b'Xue Lin'

Beta-CROWN: Efficient Bound Propagation with Per-neuron Split Constraints for Complete and Incomplete Neural Network Verification

Recent works in neural network verification show that cheap incomplete v...
03/11/2021 ∙ by Shiqi Wang, et al. ∙ 0 ∙ share

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Zeroth-Order Hybrid Gradient Descent: Towards A Principled Black-Box Optimization Framework

In this work, we focus on the study of stochastic zeroth-order (ZO) opti...
12/21/2020 ∙ by Pranay Sharma, et al. ∙ 17 ∙ share

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Mix and Match: A Novel FPGA-Centric Deep Neural Network Quantization Framework

Deep Neural Networks (DNNs) have achieved extraordinary performance in v...
12/08/2020 ∙ by Sung-En Chang, et al. ∙ 6 ∙ share

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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...
11/27/2020 ∙ by Kaidi Xu, et al. ∙ 0 ∙ share

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Learned Fine-Tuner for Incongruous Few-Shot Learning

Model-agnostic meta-learning (MAML) effectively meta-learns an initializ...
09/29/2020 ∙ by Pu Zhao, et al. ∙ 0 ∙ share

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MSP: An FPGA-Specific Mixed-Scheme, Multi-Precision Deep Neural Network Quantization Framework

With the tremendous success of deep learning, there exists imminent need...
09/16/2020 ∙ by Sung-En Chang, et al. ∙ 0 ∙ share

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Hold Tight and Never Let Go: Security of Deep Learning based Automated Lane Centering under Physical-World Attack

Automated Lane Centering (ALC) systems are convenient and widely deploye...
09/14/2020 ∙ by Takami Sato, et al. ∙ 0 ∙ share

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Alleviating Human-level Shift : A Robust Domain Adaptation Method for Multi-person Pose Estimation

Human pose estimation has been widely studied with much focus on supervi...
08/13/2020 ∙ by Xixia Xu, et al. ∙ 0 ∙ share

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Achieving Real-Time Execution of 3D Convolutional Neural Networks on Mobile Devices

Mobile devices are becoming an important carrier for deep learning tasks...
07/20/2020 ∙ by Wei Niu, et al. ∙ 20 ∙ share

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Bridging Mode Connectivity in Loss Landscapes and Adversarial Robustness

Mode connectivity provides novel geometric insights on analyzing loss la...
04/30/2020 ∙ by Pu Zhao, et al. ∙ 7 ∙ share

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

High-end mobile platforms rapidly serve as primary computing devices for...
04/22/2020 ∙ by Wei Niu, et al. ∙ 0 ∙ share

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Multi-Person Pose Estimation with Enhanced Feature Aggregation and Selection

We propose a novel Enhanced Feature Aggregation and Selection network (E...
03/20/2020 ∙ by Xixia Xu, et al. ∙ 10 ∙ share

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A Privacy-Preserving DNN Pruning and Mobile Acceleration Framework

To facilitate the deployment of deep neural networks (DNNs) on resource-...
03/13/2020 ∙ by Zheng Zhan, et al. ∙ 0 ∙ share

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Security of Deep Learning based Lane Keeping System under Physical-World Adversarial Attack

Lane-Keeping Assistance System (LKAS) is convenient and widely available...
03/03/2020 ∙ by Takami Sato, et al. ∙ 0 ∙ share

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Automatic Perturbation Analysis on General Computational Graphs

Linear relaxation based perturbation analysis for neural networks, which...
02/28/2020 ∙ by Kaidi Xu, et al. ∙ 11 ∙ share

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Defending against Backdoor Attack on Deep Neural Networks

Although deep neural networks (DNNs) have achieved a great success in va...
02/26/2020 ∙ by Hao Cheng, et al. ∙ 0 ∙ share

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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...
02/25/2020 ∙ by Kaidi Xu, et al. ∙ 43 ∙ share

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AdvMS: A Multi-source Multi-cost Defense Against Adversarial Attacks

Designing effective defense against adversarial attacks is a crucial top...
02/19/2020 ∙ by Xiao Wang, et al. ∙ 15 ∙ share

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RTMobile: Beyond Real-Time Mobile Acceleration of RNNs for Speech Recognition

Recurrent neural networks (RNNs) based automatic speech recognition has ...
02/19/2020 ∙ by Peiyan Dong, et al. ∙ 0 ∙ share

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Block Switching: A Stochastic Approach for Deep Learning Security

Recent study of adversarial attacks has revealed the vulnerability of mo...
02/18/2020 ∙ by Xiao Wang, et al. ∙ 8 ∙ share

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

Despite the great achievements of the modern deep neural networks (DNNs)...
02/18/2020 ∙ by Pu Zhao, et al. ∙ 25 ∙ share

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

Accelerating DNN execution on various resource-limited computing platfor...
01/23/2020 ∙ by Xiaolong Ma, et al. ∙ 7 ∙ share

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PatDNN: Achieving Real-Time DNN Execution on Mobile Devices with Pattern-based Weight Pruning

With the emergence of a spectrum of high-end mobile devices, many applic...
01/01/2020 ∙ by Wei Niu, et al. ∙ 15 ∙ share

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Evading Real-Time Person Detectors by Adversarial T-shirt

It is known that deep neural networks (DNNs) could be vulnerable to adve...
10/18/2019 ∙ by Kaidi Xu, et al. ∙ 41 ∙ share

