Zobrazeno 1 - 10
of 18
pro vyhledávání: '"Matthew, Amodio"'
Autor:
Matthew Amodio, Dennis Shung, Daniel B. Burkhardt, Patrick Wong, Michael Simonov, Yu Yamamoto, David van Dijk, Francis Perry Wilson, Akiko Iwasaki, Smita Krishnaswamy
Publikováno v:
Patterns, Vol 2, Iss 7, Pp 100288- (2021)
Summary: Often when biological entities are measured in multiple ways, there are distinct categories of information: some information is easy-to-obtain information (EI) and can be gathered on virtually every subject of interest, while other informati
Externí odkaz:
https://doaj.org/article/e0eead5ed2a3472898bb3458cbcb81bf
Autor:
Yujiao Zhao, Matthew Amodio, Brent Vander Wyk, Bram Gerritsen, Mahesh M Kumar, David van Dijk, Kevin Moon, Xiaomei Wang, Anna Malawista, Monique M Richards, Megan E Cahill, Anita Desai, Jayasree Sivadasan, Manjunatha M Venkataswamy, Vasanthapuram Ravi, Erol Fikrig, Priti Kumar, Steven H Kleinstein, Smita Krishnaswamy, Ruth R Montgomery
Publikováno v:
PLoS Neglected Tropical Diseases, Vol 14, Iss 3, p e0008112 (2020)
The genus Flavivirus contains many mosquito-borne human pathogens of global epidemiological importance such as dengue virus, West Nile virus, and Zika virus, which has recently emerged at epidemic levels. Infections with these viruses result in diver
Externí odkaz:
https://doaj.org/article/b845d9f70c124f9aaf91dca3f01f7623
Autor:
Chen Liu, Matthew Amodio, Liangbo Shen, Feng Gao, Arman Avesta, Sanjay Aneja, Jay Wang, Lucian Del Priore, Smita Krishnaswamy
In this work we introduce CUTS (Contrastive and Unsupervised Training for Segmentation), the first fully unsupervised deep learning framework for medical image segmentation to better utilize the vast majority of imaging data that is not labeled or an
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::995f2fcc8f922ce94821d57b5ba77787
https://doi.org/10.21203/rs.3.rs-2535268/v1
https://doi.org/10.21203/rs.3.rs-2535268/v1
Autor:
Matthew Amodio, Scott E Youlten, Aarthi Venkat, Beatriz P San Juan, Christine Chaffer, Smita Krishnaswamy
Exciting advances in technologies to measure biological systems are currently at the forefront of research. The ability to gather data along an increasing number of omic dimensions has created a need for tools to analyze all of this information toget
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b4f4d0e9e5ef7a35357de5c2c867e3c7
https://doi.org/10.1101/2022.07.04.498732
https://doi.org/10.1101/2022.07.04.498732
Publikováno v:
IEEE Int Workshop Mach Learn Signal Process
MLSP
MLSP
While generative models such as GANs have been successful at mapping from noise to specific distributions of data, or more generally from one distribution of data to another, they cannot isolate the transformation that is occurring and apply it to a
Autor:
Matthew, Amodio, Scott E, Youlten, Aarthi, Venkat, Beatriz P, San Juan, Christine L, Chaffer, Smita, Krishnaswamy
Publikováno v:
Patterns. 3:100577
Exciting advances in technologies to measure biological systems are currently at the forefront of research. The ability to gather data along an increasing number of omic dimensions has created a need for tools to analyze all of this information toget
Autor:
Matthew Amodio, Smita Krishnaswamy
Publikováno v:
IJCNN
Generative Adversarial Networks (GANs) learn generating functions that map a random noise distribution $Z$ to a target data distribution. Usually, little attention is paid to the form of $Z$ , resulting in nearly all models assuming $Z$ follows a con
Autor:
Matthew Amodio, Smita Krishnaswamy
Publikováno v:
IJCNN
Traditional Generative Adversarial Networks (GANs) sample from a continuous stochastic noise distribution, and then treating that sample as a constant, optimize the parameters of a generator network on the average loss over the sample. In other words
Autor:
Smita Krishnaswamy, Matthew Amodio
Publikováno v:
Advances in Intelligent Data Analysis XIX ISBN: 9783030742508
IDA
IDA
Generative adversarial networks (GANs) learn to map samples from a noise distribution to a chosen data distribution. Recent work has demonstrated that GANs are consequently sensitive to, and limited by, the shape of the noise distribution. For exampl
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::83bf26954340759f0c73cc5dd7d6d442
https://doi.org/10.1007/978-3-030-74251-5_3
https://doi.org/10.1007/978-3-030-74251-5_3
Autor:
Daniel B. Burkhardt, Yu Yamamoto, Smita Krishnaswamy, Matthew Amodio, Michael Simonov, Akiko Iwasaki, Dennis Shung, Patrick Wong, David van Dijk, Francis P. Wilson
Publikováno v:
Patterns
Patterns, Vol 2, Iss 7, Pp 100288-(2021)
Patterns, Vol 2, Iss 7, Pp 100288-(2021)
Summary Often when biological entities are measured in multiple ways, there are distinct categories of information: some information is easy-to-obtain information (EI) and can be gathered on virtually every subject of interest, while other informatio