no code implementations • 6 May 2024 • Xiaochen Zheng, Manuel Schürch, Xingyu Chen, Maria Angeliki Komninou, Reto Schüpbach, Ahmed Allam, Jan Bartussek, Michael Krauthammer
The identification of phenotypes within complex diseases or syndromes is a fundamental component of precision medicine, which aims to adapt healthcare to individual patient characteristics.
no code implementations • 4 May 2024 • Zeyu Yang, Zhao Meng, Xiaochen Zheng, Roger Wattenhofer
Large Language Models (LLMs) have revolutionized natural language processing, but their robustness against adversarial attacks remains a critical concern.
no code implementations • 13 Nov 2023 • Xingyu Chen, Xiaochen Zheng, Amina Mollaysa, Manuel Schürch, Ahmed Allam, Michael Krauthammer
Here, we introduce TADA, a Two-stageAggregation process with Dynamic local Attention to harmonize time-wise and feature-wise irregularities in multivariate time series.
no code implementations • 24 Oct 2023 • Ju Wu, Xiaochen Zheng, Marco Madlena, Dimitrios Kyritsis
Based on the review result, a semantic-driven digitalization framework is proposed aiming to improve the digital continuity and cohesion of digital resources and technologies for maintenance activities in the pharmaceutical industry.
1 code implementation • 31 Mar 2023 • Xiaochen Zheng, Xingyu Chen, Manuel Schürch, Amina Mollaysa, Ahmed Allam, Michael Krauthammer
Contrastive learning methods have shown an impressive ability to learn meaningful representations for image or time series classification.
1 code implementation • 29 Oct 2022 • Xiaochen Zheng
Automated animal censuses with aerial imagery are a vital ingredient towards wildlife conservation.
1 code implementation • 12 Oct 2022 • Dehua Zheng, Xiaochen Zheng, Laurence T. Yang, Yuan Gao, Chenlu Zhu, Yiheng Ruan
In addition, our MFFN exploits the dependence and interaction between views and channels.
1 code implementation • 17 Aug 2021 • Xiaochen Zheng, Benjamin Kellenberger, Rui Gong, Irena Hajnsek, Devis Tuia
In detail, we examine a combination of recent contrastive learning methodologies like Momentum Contrast (MoCo) and Cross-Level Instance-Group Discrimination (CLD) to condition our model on the aerial images without the requirement for labels.
no code implementations • 23 Mar 2021 • Jože M. Rožanec, Jinzhi Lu, Jan Rupnik, Maja Škrjanc, Dunja Mladenić, Blaž Fortuna, Xiaochen Zheng, Dimitris Kiritsis
In this paper, we propose a knowledge graph modeling approach to construct actionable cognitive twins for capturing specific knowledge related to demand forecasting and production planning in a manufacturing plant.