Proceedings of the Workshop on Artificial Intelligence Safety 2023 (SafeAI 2023) co-located with the Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI 2023), 2023

2022

Automatic Mapping of the Best-Suited DNN Pruning Schemes for Real-Time Mobile Acceleration.

[BibT_eX]

[DOI]

ACM Trans. Design Autom. Electr. Syst., 2022

Efficient Multi-Prize Lottery Tickets: Enhanced Accuracy, Training, and Inference Speed.

[BibT_eX]

[DOI]

CoRR, 2022

More or Less (MoL): Defending against Multiple Perturbation Attacks on Deep Neural Networks through Model Ensemble and Compression.

[BibT_eX]

[DOI]

Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision Workshops, 2022

Advancing Model Pruning via Bi-level Optimization.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

BLCR: Towards Real-time DNN Execution with Block-based Reweighted Pruning.

[BibT_eX]

[DOI]

Proceedings of the 23rd International Symposium on Quality Electronic Design, 2022

Learning to Generate Image Source-Agnostic Universal Adversarial Perturbations.

[BibT_eX]

[DOI]

Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022

Pruning-as-Search: Efficient Neural Architecture Search via Channel Pruning and Structural Reparameterization.

[BibT_eX]

[DOI]

Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022

All-in-One: A Highly Representative DNN Pruning Framework for Edge Devices with Dynamic Power Management.

[BibT_eX]

[DOI]

Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design, 2022

Compiler-Aware Neural Architecture Search for On-Mobile Real-time Super-Resolution.

[BibT_eX]

[DOI]

Proceedings of the Computer Vision - ECCV 2022, 2022

2021

Achieving Real-Time Object Detection on MobileDevices with Neural Pruning Search.

[BibT_eX]

[DOI]

CoRR, 2021

High-Robustness, Low-Transferability Fingerprinting of Neural Networks.

[BibT_eX]

[DOI]

CoRR, 2021

CoCoPIE: enabling real-time AI on off-the-shelf mobile devices via compression-compilation co-design.

[BibT_eX]

[DOI]

Commun. ACM, 2021

Brief Industry Paper: Towards Real-Time 3D Object Detection for Autonomous Vehicles with Pruning Search.

[BibT_eX]

[DOI]

Proceedings of the 27th IEEE Real-Time and Embedded Technology and Applications Symposium, 2021

Characteristic Examples: High-Robustness, Low-Transferability Fingerprinting of Neural Networks.

[BibT_eX]

[DOI]

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021

A Compression-Compilation Framework for On-mobile Real-time BERT Applications.

[BibT_eX]

[DOI]

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021

Achieving on-Mobile Real-Time Super-Resolution with Neural Architecture and Pruning Search.

[BibT_eX]

[DOI]

Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision, 2021

Neural Pruning Search for Real-Time Object Detection of Autonomous Vehicles.

[BibT_eX]

[DOI]

Proceedings of the 58th ACM/IEEE Design Automation Conference, 2021

NPAS: A Compiler-Aware Framework of Unified Network Pruning and Architecture Search for Beyond Real-Time Mobile Acceleration.

[BibT_eX]

[DOI]

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2021

Intrinsic Examples: Robust Fingerprinting of Deep Neural Networks.

[BibT_eX]

[DOI]

Proceedings of the 32nd British Machine Vision Conference 2021, 2021

2020

Exploring GPU acceleration of Deep Neural Networks using Block Circulant Matrices.

[BibT_eX]

[DOI]

Parallel Comput., 2020

Achieving Real-Time LiDAR 3D Object Detection on a Mobile Device.

[BibT_eX]

[DOI]

CoRR, 2020

6.7ms on Mobile with over 78% ImageNet Accuracy: Unified Network Pruning and Architecture Search for Beyond Real-Time Mobile Acceleration.

[BibT_eX]

[DOI]

CoRR, 2020

Learned Fine-Tuner for Incongruous Few-Shot Learning.

