research
∙
03/13/2023
Can Adversarial Examples Be Parsed to Reveal Victim Model Information?
Numerous adversarial attack methods have been developed to generate impe...
share
research
∙
02/22/2023
ASSET: Robust Backdoor Data Detection Across a Multiplicity of Deep Learning Paradigms
Backdoor data detection is traditionally studied in an end-to-end superv...
research
∙
11/15/2022
HeatViT: Hardware-Efficient Adaptive Token Pruning for Vision Transformers
While vision transformers (ViTs) have continuously achieved new mileston...
research
∙
07/08/2022
FAIVConf: Face enhancement for AI-based Video Conference with Low Bit-rate
Recently, high-quality video conferencing with fewer transmission bits h...
research
∙
06/02/2022
Pruning-as-Search: Efficient Neural Architecture Search via Channel Pruning and Structural Reparameterization
Neural architecture search (NAS) and network pruning are widely studied ...
research
∙
05/25/2022
Location-free Human Pose Estimation
Human pose estimation (HPE) usually requires large-scale training data t...
research
∙
03/26/2022
Reverse Engineering of Imperceptible Adversarial Image Perturbations
It has been well recognized that neural network based image classifiers ...
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)...
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...
research
∙
05/14/2021
High-Robustness, Low-Transferability Fingerprinting of Neural Networks
This paper proposes Characteristic Examples for effectively fingerprinti...
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...
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...
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...
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...
research
∙
09/29/2020
Learned Fine-Tuner for Incongruous Few-Shot Learning
Model-agnostic meta-learning (MAML) effectively meta-learns an initializ...
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...
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...
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...
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...
research
∙
04/30/2020
Bridging Mode Connectivity in Loss Landscapes and Adversarial Robustness
Mode connectivity provides novel geometric insights on analyzing loss la...
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...
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...
research
∙
03/13/2020
A Privacy-Preserving DNN Pruning and Mobile Acceleration Framework
To facilitate the deployment of deep neural networks (DNNs) on resource-...
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...
research
∙
02/28/2020
Automatic Perturbation Analysis on General Computational Graphs
Linear relaxation based perturbation analysis for neural networks, which...
research
∙
02/26/2020
Defending against Backdoor Attack on Deep Neural Networks
Although deep neural networks (DNNs) have achieved a great success in va...
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...
research
∙
02/19/2020
AdvMS: A Multi-source Multi-cost Defense Against Adversarial Attacks
Designing effective defense against adversarial attacks is a crucial top...
research
∙
02/19/2020
RTMobile: Beyond Real-Time Mobile Acceleration of RNNs for Speech Recognition
Recurrent neural networks (RNNs) based automatic speech recognition has ...
research
∙
02/18/2020
Block Switching: A Stochastic Approach for Deep Learning Security
Recent study of adversarial attacks has revealed the vulnerability of mo...
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)...
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...
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...
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...
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...
research
∙
09/27/2019
Reweighted Proximal Pruning for Large-Scale Language Representation
Recently, pre-trained language representation flourishes as the mainstay...
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...
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 ...
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...
research
∙
07/03/2019
Non-structured DNN Weight Pruning Considered Harmful
Large deep neural network (DNN) models pose the key challenge to energy ...
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...
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...
research
∙
04/03/2019
Interpreting Adversarial Examples by Activation Promotion and Suppression
It is widely known that convolutional neural networks (CNNs) are vulnera...
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...
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...
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...
research
∙
12/12/2018
E-RNN: Design Optimization for Efficient Recurrent Neural Networks in FPGAs
Recurrent Neural Networks (RNNs) are becoming increasingly important for...
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...
research
∙
10/17/2018
Progressive Weight Pruning of Deep Neural Networks using ADMM
Deep neural networks (DNNs) although achieving human-level performance i...
research
∙
09/13/2018
