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04/03/2023
Model-Agnostic Reachability Analysis on Deep Neural Networks
Verification plays an essential role in the formal analysis of safety-cr...
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03/03/2023
RePreM: Representation Pre-training with Masked Model for Reinforcement Learning
Inspired by the recent success of sequence modeling in RL and the use of...
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01/29/2023
Towards Verifying the Geometric Robustness of Large-scale Neural Networks
Deep neural networks (DNNs) are known to be vulnerable to adversarial ge...
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01/28/2023
Reachability Analysis of Neural Network Control Systems
Neural network controllers (NNCs) have shown great promise in autonomous...
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01/17/2023
Mortality Prediction with Adaptive Feature Importance Recalibration for Peritoneal Dialysis Patients: a deep-learning-based study on a real-world longitudinal follow-up dataset
Objective: Peritoneal Dialysis (PD) is one of the most widely used life-...
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12/22/2022
Certified Policy Smoothing for Cooperative Multi-Agent Reinforcement Learning
Cooperative multi-agent reinforcement learning (c-MARL) is widely applie...
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09/05/2022
Adversarial Detection: Attacking Object Detection in Real Time
Intelligent robots rely on object detection models to perceive the envir...
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08/01/2022
Understanding Adversarial Robustness of Vision Transformers via Cauchy Problem
Recent research on the robustness of deep learning has shown that Vision...
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07/17/2022
DIMBA: Discretely Masked Black-Box Attack in Single Object Tracking
The adversarial attack can force a CNN-based model to produce an incorre...
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07/15/2022
3DVerifier: Efficient Robustness Verification for 3D Point Cloud Models
3D point cloud models are widely applied in safety-critical scenes, whic...
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07/05/2022
PRoA: A Probabilistic Robustness Assessment against Functional Perturbations
In safety-critical deep learning applications robustness measurement is ...
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11/10/2021
Sparse Adversarial Video Attacks with Spatial Transformations
In recent years, a significant amount of research efforts concentrated o...
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08/24/2021
Adversarial Robustness of Deep Learning: Theory, Algorithms, and Applications
This tutorial aims to introduce the fundamentals of adversarial robustne...
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08/03/2021
Tutorials on Testing Neural Networks
Deep learning achieves remarkable performance on pattern recognition, bu...
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03/16/2021
Adversarial Driving: Attacking End-to-End Autonomous Driving Systems
As the research in deep neural networks advances, deep convolutional net...
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03/04/2021
Gradient-Guided Dynamic Efficient Adversarial Training
Adversarial training is arguably an effective but time-consuming way to ...
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01/04/2021
Fooling Object Detectors: Adversarial Attacks by Half-Neighbor Masks
Although there are a great number of adversarial attacks on deep learnin...
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10/15/2020
Generalizing Universal Adversarial Attacks Beyond Additive Perturbations
The previous study has shown that universal adversarial attacks can fool...
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09/30/2020
Is AI Model Interpretable to Combat with COVID? An Empirical Study on Severity Prediction Task
Black-box nature hinders the deployment of many high-accuracy models in ...
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09/13/2020
Towards the Quantification of Safety Risks in Deep Neural Networks
Safety concerns on the deep neural networks (DNNs) have been raised when...
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07/17/2020
CovidCare: Transferring Knowledge from Existing EMR to Emerging Epidemic for Interpretable Prognosis
Due to the characteristics of COVID-19, the epidemic develops rapidly an...
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04/25/2020
Enabling Cost-Effective Population Health Monitoring By Exploiting Spatiotemporal Correlation: An Empirical Study
Because of its important role in health policy-shaping, population healt...
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11/27/2019
ConCare: Personalized Clinical Feature Embedding via Capturing the Healthcare Context
Predicting the patient's clinical outcome from the historical electronic...
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11/27/2019
AdaCare: Explainable Clinical Health Status Representation Learning via Scale-Adaptive Feature Extraction and Recalibration
Deep learning-based health status representation learning and clinical p...
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12/18/2018
Safety and Trustworthiness of Deep Neural Networks: A Survey
In the past few years, significant progress has been made on deep neural...
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07/10/2018
A Game-Based Approximate Verification of Deep Neural Networks with Provable Guarantees
Despite the improved accuracy of deep neural networks, the discovery of ...
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05/06/2018
Reachability Analysis of Deep Neural Networks with Provable Guarantees
Verifying correctness of deep neural networks (DNNs) is challenging. We ...
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04/30/2018
Concolic Testing for Deep Neural Networks
Concolic testing alternates between CONCrete program execution and symbO...
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04/16/2018