[BibT_eX]

[DOI]

CoRR, 2020

Achieving Real-Time Execution of Transformer-based Large-scale Models on Mobile with Compiler-aware Neural Architecture Optimization.

[BibT_eX]

[DOI]

CoRR, 2020

A Privacy-Preserving DNN Pruning and Mobile Acceleration Framework.

[BibT_eX]

[DOI]

CoRR, 2020

Defending against Backdoor Attack on Deep Neural Networks.

[BibT_eX]

[DOI]

CoRR, 2020

BLK-REW: A Unified Block-based DNN Pruning Framework using Reweighted Regularization Method.

[BibT_eX]

[DOI]

CoRR, 2020

Towards Real-Time DNN Inference on Mobile Platforms with Model Pruning and Compiler Optimization.

[BibT_eX]

[DOI]

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, 2020

Bridging Mode Connectivity in Loss Landscapes and Adversarial Robustness.

[BibT_eX]

[DOI]

Pu Zhao

Pin-Yu Chen

Payel Das

Karthikeyan Natesan Ramamurthy

Xue Lin

Proceedings of the 8th International Conference on Learning Representations, 2020

3D CNN Acceleration on FPGA using Hardware-Aware Pruning.

[BibT_eX]

[DOI]

Proceedings of the 57th ACM/IEEE Design Automation Conference, 2020

Towards Query-Efficient Black-Box Adversary with Zeroth-Order Natural Gradient Descent.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

Towards Certificated Model Robustness Against Weight Perturbations.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019

Interpreting Adversarial Examples by Activation Promotion and Suppression.

[BibT_eX]

[DOI]

CoRR, 2019

Structured Adversarial Attack: Towards General Implementation and Better Interpretability.

[BibT_eX]

[DOI]

Proceedings of the 7th International Conference on Learning Representations, 2019

On the Design of Black-Box Adversarial Examples by Leveraging Gradient-Free Optimization and Operator Splitting Method.

[BibT_eX]

[DOI]

Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision, 2019

HSIM-DNN: Hardware Simulator for Computation-, Storage- and Power-Efficient Deep Neural Networks.

[BibT_eX]

[DOI]

Proceedings of the 2019 on Great Lakes Symposium on VLSI, 2019

Fault Sneaking Attack: a Stealthy Framework for Misleading Deep Neural Networks.

[BibT_eX]

[DOI]

Proceedings of the 56th Annual Design Automation Conference 2019, 2019

ADMM attack: an enhanced adversarial attack for deep neural networks with undetectable distortions.

[BibT_eX]

[DOI]

Proceedings of the 24th Asia and South Pacific Design Automation Conference, 2019

2018

Structured Adversarial Attack: Towards General Implementation and Better Interpretability.

[BibT_eX]

[DOI]

CoRR, 2018

An ADMM-Based Universal Framework for Adversarial Attacks on Deep Neural Networks.

[BibT_eX]

[DOI]

Proceedings of the 2018 ACM Multimedia Conference on Multimedia Conference, 2018

Defensive dropout for hardening deep neural networks under adversarial attacks.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Computer-Aided Design, 2018

Reinforced Adversarial Attacks on Deep Neural Networks Using ADMM.

[BibT_eX]

[DOI]

Proceedings of the 2018 IEEE Global Conference on Signal and Information Processing, 2018

Defending DNN Adversarial Attacks with Pruning and Logits Augmentation.

[BibT_eX]

[DOI]

Proceedings of the 2018 IEEE Global Conference on Signal and Information Processing, 2018

A deep reinforcement learning framework for optimizing fuel economy of hybrid electric vehicles.

[BibT_eX]

[DOI]

Proceedings of the 23rd Asia and South Pacific Design Automation Conference, 2018

2017

Hierarchical resource allocation and consolidation framework in a multi-core server cluster using a Markov decision process model.

[BibT_eX]

[DOI]

IET Cyper-Phys. Syst.: Theory & Appl., 2017

Pu Zhao

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