Zobrazeno 1 - 10
of 180
pro vyhledávání: '"Raetsch, P."'
Autor:
Burger, Manuel, Sergeev, Fedor, Londschien, Malte, Chopard, Daphné, Yèche, Hugo, Gerdes, Eike, Leshetkina, Polina, Morgenroth, Alexander, Babür, Zeynep, Bogojeska, Jasmina, Faltys, Martin, Kuznetsova, Rita, Rätsch, Gunnar
Notable progress has been made in generalist medical large language models across various healthcare areas. However, large-scale modeling of in-hospital time series data - such as vital signs, lab results, and treatments in critical care - remains un
Externí odkaz:
http://arxiv.org/abs/2411.16346
A key motivation in the development of distributed Model Predictive Control (MPC) is to widen the computational bottleneck of centralized MPC for large-scale systems. Parallelizing computations among individual subsystems, distributed MPC has the pro
Externí odkaz:
http://arxiv.org/abs/2411.05627
Autor:
Chen, Boqi, Zhu, Yuanzhi, Ao, Yunke, Caprara, Sebastiano, Sutter, Reto, Rätsch, Gunnar, Konukoglu, Ender, Susmelj, Anna
Single-source domain generalization (SDG) aims to learn a model from a single source domain that can generalize well on unseen target domains. This is an important task in computer vision, particularly relevant to medical imaging where domain shifts
Externí odkaz:
http://arxiv.org/abs/2411.05223
Aligning Large Language Models (LLMs) to human preferences in content, style, and presentation is challenging, in part because preferences are varied, context-dependent, and sometimes inherently ambiguous. While successful, Reinforcement Learning fro
Externí odkaz:
http://arxiv.org/abs/2410.20187
Knowing which features of a multivariate time series to measure and when is a key task in medicine, wearables, and robotics. Better acquisition policies can reduce costs while maintaining or even improving the performance of downstream predictors. In
Externí odkaz:
http://arxiv.org/abs/2407.13429
Applying reinforcement learning (RL) to real-world problems is often made challenging by the inability to interact with the environment and the difficulty of designing reward functions. Offline RL addresses the first challenge by considering access t
Externí odkaz:
http://arxiv.org/abs/2406.18450
Electronic Health Record (EHR) datasets from Intensive Care Units (ICU) contain a diverse set of data modalities. While prior works have successfully leveraged multiple modalities in supervised settings, we apply advanced self-supervised multi-modal
Externí odkaz:
http://arxiv.org/abs/2403.18316
This study advances Early Event Prediction (EEP) in healthcare through Dynamic Survival Analysis (DSA), offering a novel approach by integrating risk localization into alarm policies to enhance clinical event metrics. By adapting and evaluating DSA m
Externí odkaz:
http://arxiv.org/abs/2403.12818
The rapid expansion of genomic sequence data calls for new methods to achieve robust sequence representations. Existing techniques often neglect intricate structural details, emphasizing mainly contextual information. To address this, we developed k-
Externí odkaz:
http://arxiv.org/abs/2312.03865
Publikováno v:
PMLR 225:268-291, 2023
Recent advances in deep learning architectures for sequence modeling have not fully transferred to tasks handling time-series from electronic health records. In particular, in problems related to the Intensive Care Unit (ICU), the state-of-the-art re
Externí odkaz:
http://arxiv.org/abs/2311.08902