activity
20172021
most citedSQLNet: Generating Structured Queries From Natural Language Without Reinforcement Learning

306 citations · 310 across the 2 of their papers we have counts for

collaborators

6 papers

cs.LG2021

TRS: Transferability Reduced Ensemble via Encouraging Gradient Diversity and Model Smoothness

Zhuolin Yang, Linyi Li, Xiaojun Xu +6

Adversarial Transferability is an intriguing property - adversarial perturbation crafted against one model is also effective against another model, while these models are from diff…

cs.LG2021

Nonlinear Projection Based Gradient Estimation for Query Efficient Blackbox Attacks

Huichen Li, Linyi Li, Xiaojun Xu +3

Gradient estimation and vector space projection have been studied as two distinct topics. We aim to bridge the gap between the two by investigating how to efficiently estimate grad…

cs.LG20204 cited

QEBA: Query-Efficient Boundary-Based Blackbox Attack

Huichen Li, Xiaojun Xu, Xiaolu Zhang +2

Machine learning (ML), especially deep neural networks (DNNs) have been widely used in various applications, including several safety-critical ones (e.g. autonomous driving). As a…

cs.AI2019

Detecting AI Trojans Using Meta Neural Analysis

Xiaojun Xu, Qi Wang, Huichen Li +3

In machine learning Trojan attacks, an adversary trains a corrupted model that obtains good performance on normal data but behaves maliciously on data samples with certain trigger…

cs.CR2018

A Machine Learning Approach To Prevent Malicious Calls Over Telephony Networks

Huichen Li, Xiaojun Xu, Chang Liu +6

Malicious calls, i.e., telephony spams and scams, have been a long-standing challenging issue that causes billions of dollars of annual financial loss worldwide. This work presents…

cs.CL2017306 cited

SQLNet: Generating Structured Queries From Natural Language Without Reinforcement Learning

Xiaojun Xu, Chang Liu, Dawn Song

Synthesizing SQL queries from natural language is a long-standing open problem and has been attracting considerable interest recently. Toward solving the problem, the de facto appr…