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05/20/2023
Annealing Self-Distillation Rectification Improves Adversarial Training
In standard adversarial training, models are optimized to fit one-hot la...
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02/12/2023
Position Matters! Empirical Study of Order Effect in Knowledge-grounded Dialogue
With the power of large pretrained language models, various research wor...
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02/01/2023
QCRS: Improve Randomized Smoothing using Quasi-Concave Optimization
Randomized smoothing is currently the state-of-the-art method that provi...
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09/22/2022
Fair Robust Active Learning by Joint Inconsistency
Fair Active Learning (FAL) utilized active learning techniques to achiev...
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08/18/2022
Enhancing Targeted Attack Transferability via Diversified Weight Pruning
Malicious attackers can generate targeted adversarial examples by imposi...
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06/08/2022
Few-shot Prompting Towards Controllable Response Generation
Much literature has shown that prompt-based learning is an efficient met...
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02/21/2020
UnMask: Adversarial Detection and Defense Through Robust Feature Alignment
Deep learning models are being integrated into a wide range of high-impa...
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05/30/2018
ADAGIO: Interactive Experimentation with Adversarial Attack and Defense for Audio
Adversarial machine learning research has recently demonstrated the feas...
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04/16/2018
Robust Physical Adversarial Attack on Faster R-CNN Object Detector
Given the ability to directly manipulate image pixels in the digital inp...
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02/19/2018
Shield: Fast, Practical Defense and Vaccination for Deep Learning using JPEG Compression
The rapidly growing body of research in adversarial machine learning has...
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05/08/2017
Keeping the Bad Guys Out: Protecting and Vaccinating Deep Learning with JPEG Compression
Deep neural networks (DNNs) have achieved great success in solving a var...
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06/21/2015
Communication Efficient Distributed Agnostic Boosting
We consider the problem of learning from distributed data in the agnosti...
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06/27/2012
