Yao Qin

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Università di Trento ∙ University of California, San Diego

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CAT-Gen: Improving Robustness in NLP Models via Controlled Adversarial Text Generation

NLP models are shown to suffer from robustness issues, i.e., a model's p...
10/05/2020 ∙ by Tianlu Wang, et al. ∙ 0 ∙ share

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Deflecting Adversarial Attacks

There has been an ongoing cycle where stronger defenses against adversar...
02/18/2020 ∙ by Yao Qin, et al. ∙ 5 ∙ share

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Detecting and Diagnosing Adversarial Images with Class-Conditional Capsule Reconstructions

Adversarial examples raise questions about whether neural network models...
07/05/2019 ∙ by Yao Qin, et al. ∙ 5 ∙ share

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Imperceptible, Robust, and Targeted Adversarial Examples for Automatic Speech Recognition

Adversarial examples are inputs to machine learning models designed by a...
03/22/2019 ∙ by Yao Qin, et al. ∙ 40 ∙ share

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Transceiver Design for Ambient Backscatter Communication over Frequency-Selective Channels

Existing studies about ambient backscatter communication mostly assume f...
12/29/2018 ∙ by Chong Zhang, et al. ∙ 0 ∙ share

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Tensor Alignment based Domain Adaptation for Hyperspectral Image Classification

This paper presents a tensor alignment (TA) based domain adaptation meth...
08/29/2018 ∙ by Yao Qin, et al. ∙ 0 ∙ share

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Cross-Domain Collaborative Learning via Cluster Canonical Correlation Analysis and Random Walker for Hyperspectral Image Classification

This paper introduces a novel heterogenous domain adaptation (HDA) metho...
08/29/2018 ∙ by Yao Qin, et al. ∙ 4 ∙ share

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Autofocus Layer for Semantic Segmentation

We propose the autofocus convolutional layer for semantic segmentation w...
05/22/2018 ∙ by Yao Qin, et al. ∙ 0 ∙ share

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Hierarchical Cellular Automata for Visual Saliency

Saliency detection, finding the most important parts of an image, has be...
05/26/2017 ∙ by Yao Qin, et al. ∙ 0 ∙ share

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A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction

The Nonlinear autoregressive exogenous (NARX) model, which predicts the ...
04/07/2017 ∙ by Yao Qin, et al. ∙ 0 ∙ share

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