| AdverSparse: An Adversarial Attack Framework for Deep Spatial-Temporal Graph Neural Networks | 0 | 0.34 | 2022 |
| StructADMM: Achieving Ultrahigh Efficiency in Structured Pruning for DNNs | 0 | 0.34 | 2022 |
| Adversarial Attack Generation Empowered by Min-Max Optimization. | 0 | 0.34 | 2021 |
| A Unified DNN Weight Pruning Framework Using Reweighted Optimization Methods | 1 | 0.37 | 2021 |
| On The Optimal Interdiction Of Transportation Networks | 0 | 0.34 | 2020 |
| SGCN: A Graph Sparsifier Based on Graph Convolutional Networks. | 0 | 0.34 | 2020 |
| Efficient Transformer-based Large Scale Language Representations using Hardware-friendly Block Structured Pruning | 0 | 0.34 | 2020 |
| Generation Of Low Distortion Adversarial Attacks Via Convex Programming | 0 | 0.34 | 2019 |
| ADMM-based Weight Pruning for Real-Time Deep Learning Acceleration on Mobile Devices | 2 | 0.43 | 2019 |
| ADMM-NN: An Algorithm-Hardware Co-Design Framework of DNNs Using Alternating Direction Method of Multipliers. | 15 | 0.62 | 2019 |
| An Ultra-Efficient Memristor-Based DNN Framework with Structured Weight Pruning and Quantization Using ADMM | 0 | 0.34 | 2019 |
| Beyond Adversarial Training: Min-Max Optimization in Adversarial Attack and Defense. | 0 | 0.34 | 2019 |
| Progressive DNN Compression: A Key to Achieve Ultra-High Weight Pruning and Quantization Rates using ADMM. | 2 | 0.36 | 2019 |
| Reinforced Adversarial Attacks on Deep Neural Networks Using ADMM. | 0 | 0.34 | 2018 |
| ADAM-ADMM: A Unified, Systematic Framework of Structured Weight Pruning for DNNs. | 6 | 0.46 | 2018 |
| Systematic Weight Pruning of DNNs using Alternating Direction Method of Multipliers. | 1 | 0.35 | 2018 |
| Progressive Weight Pruning of Deep Neural Networks using ADMM. | 4 | 0.43 | 2018 |
