Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Yangsibo Huang"'
Autor:
Rachel Cummings, Damien Desfontaines, David Evans, Roxana Geambasu, Yangsibo Huang, Matthew Jagielski, Peter Kairouz, Gautam Kamath, Sewoong Oh, Olga Ohrimenko, Nicolas Papernot, Ryan Rogers, Milan Shen, Shuang Song, Weijie Su, Andreas Terzis, Abhradeep Thakurta, Sergei Vassilvitskii, Yu-Xiang Wang, Li Xiong, Sergey Yekhanin, Da Yu, Huanyu Zhang, Wanrong Zhang
Publikováno v:
Harvard Data Science Review, Vol 6, Iss 1 (2024)
Externí odkaz:
https://doaj.org/article/b2e67d0dbbbb45968b681bb675b120fc
Publikováno v:
Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 ISBN: 9783030872397
MICCAI (5)
MICCAI (5)
Data auditing is a process to verify whether certain data have been removed from a trained model. A recently proposed method [10] uses Kolmogorov-Smirnov (KS) distance for such data auditing. However, it fails under certain practical conditions. In t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6f1fed201038e765c458512d6d4407b4
https://doi.org/10.1007/978-3-030-87240-3_76
https://doi.org/10.1007/978-3-030-87240-3_76
Publikováno v:
IEEE BigData
Missing value imputation is a challenging and well-researched topic in data mining. In this paper, we propose IFGAN, a missing value imputation algorithm based on Feature-specific Generative Adversarial Networks (GAN). Our idea is intuitive yet effec
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ae9b37dcdd90358f72f5bafe8f8fd3c6
http://arxiv.org/abs/2012.12581
http://arxiv.org/abs/2012.12581
Publikováno v:
EMNLP (Findings)
An unsolved challenge in distributed or federated learning is to effectively mitigate privacy risks without slowing down training or reducing accuracy. In this paper, we propose TextHide aiming at addressing this challenge for natural language unders
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::381d728e8a6ab7fc20dd909c632d3d66
http://arxiv.org/abs/2010.06053
http://arxiv.org/abs/2010.06053
Publikováno v:
IEEE Transactions on Medical Imaging. :1-1
Dataset auditing for machine learning (ML) models is a method to evaluate if a given dataset is used in training a model. In a Federated Learning setting where multiple institutions collaboratively train a model with their decentralized private datas
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6cb6afb46e465ae1393f99b5cc80b7d9
https://doi.org/10.1109/tmi.2022.3220706
https://doi.org/10.1109/tmi.2022.3220706
Publikováno v:
IEEE transactions on medical imaging. 38(8)
Segmentation of pancreas is important for medical image analysis, yet it faces great challenges of class imbalance, background distractions and non-rigid geometrical features. To address these difficulties, we introduce a Deep Q Network(DQN) driven a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e17c60de2ff0f88a5973667176f5a47f
https://pubmed.ncbi.nlm.nih.gov/30998461
https://pubmed.ncbi.nlm.nih.gov/30998461
Publikováno v:
Artificial Intelligence in Radiation Therapy ISBN: 9783030324858
AIRT@MICCAI
AIRT@MICCAI
The next great leap toward improving treatment of cancer with radiation will require the combined use of online adaptive and magnetic resonance guided radiation therapy techniques with automatic X-ray beam orientation selection. Unfortunately, by uni
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9ffac272264417b4743f0511680c397a
https://doi.org/10.1007/978-3-030-32486-5_17
https://doi.org/10.1007/978-3-030-32486-5_17
Publikováno v:
Phys Med Biol
Emerging magnetic resonance (MR) guided radiotherapy affords significantly improved anatomy visualization and, subsequently, more effective personalized treatment. The new therapy paradigm imposes significant demands on radiation dose calculation qua
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7564e3a8c1bc9696c746a80c571ca9eb
https://doi.org/10.1088/1361-6560/abca01
https://doi.org/10.1088/1361-6560/abca01