Výsledky vyhledávání - "Liu, Hanyang"

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Unlocking Attributes' Contribution to Successful Camouflage: A Combined Textual and VisualAnalysis Strategy

Autor: Zhang, Hong, Lyu, Yixuan, Yu, Qian, Liu, Hanyang, Ma, Huimin, Yuan, Ding, Yang, Yifan

In the domain of Camouflaged Object Segmentation (COS), despite continuous improvements in segmentation performance, the underlying mechanisms of effective camouflage remain poorly understood, akin to a black box. To address this gap, we present the

Externí odkaz: http://arxiv.org/abs/2408.12086

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Progressive Neural Compression for Adaptive Image Offloading under Timing Constraints

Autor: Wang, Ruiqi, Liu, Hanyang, Qiu, Jiaming, Xu, Moran, Guerin, Roch, Lu, Chenyang

IoT devices are increasingly the source of data for machine learning (ML) applications running on edge servers. Data transmissions from devices to servers are often over local wireless networks whose bandwidth is not just limited but, more importantl

Externí odkaz: http://arxiv.org/abs/2310.05306

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Assisting Clinical Decisions for Scarcely Available Treatment via Disentangled Latent Representation

Autor: Xue, Bing, Said, Ahmed Sameh, Xu, Ziqi, Liu, Hanyang, Shah, Neel, Yang, Hanqing, Payne, Philip, Lu, Chenyang

Extracorporeal membrane oxygenation (ECMO) is an essential life-supporting modality for COVID-19 patients who are refractory to conventional therapies. However, the proper treatment decision has been the subject of significant debate and it remains c

Externí odkaz: http://arxiv.org/abs/2307.03315

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Learning to Rank Normalized Entropy Curves with Differentiable Window Transformation

Autor: Liu, Hanyang, Yang, Shuai, Qi, Feng, Wang, Shuaiwen

Recent automated machine learning systems often use learning curves ranking models to inform decisions about when to stop unpromising trials and identify better model configurations. In this paper, we present a novel learning curve ranking model spec

Externí odkaz: http://arxiv.org/abs/2301.10443

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HiPAL: A Deep Framework for Physician Burnout Prediction Using Activity Logs in Electronic Health Records

Autor: Liu, Hanyang, Lou, Sunny S., Warner, Benjamin C., Harford, Derek R., Kannampallil, Thomas, Lu, Chenyang

Publikováno v: KDD 2022

Burnout is a significant public health concern affecting nearly half of the healthcare workforce. This paper presents the first end-to-end deep learning framework for predicting physician burnout based on electronic health record (EHR) activity logs,

Externí odkaz: http://arxiv.org/abs/2205.11680

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