Jinfeng Yi (易津锋)
Jinfeng Yi (易津锋)
JD AI Research
Потвърден имейл адрес: jd.com - Начална страница
Zoo: Zeroth order optimization based black-box attacks to deep neural networks without training substitute models
PY Chen, H Zhang, Y Sharma, J Yi, CJ Hsieh
Proceedings of the 10th ACM workshop on artificial intelligence and security …, 2017
Symmetric cross entropy for robust learning with noisy labels
Y Wang, X Ma, Z Chen, Y Luo, J Yi, J Bailey
Proceedings of the IEEE/CVF International Conference on Computer Vision, 322-330, 2019
Ead: elastic-net attacks to deep neural networks via adversarial examples
PY Chen, Y Sharma, H Zhang, J Yi, CJ Hsieh
Proceedings of the AAAI conference on artificial intelligence 32 (1), 2018
Improving adversarial robustness requires revisiting misclassified examples
Y Wang, D Zou, J Yi, J Bailey, X Ma, Q Gu
International Conference on Learning Representations, 2020
Evaluating the robustness of neural networks: An extreme value theory approach
TW Weng, H Zhang, PY Chen, J Yi, D Su, Y Gao, CJ Hsieh, L Daniel
arXiv preprint arXiv:1801.10578, 2018
Is Robustness the Cost of Accuracy?--A Comprehensive Study on the Robustness of 18 Deep Image Classification Models
D Su, H Zhang, H Chen, J Yi, PY Chen, Y Gao
Proceedings of the European conference on computer vision (ECCV), 631-648, 2018
Query-efficient hard-label black-box attack: An optimization-based approach
M Cheng, T Le, PY Chen, J Yi, H Zhang, CJ Hsieh
arXiv preprint arXiv:1807.04457, 2018
Autozoom: Autoencoder-based zeroth order optimization method for attacking black-box neural networks
CC Tu, P Ting, PY Chen, S Liu, H Zhang, J Yi, CJ Hsieh, SM Cheng
Proceedings of the AAAI Conference on Artificial Intelligence 33 (01), 742-749, 2019
On the convergence and robustness of adversarial training
Y Wang, X Ma, J Bailey, J Yi, B Zhou, Q Gu
arXiv preprint arXiv:2112.08304, 2021
Diverse few-shot text classification with multiple metrics
M Yu, X Guo, J Yi, S Chang, S Potdar, Y Cheng, G Tesauro, H Wang, ...
arXiv preprint arXiv:1805.07513, 2018
Seq2sick: Evaluating the robustness of sequence-to-sequence models with adversarial examples
M Cheng, J Yi, PY Chen, H Zhang, CJ Hsieh
Proceedings of the AAAI conference on artificial intelligence 34 (04), 3601-3608, 2020
Practical machine learning
S Gollapudi
Packt Publishing Ltd, 2016
Tracking slowly moving clairvoyant: Optimal dynamic regret of online learning with true and noisy gradient
T Yang, L Zhang, R Jin, J Yi
International Conference on Machine Learning, 449-457, 2016
Attacking visual language grounding with adversarial examples: A case study on neural image captioning
H Chen, H Zhang, PY Chen, J Yi, CJ Hsieh
arXiv preprint arXiv:1712.02051, 2017
Semi-crowdsourced Clustering: Generalizing Crowd Labeling by Robust Distance Metric Learning
J Yi, R Jin, A Jain, S Jain, T Yang
Advances in Neural Information Processing Systems (NIPS), 1781-1789, 2012
Model-agnostic counterfactual reasoning for eliminating popularity bias in recommender system
T Wei, F Feng, J Chen, Z Wu, J Yi, X He
Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data …, 2021
Improved Dynamic Regret for Non-degeneracy Functions
L Zhang, T Yang, J Yi, R Jin, ZH Zhou
arXiv preprint arXiv:1608.03933, 2016
Efficient distance metric learning by adaptive sampling and mini-batch stochastic gradient descent (SGD)
Q Qian, R Jin, J Yi, L Zhang, S Zhu
Machine Learning 99, 353-372, 2015
Robust Ensemble Clustering by Matrix Completion
J Yi, T Yang, R Jin, AK Jain, M Mahdavi
IEEE International Conference on Data Mining (ICDM), 2012
Efficient Algorithms for Robust One-bit Compressive Sensing
L Zhang, J Yi, R Jin
International Conference on Machine Learning (ICML), 820-828, 2014
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