Zobrazeno 1 - 10
of 30
pro vyhledávání: '"Paul Pu Liang"'
Autor:
Paul Pu Liang, Yiwei Lyu, Gunjan Chhablani, Nihal Jain, Zihao Deng, Xingbo Wang, Louis-Philippe Morency, Ruslan Salakhutdinov
Publikováno v:
Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f3d605587c935ec1d0b1296207c8d271
https://doi.org/10.1145/3544549.3585604
https://doi.org/10.1145/3544549.3585604
Creating artificial social intelligence - algorithms that can understand the nuances of multi-person interactions - is an exciting and emerging challenge in processing facial expressions and gestures from multimodal videos. Recent multimodal methods
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9c23d1acfce6a3d41c64a93fc4a7fd4f
http://arxiv.org/abs/2208.01036
http://arxiv.org/abs/2208.01036
Publikováno v:
Information Fusion. 64:188-193
In the current state of the field of machine learning, often, real-world phenomena are learned through studies of isolated modalities; such as modeling language exclusively from verbal modality, which is a common theme in natural language processing.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::45498bfe19e2b0a3c504bbac42f4d53b
https://doi.org/10.1016/j.inffus.2020.06.001
https://doi.org/10.1016/j.inffus.2020.06.001
The ability for a human to understand an Artificial Intelligence (AI) model's decision-making process is critical in enabling stakeholders to visualize model behavior, perform model debugging, promote trust in AI models, and assist in collaborative h
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e94c060a99f99c7143bc09d0c7ef010e
http://arxiv.org/abs/2203.02013
http://arxiv.org/abs/2203.02013
Autor:
Paul Pu Liang1 PLIANG@CS.CMU.EDU, Yiwei Lyu1 YLYU1@CS.CMU.EDU, Xiang Fan1 XIANGFAN@CS.CMU.EDU, Agarwal, Arav1 ARAVA@CS.CMU.EDU, Yun Cheng1 YUNCHENG@CS.CMU.EDU, Morency, Louis-Philippe1 MORENCY@CS.CMU.EDU, Salakhutdinov, Ruslan1 RSALAKHU@CS.CMU.EDU
Publikováno v:
Journal of Machine Learning Research. 2023, Vol. 24, p1-7. 7p.
Publikováno v:
IEEE Transactions on Vehicular Technology. 69:564-577
Recently, classifying the modulation schemes of signals using deep neural network has received much attention. In this paper, we introduce a general model of deep neural network (DNN)-based modulation classifiers for single-input single-output (SISO)
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::63ba55c2e232c4b16b49f65c212529a5
https://doi.org/10.1109/tvt.2019.2951594
https://doi.org/10.1109/tvt.2019.2951594
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783031198359
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f370252960ffccbd7cb9879a56cfaf51
https://doi.org/10.1007/978-3-031-19836-6_17
https://doi.org/10.1007/978-3-031-19836-6_17
Publikováno v:
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Tutorial Abstracts.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::50aa4f2e0d962ab6cef83e0c83ef7392
https://doi.org/10.18653/v1/2022.naacl-tutorials.5
https://doi.org/10.18653/v1/2022.naacl-tutorials.5
Publikováno v:
ACM Multimedia
How can we generalize to a new prediction task at test time when it also uses a new modality as input? More importantly, how can we do this with as little annotated data as possible? This problem of cross-modal generalization is a new research milest
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5725e476afb272b470579e3695f1a2a8
https://doi.org/10.1145/3474085.3475247
https://doi.org/10.1145/3474085.3475247
Autor:
Liangqiong Qu, Yuyin Zhou, Paul Pu Liang, Yingda Xia, Feifei Wang, Ehsan Adeli, Li Fei-Fei, Daniel Rubin
Publikováno v:
Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit
Federated learning is an emerging research paradigm enabling collaborative training of machine learning models among different organizations while keeping data private at each institution. Despite recent progress, there remain fundamental challenges
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c62b477f6875e709902294617a57f82b
http://arxiv.org/abs/2106.06047
http://arxiv.org/abs/2106.06047