficient framework, CNN-Cert, that is capable of certifying robustness on general convolutional neural networks.
Efficient Neural Network Robustness Certification with General Activation Functions
Finding minimum distortion of adversarial examples and thus certifying robustness in neural network classifiers for given data points is known to be a challenging problem.
On Extensions of CLEVER: A Neural Network Robustness Evaluation Algorithm
We apply extreme value theory on the new formal robustness guarantee and the estimated robustness is called second-order CLEVER score.
Towards Fast Computation of Certified Robustness for ReLU Networks
Verifying the robustness property of a general Rectified Linear Unit (ReLU) network is an NP-complete problem [Katz, Barrett, Dill, Julian and Kochenderfer CAV17].
Evaluating the Robustness of Neural Networks: An Extreme Value Theory Approach
Our analysis yields a novel robustness metric called CLEVER, which is short for Cross Lipschitz Extreme Value for nEtwork Robustness.
