Training Robust Deep Models for Time-Series Domain: Novel Algorithms and
Theoretical Analysis

Training Robust Deep Models for Time-Series Domain: Novel Algorithms and Theoretical Analysis

9 July 2022

Yan Yan

Papers citing "Training Robust Deep Models for Time-Series Domain: Novel Algorithms and Theoretical Analysis"

8 / 8 papers shown

Title
PePNet: A Periodicity-Perceived Workload Prediction Network Supporting Rare Occurrence of Heavy Workload Feiyi Chen Zhen Qin Hailiang Zhao Shuiguang Deng AI4TS 23 0 0 11 Jul 2023
MHCCL: Masked Hierarchical Cluster-Wise Contrastive Learning for Multivariate Time Series Qianwen Meng Hangwei Qian Yong Liu Li-zhen Cui Yonghui Xu Zhiqi Shen AI4TS 33 33 0 02 Dec 2022
Dynamic Time Warping based Adversarial Framework for Time-Series Domain Taha Belkhouja Yan Yan J. Doppa AAML AI4TS 27 25 0 09 Jul 2022
Adversarial Framework with Certified Robustness for Time-Series Domain via Statistical Features Taha Belkhouja J. Doppa AAML AI4TS 25 11 0 09 Jul 2022
Out-of-Distribution Detection in Time-Series Domain: A Novel Seasonal Ratio Scoring Approach Taha Belkhouja Yan Yan J. Doppa OODD AI4TS 27 3 0 09 Jul 2022
Solving Stochastic Compositional Optimization is Nearly as Easy as Solving Stochastic Optimization Tianyi Chen Yuejiao Sun W. Yin 48 81 0 25 Aug 2020
Linear Convergence of Gradient and Proximal-Gradient Methods Under the Polyak-Łojasiewicz Condition Hamed Karimi J. Nutini Mark W. Schmidt 139 1,204 0 16 Aug 2016
Adversarial examples in the physical world Alexey Kurakin Ian Goodfellow Samy Bengio SILM AAML 296 5,842 0 08 Jul 2016