Zobrazeno 1 - 10
of 33
pro vyhledávání: '"Polo Chau"'
Hear No Evil: Towards Adversarial Robustness of Automatic Speech Recognition via Multi-Task Learning
Autor:
Nilaksh Das, Polo Chau
Publikováno v:
Interspeech 2022.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::20add59b2a9fac6e2653ccf164fc5377
https://doi.org/10.21437/interspeech.2022-11361
https://doi.org/10.21437/interspeech.2022-11361
Autor:
Sivapriya Vellaichamy, Matthew Hull, Zijie J. Wang, Nilaksh Das, ShengYun Peng, Haekyu Park, Duen Horng Polo Chau
Publikováno v:
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3ba1db248d7f6861b4e2384265565433
https://doi.org/10.1109/cvpr52688.2022.02082
https://doi.org/10.1109/cvpr52688.2022.02082
Autor:
Zijie J. Wang, Nilaksh Das, Haekyu Park, Omar Shaikh, Minsuk Kahng, Fred Hohman, Robert Turko, Duen Horng Polo Chau
Publikováno v:
IEEE transactions on visualization and computer graphics. 27(2)
Deep learning's great success motivates many practitioners and students to learn about this exciting technology. However, it is often challenging for beginners to take their first step due to the complexity of understanding and applying deep learning
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c022c43a4547d75744c5fd5e3f35b391
https://pubmed.ncbi.nlm.nih.gov/33048723
https://pubmed.ncbi.nlm.nih.gov/33048723
Autor:
Duen Horng Polo Chau, Minsuk Kahng
Publikováno v:
IEEE VIS (Short Papers)
While a rapidly growing number of people want to learn artificial intelligence (AI) and deep learning, the increasing complexity of such models poses significant learning barriers. Recently, interactive visualizations, such as TensorFlow Playground a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::73eedf6957ef60b6e515e6dea36afd6b
https://doi.org/10.1109/vis47514.2020.00060
https://doi.org/10.1109/vis47514.2020.00060
Publikováno v:
EMNLP (Findings)
Interpreting how persuasive language influences audiences has implications across many domains like advertising, argumentation, and propaganda. Persuasion relies on more than a message's content. Arranging the order of the message itself (i.e., order
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fe289c46e0b7fa4cde1a7a3373a1a939
https://doi.org/10.18653/v1/2020.findings-emnlp.116
https://doi.org/10.18653/v1/2020.findings-emnlp.116
Autor:
Park, Haekyu, Das, Nilaksh, Duggal, Rahul, Wright, Austin P., Shaikh, Omar, Hohman, Fred, Polo Chau, Duen Horng
Publikováno v:
IEEE Transactions on Visualization & Computer Graphics; Jan2022, Vol. 28 Issue 1, p813-823, 11p
Deep learning is increasingly used in decision-making tasks. However, understanding how neural networks produce final predictions remains a fundamental challenge. Existing work on interpreting neural network predictions for images often focuses on ex
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d57e54bd03259fb2ef7de2577c855e87
http://arxiv.org/abs/1904.02323
http://arxiv.org/abs/1904.02323
Publikováno v:
IEEE BigData
Local graph partitioning is a key graph mining tool that allows researchers to identify small groups of interrelated nodes (e.g., people) and their connective edges (e.g., interactions). As local graph partitioning focuses primarily on the graph stru
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::789a0b85842f05863e03451205b12c02
https://doi.org/10.1109/bigdata.2018.8622227
https://doi.org/10.1109/bigdata.2018.8622227
Recent success in deep learning has generated immense interest among practitioners and students, inspiring many to learn about this new technology. While visual and interactive approaches have been successfully developed to help people more easily le
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8edc55906bef9c6d0a06d75f9b8822b5
http://arxiv.org/abs/1809.01587
http://arxiv.org/abs/1809.01587