b'Xue Lin'

research

∙ 03/26/2022

Reverse Engineering of Imperceptible Adversarial Image Perturbations

It has been well recognized that neural network based image classifiers ...

Yifan Gong, et al. ∙

research

∙ 10/30/2021

ILMPQ : An Intra-Layer Multi-Precision Deep Neural Network Quantization framework for FPGA

This work targets the commonly used FPGA (field-programmable gate array)...

Sung-En Chang, et al. ∙

research

∙ 10/30/2021

RMSMP: A Novel Deep Neural Network Quantization Framework with Row-wise Mixed Schemes and Multiple Precisions

This work proposes a novel Deep Neural Network (DNN) quantization framew...

Sung-En Chang, et al. ∙

research

∙ 05/14/2021

High-Robustness, Low-Transferability Fingerprinting of Neural Networks

This paper proposes Characteristic Examples for effectively fingerprinti...

Siyue Wang, et al. ∙

research

∙ 03/11/2021

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...

Shiqi Wang, et al. ∙

research

∙ 12/21/2020

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...

Pranay Sharma, et al. ∙

research

∙ 12/08/2020

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

Deep Neural Networks (DNNs) have achieved extraordinary performance in v...

Sung-En Chang, et al. ∙

research

∙ 11/27/2020

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...

Kaidi Xu, et al. ∙

research

∙ 09/29/2020

Learned Fine-Tuner for Incongruous Few-Shot Learning

Model-agnostic meta-learning (MAML) effectively meta-learns an initializ...

Pu Zhao, et al. ∙

research

∙ 09/16/2020

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

With the tremendous success of deep learning, there exists imminent need...

Sung-En Chang, et al. ∙

research

∙ 09/14/2020

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...

Takami Sato, et al. ∙

research

∙ 08/13/2020

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...

Xixia Xu, et al. ∙

research

∙ 07/20/2020

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

Mobile devices are becoming an important carrier for deep learning tasks...

Wei Niu, et al. ∙

research

∙ 04/30/2020

Bridging Mode Connectivity in Loss Landscapes and Adversarial Robustness

Mode connectivity provides novel geometric insights on analyzing loss la...

Pu Zhao, et al. ∙

research

∙ 04/22/2020

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...

Wei Niu, et al. ∙

research

∙ 03/20/2020

Multi-Person Pose Estimation with Enhanced Feature Aggregation and Selection

We propose a novel Enhanced Feature Aggregation and Selection network (E...

Xixia Xu, et al. ∙

research

∙ 03/13/2020

A Privacy-Preserving DNN Pruning and Mobile Acceleration Framework

To facilitate the deployment of deep neural networks (DNNs) on resource-...

Zheng Zhan, et al. ∙

research

∙ 03/03/2020

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

Lane-Keeping Assistance System (LKAS) is convenient and widely available...

Takami Sato, et al. ∙

research

∙ 02/28/2020

Automatic Perturbation Analysis on General Computational Graphs

Linear relaxation based perturbation analysis for neural networks, which...

Kaidi Xu, et al. ∙

research

∙ 02/26/2020

Defending against Backdoor Attack on Deep Neural Networks

Although deep neural networks (DNNs) have achieved a great success in va...

Hao Cheng, et al. ∙

research

∙ 02/25/2020

Towards an Efficient and General Framework of Robust Training for Graph Neural Networks

Graph Neural Networks (GNNs) have made significant advances on several f...

Kaidi Xu, et al. ∙

research

∙ 02/19/2020

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

Designing effective defense against adversarial attacks is a crucial top...

Xiao Wang, et al. ∙

research

∙ 02/19/2020

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

Recurrent neural networks (RNNs) based automatic speech recognition has ...

Peiyan Dong, et al. ∙

research

∙ 02/18/2020

Block Switching: A Stochastic Approach for Deep Learning Security

Recent study of adversarial attacks has revealed the vulnerability of mo...

