Zobrazeno 1 - 10
of 995
pro vyhledávání: '"Tran Huong"'
Autor:
Calandrelli Rosalinda, Panfili Marco, Onofrj Valeria, Tran Huong Elena, Piludu Francesca, Guglielmi Valeria, Colosimo Cesare, Pilato Fabio
Publikováno v:
Translational Neuroscience, Vol 13, Iss 1, Pp 335-348 (2022)
We evaluated the accuracy of the quantitative and semiquantitative analysis in detecting regional atrophy patterns and differentiating mild cognitive impairment patients who remain stable (aMCI-S) from patients who develop Alzheimer’s disease (aMCI
Externí odkaz:
https://doaj.org/article/28a8e39173ef4abbacc90b48b0e7192f
Publikováno v:
Health Research Policy and Systems, Vol 18, Iss 1, Pp 1-11 (2020)
Abstract Background Translating research evidence into practice is challenging and, to date, there are relatively few public health interventions that have been effectively and cost-effectively implemented and delivered at scale. Theories, models and
Externí odkaz:
https://doaj.org/article/1930ebb7e1d44a59aac91cab4d06b8c6
Autor:
Le Thai Van Thanh, Le Vi Anh, Tran Huong Giang, Ta Quoc Hung, Van The Trung, Nguyen Lam Vuong
Publikováno v:
Dermatology Reports (2022)
Background: Acne vulgaris is the most common inflammatory disease of the skin. IL-1b has been found in acne lesions and is a promising target for therapy, but the evidence is limited. Therefore, this study was conducted to investigate the immunohisto
Externí odkaz:
https://doaj.org/article/141649d1cfd54fb28f25ac790478c403
Autor:
Ta, Lien T. P., Tran, Huong T. T.
Most Mahonian statistics can be expressed as a linear combination of vincular patterns. This is not only true with statistics on the permutation set, but it can also be applied for statistics on the permutation with repetition set. By following the m
Externí odkaz:
http://arxiv.org/abs/2405.10983
Autor:
Chebotar, Yevgen, Vuong, Quan, Irpan, Alex, Hausman, Karol, Xia, Fei, Lu, Yao, Kumar, Aviral, Yu, Tianhe, Herzog, Alexander, Pertsch, Karl, Gopalakrishnan, Keerthana, Ibarz, Julian, Nachum, Ofir, Sontakke, Sumedh, Salazar, Grecia, Tran, Huong T, Peralta, Jodilyn, Tan, Clayton, Manjunath, Deeksha, Singht, Jaspiar, Zitkovich, Brianna, Jackson, Tomas, Rao, Kanishka, Finn, Chelsea, Levine, Sergey
In this work, we present a scalable reinforcement learning method for training multi-task policies from large offline datasets that can leverage both human demonstrations and autonomously collected data. Our method uses a Transformer to provide a sca
Externí odkaz:
http://arxiv.org/abs/2309.10150
Autor:
Brohan, Anthony, Brown, Noah, Carbajal, Justice, Chebotar, Yevgen, Chen, Xi, Choromanski, Krzysztof, Ding, Tianli, Driess, Danny, Dubey, Avinava, Finn, Chelsea, Florence, Pete, Fu, Chuyuan, Arenas, Montse Gonzalez, Gopalakrishnan, Keerthana, Han, Kehang, Hausman, Karol, Herzog, Alexander, Hsu, Jasmine, Ichter, Brian, Irpan, Alex, Joshi, Nikhil, Julian, Ryan, Kalashnikov, Dmitry, Kuang, Yuheng, Leal, Isabel, Lee, Lisa, Lee, Tsang-Wei Edward, Levine, Sergey, Lu, Yao, Michalewski, Henryk, Mordatch, Igor, Pertsch, Karl, Rao, Kanishka, Reymann, Krista, Ryoo, Michael, Salazar, Grecia, Sanketi, Pannag, Sermanet, Pierre, Singh, Jaspiar, Singh, Anikait, Soricut, Radu, Tran, Huong, Vanhoucke, Vincent, Vuong, Quan, Wahid, Ayzaan, Welker, Stefan, Wohlhart, Paul, Wu, Jialin, Xia, Fei, Xiao, Ted, Xu, Peng, Xu, Sichun, Yu, Tianhe, Zitkovich, Brianna
We study how vision-language models trained on Internet-scale data can be incorporated directly into end-to-end robotic control to boost generalization and enable emergent semantic reasoning. Our goal is to enable a single end-to-end trained model to
Externí odkaz:
http://arxiv.org/abs/2307.15818
Publikováno v:
Journal of Management Development, 2024, Vol. 43, Issue 4, pp. 619-641.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/JMD-05-2023-0154
Autor:
Brohan, Anthony, Brown, Noah, Carbajal, Justice, Chebotar, Yevgen, Dabis, Joseph, Finn, Chelsea, Gopalakrishnan, Keerthana, Hausman, Karol, Herzog, Alex, Hsu, Jasmine, Ibarz, Julian, Ichter, Brian, Irpan, Alex, Jackson, Tomas, Jesmonth, Sally, Joshi, Nikhil J, Julian, Ryan, Kalashnikov, Dmitry, Kuang, Yuheng, Leal, Isabel, Lee, Kuang-Huei, Levine, Sergey, Lu, Yao, Malla, Utsav, Manjunath, Deeksha, Mordatch, Igor, Nachum, Ofir, Parada, Carolina, Peralta, Jodilyn, Perez, Emily, Pertsch, Karl, Quiambao, Jornell, Rao, Kanishka, Ryoo, Michael, Salazar, Grecia, Sanketi, Pannag, Sayed, Kevin, Singh, Jaspiar, Sontakke, Sumedh, Stone, Austin, Tan, Clayton, Tran, Huong, Vanhoucke, Vincent, Vega, Steve, Vuong, Quan, Xia, Fei, Xiao, Ted, Xu, Peng, Xu, Sichun, Yu, Tianhe, Zitkovich, Brianna
By transferring knowledge from large, diverse, task-agnostic datasets, modern machine learning models can solve specific downstream tasks either zero-shot or with small task-specific datasets to a high level of performance. While this capability has
Externí odkaz:
http://arxiv.org/abs/2212.06817
Autor:
Do, Mai Tuyet, Pham, Anh Tuan, Nguyen, Linh Thi Thuy, Nguyen, Tam Thanh, Le, Ngoc Minh, Tran, Huong Thi Thanh
Publikováno v:
In Journal of Behavioral and Cognitive Therapy November 2024 34(3)
Publikováno v:
In Journal of Environmental Sciences November 2024 145:139-151