no code implementations • 14 Jul 2023 • Fei Zhang, Yunjie Ye, Lei Feng, Zhongwen Rao, Jieming Zhu, Marcus Kalander, Chen Gong, Jianye Hao, Bo Han
In this setting, an oracle annotates the query samples with partial labels, relaxing the oracle from the demanding accurate labeling process.
1 code implementation • 9 Mar 2023 • Qizhou Wang, Junjie Ye, Feng Liu, Quanyu Dai, Marcus Kalander, Tongliang Liu, Jianye Hao, Bo Han
It leads to a min-max learning scheme -- searching to synthesize OOD data that leads to worst judgments and learning from such OOD data for uniform performance in OOD detection.
Ranked #12 on Out-of-Distribution Detection on ImageNet-1k vs Textures
no code implementations • 4 Mar 2023 • Jiren Mai, Fei Zhang, Junjie Ye, Marcus Kalander, Xian Zhang, Wankou Yang, Tongliang Liu, Bo Han
Motivated by this simple but effective learning pattern, we propose a General-Specific Learning Mechanism (GSLM) to explicitly drive a coarse-grained CAM to a fine-grained pseudo mask.
no code implementations • 20 May 2022 • Marcus Kalander
In this work, we consider the problem of identifying the root cause set that best explains an anomaly in multi-dimensional time series with categorical attributes.
2 code implementations • 30 Nov 2021 • Keli Zhang, Shengyu Zhu, Marcus Kalander, Ignavier Ng, Junjian Ye, Zhitang Chen, Lujia Pan
$\texttt{gCastle}$ is an end-to-end Python toolbox for causal structure learning.
1 code implementation • 8 Jul 2021 • Menglin Yang, Min Zhou, Marcus Kalander, Zengfeng Huang, Irwin King
To explore these properties of a complex temporal network, we propose a hyperbolic temporal graph network (HTGN) that fully takes advantage of the exponential capacity and hierarchical awareness of hyperbolic geometry.
no code implementations • 6 Jul 2021 • Yong Wen, Marcus Kalander, Chanfei Su, Lujia Pan
E-NKCVS is empirically shown to be highly tolerant to considerable proportions of label noise and has a consistent improvement over state-of-the-art methods.
1 code implementation • 7 May 2021 • Keli Zhang, Marcus Kalander, Min Zhou, Xi Zhang, Junjian Ye
Alarm root cause analysis is a significant component in the day-to-day telecommunication network maintenance, and it is critical for efficient and accurate fault localization and failure recovery.
no code implementations • 17 Sep 2020 • Marcus Kalander, Min Zhou, Chengzhi Zhang, Hanling Yi, Lujia Pan
We conduct extensive experiments on real-world traffic datasets collected from telecommunication networks.