Xiao Wang, et al. ∙

research

∙ 02/18/2020

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

Despite the great achievements of the modern deep neural networks (DNNs)...

Pu Zhao, et al. ∙

research

∙ 01/23/2020

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

Accelerating DNN execution on various resource-limited computing platfor...

Xiaolong Ma, et al. ∙

research

∙ 01/01/2020

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...

Wei Niu, et al. ∙

research

∙ 10/18/2019

Evading Real-Time Person Detectors by Adversarial T-shirt

It is known that deep neural networks (DNNs) could be vulnerable to adve...

Kaidi Xu, et al. ∙

research

∙ 10/15/2019

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

The adaptive momentum method (AdaMM), which uses past gradients to updat...

Xiangyi Chen, et al. ∙

research

∙ 09/27/2019

Reweighted Proximal Pruning for Large-Scale Language Representation

Recently, pre-trained language representation flourishes as the mainstay...

Fu-Ming Guo, et al. ∙

research

∙ 09/06/2019

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...

Xiaolong Ma, et al. ∙

research

∙ 08/20/2019

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 ...

Xiao Wang, et al. ∙

research

∙ 07/26/2019

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...

Pu Zhao, et al. ∙

research

∙ 07/03/2019

Non-structured DNN Weight Pruning Considered Harmful

Large deep neural network (DNN) models pose the key challenge to energy ...

Yanzhi Wang, et al. ∙

research

∙ 06/10/2019

Topology Attack and Defense for Graph Neural Networks: An Optimization Perspective

Graph neural networks (GNNs) which apply the deep neural networks to gra...

Kaidi Xu, et al. ∙

research

∙ 05/28/2019

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

Despite the great achievements of deep neural networks (DNNs), the vulne...

Pu Zhao, et al. ∙

research

∙ 04/03/2019

Interpreting Adversarial Examples by Activation Promotion and Suppression

It is widely known that convolutional neural networks (CNNs) are vulnera...

Kaidi Xu, et al. ∙

research

∙ 03/29/2019

Second Rethinking of Network Pruning in the Adversarial Setting

It is well known that deep neural networks (DNNs) are vulnerable to adve...

Shaokai Ye, et al. ∙

research

∙ 03/23/2019

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...

Shaokai Ye, et al. ∙

research

∙ 12/31/2018

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...

Ao Ren, et al. ∙

research

∙ 12/12/2018

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

Recurrent Neural Networks (RNNs) are becoming increasingly important for...

Zhe Li, et al. ∙

research

∙ 11/05/2018

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

Many model compression techniques of Deep Neural Networks (DNNs) have be...

Shaokai Ye, et al. ∙

research

∙ 10/17/2018

Progressive Weight Pruning of Deep Neural Networks using ADMM

Deep neural networks (DNNs) although achieving human-level performance i...

Shaokai Ye, et al. ∙

research

∙ 09/13/2018

Defensive Dropout for Hardening Deep Neural Networks under Adversarial Attacks

Deep neural networks (DNNs) are known vulnerable to adversarial attacks....

Siyue Wang, et al. ∙

research

∙ 08/05/2018

Structured Adversarial Attack: Towards General Implementation and Better Interpretability

When generating adversarial examples to attack deep neural networks (DNN...

Kaidi Xu, et al. ∙

research

∙ 07/29/2018

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

Weight pruning methods of deep neural networks (DNNs) have been demonstr...

Tianyun Zhang, et al. ∙

research

∙ 04/09/2018

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

Deep neural networks (DNNs) are known vulnerable to adversarial attacks....

Pu Zhao, et al. ∙

research

∙ 03/28/2018

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

Thermoelectric generation (TEG) has increasingly drawn attention for bei...

Hanchen Yang, et al. ∙

research

∙ 03/14/2018

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...

Yanzhi Wang, et al. ∙

research

∙ 02/18/2018

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...

Yanzhi Wang, et al. ∙

Xue Lin

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