Zobrazeno 1 - 10
of 53
pro vyhledávání: '"Hung, Jane"'
Autor:
Edwards, Ashley D., Sahni, Himanshu, Liu, Rosanne, Hung, Jane, Jain, Ankit, Wang, Rui, Ecoffet, Adrien, Miconi, Thomas, Isbell, Charles, Yosinski, Jason
In this paper, we introduce a novel form of value function, $Q(s, s')$, that expresses the utility of transitioning from a state $s$ to a neighboring state $s'$ and then acting optimally thereafter. In order to derive an optimal policy, we develop a
Externí odkaz:
http://arxiv.org/abs/2002.09505
Autor:
Dathathri, Sumanth, Madotto, Andrea, Lan, Janice, Hung, Jane, Frank, Eric, Molino, Piero, Yosinski, Jason, Liu, Rosanne
Large transformer-based language models (LMs) trained on huge text corpora have shown unparalleled generation capabilities. However, controlling attributes of the generated language (e.g. switching topic or sentiment) is difficult without modifying t
Externí odkaz:
http://arxiv.org/abs/1912.02164
Autor:
Hung, Jane Yen.
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Chemical Engineering, 2018
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 107-113).
Computers are better than ever at extracting inf
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 107-113).
Computers are better than ever at extracting inf
Externí odkaz:
https://hdl.handle.net/1721.1/121894
We introduce the Metropolis-Hastings generative adversarial network (MH-GAN), which combines aspects of Markov chain Monte Carlo and GANs. The MH-GAN draws samples from the distribution implicitly defined by a GAN's discriminator-generator pair, as o
Externí odkaz:
http://arxiv.org/abs/1811.11357
Autor:
Hung, Jane, Ravel, Deepali, Lopes, Stefanie C. P., Rangel, Gabriel, Nery, Odailton Amaral, Malleret, Benoit, Nosten, Francois, Lacerda, Marcus V. G., Ferreira, Marcelo U., Rénia, Laurent, Duraisingh, Manoj T., Costa, Fabio T. M., Marti, Matthias, Carpenter, Anne E.
Deep learning based models have had great success in object detection, but the state of the art models have not yet been widely applied to biological image data. We apply for the first time an object detection model previously used on natural images
Externí odkaz:
http://arxiv.org/abs/1804.09548
Autor:
Hung, Jane.
Publikováno v:
Click to view the E-thesis via HKUTO.
Thesis (M.A.)--University of Hong Kong, 1998.
Includes bibliographical references (leaves 65-68). Also available in print.
Includes bibliographical references (leaves 65-68). Also available in print.
Externí odkaz:
http://sunzi.lib.hku.hk/hkuto/record/B31963390
Autor:
Hung, Jane1,2 (AUTHOR), Goodman, Allen2 (AUTHOR), Ravel, Deepali3 (AUTHOR), Lopes, Stefanie C. P.4,5 (AUTHOR), Rangel, Gabriel W.3 (AUTHOR), Nery, Odailton A.6 (AUTHOR), Malleret, Benoit7,8 (AUTHOR), Nosten, Francois9,10 (AUTHOR), Lacerda, Marcus V. G.4,5 (AUTHOR), Ferreira, Marcelo U.6 (AUTHOR), Rénia, Laurent8 (AUTHOR), Duraisingh, Manoj T.3 (AUTHOR), Costa, Fabio T. M.11 (AUTHOR), Marti, Matthias3,12 (AUTHOR), Carpenter, Anne E.2 (AUTHOR) anne@broadinstitute.org
Publikováno v:
BMC Bioinformatics. 7/11/2020, Vol. 21 Issue 1, p1-7. 7p. 1 Graph.
Autor:
Bellanger, Clément, Hung, Jane, Juniarta, Nyoman, Leroux, Vincent, Maigret, Bernard, Napoli, Amedeo
Publikováno v:
[Technical Report] Inria Nancy-Grand Est. 2020
Many bacteria are acquiring more resistance to usual treatments worldwide, to the point that the possible advent of pathogens resistant to the entire current arsenal is a true concern. Therefore, there is an urgent need for finding new effective anti
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::0e6861295cff1e2577ca611737567f42
https://hal.inria.fr/hal-02615395/file/document.pdf
https://hal.inria.fr/hal-02615395/file/document.pdf
Autor:
Hung, Jane, Goodman, Allen, Ravel, Deepali, Lopes, Stefanie C. P., Rangel, Gabriel W., Odailton A. Nery, Benoit Malleret, Francois Nosten, Lacerda, Marcus V. G., Ferreira, Marcelo U., Rénia, Laurent, Duraisingh, Manoj T., Costa, Fabio T. M., Marti, Matthias, Carpenter, Anne E.
Additional file 1: Figure S1. Comparison of mean average precision curves for different IoU thresholds for Keras R-CNN versus CellProfiler on the nuclei and malaria datasets. For nuclei, the mean average precision is 0.99 at a threshold of 0.5 for Ke
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2dee29df043be2481487ee9622464aff
Autor:
Yang, Samuel J, Berndl, Marc, Michael Ando, D., Narayanaswamy, Arunachalam, Christiansen, Eric, Hoyer, Stephan, Roat, Chris, Rueden, Curtis T, Shankar, Asim, Finkbeiner, Steven, Nelson, Philip, Yang, Samuel J., Rueden, Curtis T., Barch, Mariya, Hung, Jane Yen
Publikováno v:
BioMed Central
BMC bioinformatics, vol 19, iss 1
BMC Bioinformatics, Vol 19, Iss 1, Pp 1-9 (2018)
BMC Bioinformatics
BMC bioinformatics, vol 19, iss 1
BMC Bioinformatics, Vol 19, Iss 1, Pp 1-9 (2018)
BMC Bioinformatics
Background Large image datasets acquired on automated microscopes typically have some fraction of low quality, out-of-focus images, despite the use of hardware autofocus systems. Identification of these images using automated image analysis with high
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0e46625dca1b75622caa2c34d059d37d
http://hdl.handle.net/1721.1/114668
http://hdl.handle.net/1721.1/114668