40 citations · 89 across the 9 of their papers we have counts for
9 papers
A multi-functional simulation platform for on-demand ride service operations
Siyuan Feng, Taijie Chen, Yuhao Zhang +3
On-demand ride services or ride-sourcing services have been experiencing fast development in the past decade. Various mathematical models and optimization algorithms have been deve…
Reliability Assurance for Deep Neural Network Architectures Against Numerical Defects
Linyi Li, Yuhao Zhang, Luyao Ren +2
With the widespread deployment of deep neural networks (DNNs), ensuring the reliability of DNN-based systems is of great importance. Serious reliability issues such as system failu…
PECAN: A Deterministic Certified Defense Against Backdoor Attacks
Yuhao Zhang, Aws Albarghouthi, Loris D'Antoni
Neural networks are vulnerable to backdoor poisoning attacks, where the attackers maliciously poison the training set and insert triggers into the test input to change the predicti…
An Ultra-low Power TinyML System for Real-time Visual Processing at Edge
Kunran Xu, Huawei Zhang, Yishi Li +3
Tiny machine learning (TinyML), executing AI workloads on resource and power strictly restricted systems, is an important and challenging topic. This brief firstly presents an extr…
BagFlip: A Certified Defense against Data Poisoning
Yuhao Zhang, Aws Albarghouthi, Loris D'Antoni
Machine learning models are vulnerable to data-poisoning attacks, in which an attacker maliciously modifies the training set to change the prediction of a learned model. In a trigg…
Towards Lossless ANN-SNN Conversion under Ultra-Low Latency with Dual-Phase Optimization
Ziming Wang, Shuang Lian, Yuhao Zhang +3
Spiking neural networks (SNNs) operating with asynchronous discrete events show higher energy efficiency with sparse computation. A popular approach for implementing deep SNNs is A…
Certified Robustness to Programmable Transformations in LSTMs
Yuhao Zhang, Aws Albarghouthi, Loris D'Antoni
Deep neural networks for natural language processing are fragile in the face of adversarial examples -- small input perturbations, like synonym substitution or word duplication, wh…
Robustness to Programmable String Transformations via Augmented Abstract Training
Yuhao Zhang, Aws Albarghouthi, Loris D'Antoni
Deep neural networks for natural language processing tasks are vulnerable to adversarial input perturbations. In this paper, we present a versatile language for programmatically sp…
MULDEF: Multi-model-based Defense Against Adversarial Examples for Neural Networks
Siwakorn Srisakaokul, Yuhao Zhang, Zexuan Zhong +3
Despite being popularly used in many applications, neural network models have been found to be vulnerable to adversarial examples, i.e., carefully crafted examples aiming to mislea…