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ZO-AdaMM: Zeroth-Order Adaptive Momentum Method for Black-Box Optimization

The adaptive momentum method (AdaMM), which uses past gradients to updat...
10/15/2019 ∙ by Xiangyi Chen, et al. ∙ 16 ∙ share

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Reweighted Proximal Pruning for Large-Scale Language Representation

Recently, pre-trained language representation flourishes as the mainstay...
09/27/2019 ∙ by Fu-Ming Guo, et al. ∙ 0 ∙ share

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PCONV: The Missing but Desirable Sparsity in DNN Weight Pruning for Real-time Execution on Mobile Devices

Model compression techniques on Deep Neural Network (DNN) have been wide...
09/06/2019 ∙ by Xiaolong Ma, et al. ∙ 11 ∙ share

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Protecting Neural Networks with Hierarchical Random Switching: Towards Better Robustness-Accuracy Trade-off for Stochastic Defenses

Despite achieving remarkable success in various domains, recent studies ...
08/20/2019 ∙ by Xiao Wang, et al. ∙ 4 ∙ share

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On the Design of Black-box Adversarial Examples by Leveraging Gradient-free Optimization and Operator Splitting Method

Robust machine learning is currently one of the most prominent topics wh...
07/26/2019 ∙ by Pu Zhao, et al. ∙ 6 ∙ share

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Non-structured DNN Weight Pruning Considered Harmful

Large deep neural network (DNN) models pose the key challenge to energy ...
07/03/2019 ∙ by Yanzhi Wang, et al. ∙ 3 ∙ share

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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...
06/10/2019 ∙ by Kaidi Xu, et al. ∙ 4 ∙ share

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

Despite the great achievements of deep neural networks (DNNs), the vulne...
05/28/2019 ∙ by Pu Zhao, et al. ∙ 0 ∙ share

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Interpreting Adversarial Examples by Activation Promotion and Suppression

It is widely known that convolutional neural networks (CNNs) are vulnera...
04/03/2019 ∙ by Kaidi Xu, et al. ∙ 0 ∙ share

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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...
03/29/2019 ∙ by Shaokai Ye, et al. ∙ 6 ∙ share

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Progressive DNN Compression: A Key to Achieve Ultra-High Weight Pruning and Quantization Rates using ADMM

Weight pruning and weight quantization are two important categories of D...
03/23/2019 ∙ by Shaokai Ye, et al. ∙ 16 ∙ share

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ADMM-NN: An Algorithm-Hardware Co-Design Framework of DNNs Using Alternating Direction Method of Multipliers

To facilitate efficient embedded and hardware implementations of deep ne...
12/31/2018 ∙ by Ao Ren, et al. ∙ 10 ∙ share

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E-RNN: Design Optimization for Efficient Recurrent Neural Networks in FPGAs

Recurrent Neural Networks (RNNs) are becoming increasingly important for...
12/12/2018 ∙ by Zhe Li, et al. ∙ 0 ∙ share

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A Unified Framework of DNN Weight Pruning and Weight Clustering/Quantization Using ADMM

Many model compression techniques of Deep Neural Networks (DNNs) have be...
11/05/2018 ∙ by Shaokai Ye, et al. ∙ 10 ∙ share

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Progressive Weight Pruning of Deep Neural Networks using ADMM

Deep neural networks (DNNs) although achieving human-level performance i...
10/17/2018 ∙ by Shaokai Ye, et al. ∙ 16 ∙ share

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Defensive Dropout for Hardening Deep Neural Networks under Adversarial Attacks

Deep neural networks (DNNs) are known vulnerable to adversarial attacks....
09/13/2018 ∙ by Siyue Wang, et al. ∙ 0 ∙ share

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Structured Adversarial Attack: Towards General Implementation and Better Interpretability

When generating adversarial examples to attack deep neural networks (DNN...
08/05/2018 ∙ by Kaidi Xu, et al. ∙ 16 ∙ share

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ADAM-ADMM: A Unified, Systematic Framework of Structured Weight Pruning for DNNs

Weight pruning methods of deep neural networks (DNNs) have been demonstr...
07/29/2018 ∙ by Tianyun Zhang, et al. ∙ 0 ∙ share

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

Deep neural networks (DNNs) are known vulnerable to adversarial attacks....
04/09/2018 ∙ by Pu Zhao, et al. ∙ 0 ∙ share

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Prediction-Based Fast Thermoelectric Generator Reconfiguration for Energy Harvesting from Vehicle Radiators

Thermoelectric generation (TEG) has increasingly drawn attention for bei...
03/28/2018 ∙ by Hanchen Yang, et al. ∙ 0 ∙ share

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On the Universal Approximation Property and Equivalence of Stochastic Computing-based Neural Networks and Binary Neural Networks

Large-scale deep neural networks are both memory intensive and computati...
03/14/2018 ∙ by Yanzhi Wang, et al. ∙ 0 ∙ share

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Towards Ultra-High Performance and Energy Efficiency of Deep Learning Systems: An Algorithm-Hardware Co-Optimization Framework

Hardware accelerations of deep learning systems have been extensively in...
02/18/2018 ∙ by Yanzhi Wang, et al. ∙ 0 ∙ share

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CirCNN: Accelerating and Compressing Deep Neural Networks Using Block-CirculantWeight Matrices

Large-scale deep neural networks (DNNs) are both compute and memory inte...
08/29/2017 ∙ by Caiwen Ding, et al. ∙ 0 ∙ share

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