activity
20172020
most citedAdversarial Examples: Attacks and Defenses for Deep Learning

221 citations · 314 across the 8 of their papers we have counts for

collaborators

8 papers

cs.CL2020★ 1 cited

Don't Parse, Insert: Multilingual Semantic Parsing with Insertion Based Decoding

Qile Zhu, Haidar Khan, Saleh Soltan +2

Semantic parsing is one of the key components of natural language understanding systems. A successful parse transforms an input utterance to an action that is easily understood by…

cs.CL2020★ 1 cited

Asking Complex Questions with Multi-hop Answer-focused Reasoning

Xiyao Ma, Qile Zhu, Yanlin Zhou +2

Asking questions from natural language text has attracted increasing attention recently, and several schemes have been proposed with promising results by asking the right question…

cs.LG2020★ 4 cited

A Batch Normalized Inference Network Keeps the KL Vanishing Away

Qile Zhu, Jianlin Su, Wei Bi +4

Variational Autoencoder (VAE) is widely used as a generative model to approximate a model's posterior on latent variables by combining the amortized variational inference and deep…

cs.CL2020★ 14 cited

Local Contextual Attention with Hierarchical Structure for Dialogue Act Recognition

Zhigang Dai, Jinhua Fu, Qile Zhu +3

Dialogue act recognition is a fundamental task for an intelligent dialogue system. Previous work models the whole dialog to predict dialog acts, which may bring the noise from unre…

cs.CL2019★ 3 cited

Improving Question Generation with Sentence-level Semantic Matching and Answer Position Inferring

Xiyao Ma, Qile Zhu, Yanlin Zhou +2

Taking an answer and its context as input, sequence-to-sequence models have made considerable progress on question generation. However, we observe that these approaches often gener…

cs.LG2017★ 221 cited

Adversarial Examples: Attacks and Defenses for Deep Learning

Xiaoyong Yuan, Pan He, Qile Zhu +1

With rapid progress and significant successes in a wide spectrum of applications, deep learning is being applied in many safety-critical environments. However, deep neural networks…

cs.CR2017★ 12 cited

Learning Fast and Slow: PROPEDEUTICA for Real-time Malware Detection

Ruimin Sun, Xiaoyong Yuan, Pan He +5

Existing malware detectors on safety-critical devices have difficulties in runtime detection due to the performance overhead. In this paper, we introduce PROPEDEUTICA, a framework…

cs.CV2017★ 58 cited

Single Shot Text Detector with Regional Attention

Pan He, Weilin Huang, Tong He +3

We present a novel single-shot text detector that directly outputs word-level bounding boxes in a natural image. We propose an attention mechanism which roughly identifies text reg…