Haiyan JIANG (she/her) - Postdoc Research Associate @ NYUAD
Publications
Currently, my research focuses on developing attack and defense algorithms for deep neural networks (DNNs) in both supervised and self-supervised learning,
as well as adversarial machine learning.
From 2022 to 2024, I worked on supervised training algorithms for spiking neural networks (SNNs),
with an emphasis on direct supervised training and ANN-to-SNN conversion (i.e., transfer learning),
collaborating with professors at MBZUAI.
From 2020 to 2022, I focused on developing statistical machine learning algorithms at Baidu Research (China).
Prior to that (2013–2020), my research was centered on time series prediction using neural networks.
In the following list,
† denotes equal contribution, and ✉ indicates the corresponding author.
[Selected Conference Papers,
Selected Journal Articles]
Selected Conference Papers
H. Jiang, G.D. Masi, H. Xiong and B. Gu
NDOT: Neuronal Dynamics-based Online Training for Spiking Neural Networks.
In International Conference on Machine Learning
(ICML 2024) ,Vienna, Austria.
[pdf]
[OpenReview]
[Code]
H. Jiang, V. Zoonekynd, G.D. Masi, B. Gu, and H. Xiong
TAB: Temporal Accumulated Batch Normalization in Spiking Neural Networks.
In International Conference on Learning Representations
(ICLR 2024) ,Vienna, Austria.
[pdf]
[OpenReview]
[Code]
H. Jiang, S. Anumasa, G.D. Masi, H. Xiong, and B. Gu
A unified optimization framework of ANN-SNN Conversion: towards optimal mapping from activation values
to firing rates.
In International Conference on Machine Learning
(ICML 2023) , Hawaii, US.
[pdf]
[OpenReview]
[Code]
Y. Zhang, H. Jiang✉, H. Ren✉, C. Zou, and D. Dou.
AutoMS: Automatic model selection for novelty detection with error rate control.
In Advances in Neural Information Processing Systems
(NeurIPS 2022).
[pdf]
[Code]
Selected Journal Articles
H. Jiang, S. Qin, and O.H.M. Padilla.
Feature Grouping and Sparse Principal Component Analysis with Truncated Regularization.
Stat, 12(1), 2023, e538.
[Link]
[Code]
[arxiv]
X. Li, H. Xiong, X. Li, X. Zhang, J. Liu, H. Jiang, Z. Chen, and D. Dou
G-LIME: Statistical learning for local interpretations of deep neural networks using global priors.
Artificial Intelligence 314 (2023): 103823.
[pdf]
H. Jiang, H. Xiong, D. Wu, J. Liu, and D. Dou.
AgFlow: fast model selection of penalized PCA via implicit regularization effects of gradient flow.
Machine Learning 110(8), 2021, 2131-2150.
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases
(ECML PKDD 2021)
[Link]
[arxiv]
J. Liu, T. Huang, H. Xiong, J. Huang, J. Zhou, H. Jiang, G. Yang, H. Wang, and D. Dou
Analysis of collective response reveals that Covid-19-related activities start from the end of 2019 in mainland China.
medRxiv, 2020-10.
[Link]
H. Jiang, J. Li, and Z. Li
Determining the number of change-point via high-dimensional cross-validation.
Stat, 9(1), 2020, e284.
[Link]
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