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Dan Margolis
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2020 – today
- 2023
- [i4]Wei Cai, Andrew He, Daniel Margolis:
DeepMartNet - A Martingale Based Deep Neural Network Learning Method for Dirichlet BVP and Eigenvalue Problems of Elliptic PDEs. CoRR abs/2311.09456 (2023) - 2021
- [c7]Kayla Caughlin, Maysam Shahedi, Jonathan Shoag, Christopher Barbieri, Daniel Margolis, Baowei Fei
:
Three-dimensional prostate CT segmentation through fine-tuning of a pre-trained neural network using no reference labeling. Medical Imaging: Image-Guided Procedures 2021 - 2020
- [i3]Ramin Jafari, Pascal Spincemaille, Jinwei Zhang, Thanh D. Nguyen, Martin R. Prince, Xianfu Luo, Junghun Cho, Daniel Margolis, Yi Wang:
Deep Neural Network (DNN) for Water/Fat Separation: Supervised Training, Unsupervised Training, and No Training. CoRR abs/2004.07923 (2020)
2010 – 2019
- 2018
- [i2]Daniel Margolis, Alexander Brotman, Binu Ramakrishnan, Janet Jones, Mark Risher:
SMTP TLS Reporting. RFC 8460: 1-34 (2018) - [i1]Daniel Margolis, Mark Risher, Binu Ramakrishnan, Alexander Brotman, Janet Jones:
SMTP MTA Strict Transport Security (MTA-STS). RFC 8461: 1-29 (2018) - 2017
- [c6]Kurt Thomas, Frank Li, Ali Zand, Jacob Barrett, Juri Ranieri, Luca Invernizzi, Yarik Markov, Oxana Comanescu, Vijay Eranti, Angelika Moscicki, Daniel Margolis, Vern Paxson, Elie Bursztein:
Data Breaches, Phishing, or Malware?: Understanding the Risks of Stolen Credentials. CCS 2017: 1421-1434 - 2014
- [c5]Elie Bursztein, Borbala Benko, Daniel Margolis, Tadek Pietraszek, Andy Archer, Allan Aquino, Andreas Pitsillidis, Stefan Savage:
Handcrafted Fraud and Extortion: Manual Account Hijacking in the Wild. Internet Measurement Conference 2014: 347-358 - 2013
- [c4]Aaron S. Campbell, Walker H. Land Jr., Dan Margolis, Ravi Mathur
, J. David Schaffer:
Investigating the GRNN Oracle as a Method for Combining Multiple Predictive Models of Colon Cancer Recurrence from Gene Microarrays. Complex Adaptive Systems 2013: 374-378 - 2012
- [c3]Daniel Margolis, Alberto Santamaría-Pang, Jens Rittscher
:
Tissue segmentation and classification using graph-based unsupervised clustering. ISBI 2012: 162-165 - 2011
- [j4]Walker H. Land Jr., Xingye Qiao, Daniel Margolis, William S. Ford, Christopher T. Paquette, Joseph F. Perez-Rogers, Jeffrey A. Borgia, Jack Y. Yang, Youping Deng:
Kernelized partial least squares for feature reduction and classification of gene microarray data. BMC Syst. Biol. 5(S-3): S13 (2011) - [c2]Walker H. Land Jr., Xingye Qiao, Dan Margolis, Ronald Gottlieb:
A new tool for survival analysis: evolutionary programming/evolutionary strategies (EP/ES) support vector regression hybrid using both censored / non-censored (event) data. Complex Adaptive Systems 2011: 267-272 - [c1]Dan Margolis, Walker H. Land Jr., Ronald Gottlieb, Xingye Qiao:
A complex adaptive system using statistical learning theory as an inline preprocess for clinical survival analysis. Complex Adaptive Systems 2011: 279-284 - 2010
- [j3]Walker H. Land Jr., Dan Margolis, Ronald Gottlieb, Jack Y. Yang, Elizabeth A. Krupinski:
Improving CT prediction of treatment response in patients with metastatic colorectal carcinoma using statistical learning. Int. J. Comput. Biol. Drug Des. 3(1): 15-18 (2010) - [j2]Walker H. Land Jr., Dan Margolis, Maria Kallergi, John J. Heine:
A kernel approach for ensemble decision combinations with two-view mammography applications. Int. J. Funct. Informatics Pers. Medicine 3(2): 157-182 (2010)
2000 – 2009
- 2009
- [j1]Mithun N. Prasad, Matthew S. Brown, Chiayi Ni, Daniel Margolis, Michael Douek, Sundaresan Raman, David Lu, Jonathan G. Goldin
, Simon K. Warfield
:
Reproducibility of Laplacian Wall Thickness Measurements of the Gallbladder with Varying CT Slice Thickness. J. Signal Process. Syst. 55(1-3): 67-75 (2009)
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