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02/23/2023
PIFON-EPT: MR-Based Electrical Property Tomography Using Physics-Informed Fourier Networks
Objective: In this paper, we introduce Physics-Informed Fourier Networks...
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02/11/2023
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 dynam...
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01/26/2023
Certified Interpretability Robustness for Class Activation Mapping
Interpreting machine learning models is challenging but crucial for ensu...
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10/23/2022
MR-Based Electrical Property Reconstruction Using Physics-Informed Neural Networks
Electrical properties (EP), namely permittivity and electric conductivit...
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10/06/2022
SynBench: Task-Agnostic Benchmarking of Pretrained Representations using Synthetic Data
Recent success in fine-tuning large models, that are pretrained on broad...
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07/22/2022
Learning from Multiple Annotator Noisy Labels via Sample-wise Label Fusion
Data lies at the core of modern deep learning. The impressive performanc...
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06/22/2022
A hybrid volume-surface integral equation method for rapid electromagnetic simulations in MRI
Objective: We developed a hybrid volume surface integral equation (VSIE)...
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12/08/2021
Revisiting Contrastive Learning through the Lens of Neighborhood Component Analysis: an Integrated Framework
As a seminal tool in self-supervised representation learning, contrastiv...
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03/11/2021
Compression of volume-surface integral equation matrices via Tucker decomposition for magnetic resonance applications
In this work, we propose a method for the compression of the coupling ma...
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02/01/2021
Fast Training of Provably Robust Neural Networks by SingleProp
Recent works have developed several methods of defending neural networks...
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10/13/2020
Higher-Order Certification for Randomized Smoothing
Randomized smoothing is a recently proposed defense against adversarial ...
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08/05/2020
Robust Deep Reinforcement Learning through Adversarial Loss
Deep neural networks, including reinforcement learning agents, have been...
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06/26/2020
Proper Network Interpretability Helps Adversarial Robustness in Classification
Recent works have empirically shown that there exist adversarial example...
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03/02/2020
Rethinking Randomized Smoothing for Adversarial Robustness
The fragility of modern machine learning models has drawn a considerable...
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12/19/2019
Towards Verifying Robustness of Neural Networks Against Semantic Perturbations
Verifying robustness of neural networks given a specified threat model i...
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12/02/2019
Fastened CROWN: Tightened Neural Network Robustness Certificates
The rapid growth of deep learning applications in real life is accompani...
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08/18/2019
Verification of Neural Network Control Policy Under Persistent Adversarial Perturbation
Deep neural networks are known to be fragile to small adversarial pertur...
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05/17/2019
POPQORN: Quantifying Robustness of Recurrent Neural Networks
The vulnerability to adversarial attacks has been a critical issue for d...
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12/18/2018
PROVEN: Certifying Robustness of Neural Networks with a Probabilistic Approach
With deep neural networks providing state-of-the-art machine learning mo...
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11/29/2018
CNN-Cert: An Efficient Framework for Certifying Robustness of Convolutional Neural Networks
Verifying robustness of neural network classifiers has attracted great i...
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11/02/2018
Efficient Neural Network Robustness Certification with General Activation Functions
Finding minimum distortion of adversarial examples and thus certifying r...
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10/19/2018
On Extensions of CLEVER: A Neural Network Robustness Evaluation Algorithm
CLEVER (Cross-Lipschitz Extreme Value for nEtwork Robustness) is an Extr...
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04/25/2018
Towards Fast Computation of Certified Robustness for ReLU Networks
Verifying the robustness property of a general Rectified Linear Unit (Re...
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01/31/2018
Evaluating the Robustness of Neural Networks: An Extreme Value Theory Approach
The robustness of neural networks to adversarial examples has received g...
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07/25/2017