
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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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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Mix and Match: A Novel FPGACentric Deep Neural Network Quantization Framework
Deep Neural Networks (DNNs) have achieved extraordinary performance in v...
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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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Learned FineTuner for Incongruous FewShot Learning
Modelagnostic metalearning (MAML) effectively metalearns an initializ...
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MSP: An FPGASpecific MixedScheme, MultiPrecision Deep Neural Network Quantization Framework
With the tremendous success of deep learning, there exists imminent need...
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Hold Tight and Never Let Go: Security of Deep Learning based Automated Lane Centering under PhysicalWorld Attack
Automated Lane Centering (ALC) systems are convenient and widely deploye...
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Alleviating Humanlevel Shift : A Robust Domain Adaptation Method for Multiperson Pose Estimation
Human pose estimation has been widely studied with much focus on supervi...
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Achieving RealTime Execution of 3D Convolutional Neural Networks on Mobile Devices
Mobile devices are becoming an important carrier for deep learning tasks...
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Bridging Mode Connectivity in Loss Landscapes and Adversarial Robustness
Mode connectivity provides novel geometric insights on analyzing loss la...
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Towards RealTime DNN Inference on Mobile Platforms with Model Pruning and Compiler Optimization
Highend mobile platforms rapidly serve as primary computing devices for...
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MultiPerson Pose Estimation with Enhanced Feature Aggregation and Selection
We propose a novel Enhanced Feature Aggregation and Selection network (E...
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A PrivacyPreserving DNN Pruning and Mobile Acceleration Framework
To facilitate the deployment of deep neural networks (DNNs) on resource...
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Security of Deep Learning based Lane Keeping System under PhysicalWorld Adversarial Attack
LaneKeeping Assistance System (LKAS) is convenient and widely available...
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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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AdvMS: A Multisource Multicost Defense Against Adversarial Attacks
Designing effective defense against adversarial attacks is a crucial top...
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RTMobile: Beyond RealTime Mobile Acceleration of RNNs for Speech Recognition
Recurrent neural networks (RNNs) based automatic speech recognition has ...
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Block Switching: A Stochastic Approach for Deep Learning Security
Recent study of adversarial attacks has revealed the vulnerability of mo...
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Towards QueryEfficient BlackBox Adversary with ZerothOrder Natural Gradient Descent
Despite the great achievements of the modern deep neural networks (DNNs)...
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BLKREW: A Unified Blockbased DNN Pruning Framework using Reweighted Regularization Method
Accelerating DNN execution on various resourcelimited computing platfor...
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PatDNN: Achieving RealTime DNN Execution on Mobile Devices with Patternbased Weight Pruning
With the emergence of a spectrum of highend mobile devices, many applic...
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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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Reweighted Proximal Pruning for LargeScale Language Representation
Recently, pretrained language representation flourishes as the mainstay...
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PCONV: The Missing but Desirable Sparsity in DNN Weight Pruning for Realtime Execution on Mobile Devices
Model compression techniques on Deep Neural Network (DNN) have been wide...
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Protecting Neural Networks with Hierarchical Random Switching: Towards Better RobustnessAccuracy Tradeoff for Stochastic Defenses
Despite achieving remarkable success in various domains, recent studies ...
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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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Nonstructured DNN Weight Pruning Considered Harmful
Large deep neural network (DNN) models pose the key challenge to energy ...
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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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Fault Sneaking Attack: a Stealthy Framework for Misleading Deep Neural Networks
Despite the great achievements of deep neural networks (DNNs), the vulne...
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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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ADMMNN: An AlgorithmHardware CoDesign Framework of DNNs Using Alternating Direction Method of Multipliers
To facilitate efficient embedded and hardware implementations of deep ne...
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ERNN: Design Optimization for Efficient Recurrent Neural Networks in FPGAs
Recurrent Neural Networks (RNNs) are becoming increasingly important for...
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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...
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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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Defensive Dropout for Hardening Deep Neural Networks under Adversarial Attacks
Deep neural networks (DNNs) are known vulnerable to adversarial attacks....
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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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ADAMADMM: A Unified, Systematic Framework of Structured Weight Pruning for DNNs
Weight pruning methods of deep neural networks (DNNs) have been demonstr...
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An ADMMBased Universal Framework for Adversarial Attacks on Deep Neural Networks
Deep neural networks (DNNs) are known vulnerable to adversarial attacks....
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PredictionBased Fast Thermoelectric Generator Reconfiguration for Energy Harvesting from Vehicle Radiators
Thermoelectric generation (TEG) has increasingly drawn attention for bei...
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On the Universal Approximation Property and Equivalence of Stochastic Computingbased Neural Networks and Binary Neural Networks
Largescale deep neural networks are both memory intensive and computati...
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Towards UltraHigh Performance and Energy Efficiency of Deep Learning Systems: An AlgorithmHardware CoOptimization Framework
Hardware accelerations of deep learning systems have been extensively in...
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CirCNN: Accelerating and Compressing Deep Neural Networks Using BlockCirculantWeight Matrices
Largescale deep neural networks (DNNs) are both compute and memory inte...
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Xue Lin
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