
Efficient MicroStructured Weight Unification and Pruning for Neural Network Compression
Compressing Deep Neural Network (DNN) models to alleviate the storage an...
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BetaCROWN: Efficient Bound Propagation with Perneuron Split Constraints for Complete and Incomplete Neural Network Verification
Recent works in neural network verification show that cheap incomplete v...
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On Fast Adversarial Robustness Adaptation in ModelAgnostic MetaLearning
Modelagnostic metalearning (MAML) has emerged as one of the most succe...
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ZerothOrder Hybrid Gradient Descent: Towards A Principled BlackBox Optimization Framework
In this work, we focus on the study of stochastic zerothorder (ZO) opti...
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Fast and Complete: Enabling Complete Neural Network Verification with Rapid and Massively Parallel Incomplete Verifiers
Formal verification of neural networks (NNs) is a challenging and import...
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MIMOAided Nonlinear Hybrid Transceiver Design for Multiuser mmWave Systems Relying on TomlinsonHarashima Precoding
Hybrid analogdigital (A/D) transceivers designed for millimeter wave (m...
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Iterative Algorithm Induced DeepUnfolding Neural Networks: Precoding Design for Multiuser MIMO Systems
Optimization theory assisted algorithms have received great attention fo...
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Automatic Perturbation Analysis on General Computational Graphs
Linear relaxation based perturbation analysis for neural networks, which...
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Defending against Backdoor Attack on Deep Neural Networks
Although deep neural networks (DNNs) have achieved a great success in va...
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Towards an Efficient and General Framework of Robust Training for Graph Neural Networks
Graph Neural Networks (GNNs) have made significant advances on several f...
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Lightweight Calibrator: a Separable Component for Unsupervised Domain Adaptation
Existing domain adaptation methods aim at learning features that can be ...
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Evading RealTime Person Detectors by Adversarial Tshirt
It is known that deep neural networks (DNNs) could be vulnerable to adve...
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ZOAdaMM: ZerothOrder Adaptive Momentum Method for BlackBox Optimization
The adaptive momentum method (AdaMM), which uses past gradients to updat...
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MinMax Optimization without Gradients: Convergence and Applications to Adversarial ML
In this paper, we study the problem of constrained robust (minmax) opti...
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REQYOLO: A ResourceAware, Efficient Quantization Framework for Object Detection on FPGAs
Deep neural networks (DNNs), as the basis of object detection, will play...
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On the Design of Blackbox Adversarial Examples by Leveraging Gradientfree Optimization and Operator Splitting Method
Robust machine learning is currently one of the most prominent topics wh...
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Topology Attack and Defense for Graph Neural Networks: An Optimization Perspective
Graph neural networks (GNNs) which apply the deep neural networks to gra...
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Braininspired reverse adversarial examples
A human does not have to see all elephants to recognize an animal as an ...
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Interpreting Adversarial Examples by Activation Promotion and Suppression
It is widely known that convolutional neural networks (CNNs) are vulnera...
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Second Rethinking of Network Pruning in the Adversarial Setting
It is well known that deep neural networks (DNNs) are vulnerable to adve...
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Progressive DNN Compression: A Key to Achieve UltraHigh Weight Pruning and Quantization Rates using ADMM
Weight pruning and weight quantization are two important categories of D...
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Progressive Weight Pruning of Deep Neural Networks using ADMM
Deep neural networks (DNNs) although achieving humanlevel performance i...
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Structured Adversarial Attack: Towards General Implementation and Better Interpretability
When generating adversarial examples to attack deep neural networks (DNN...
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Kaidi Xu
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