Zobrazeno 1 - 10
of 31
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.
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).
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
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
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
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
Publikováno v:
ACM Transactions on Knowledge Discovery from Data. 13:1-2
Publikováno v:
Statistical Analysis and Data Mining: The ASA Data Science Journal. 8:147-161
The popularity and influence of reviews, make sites like Yelp ideal targets for malicious behaviors. We present Marco, a novel system that exploits the unique combination of social, spatial and temporal signals gleaned from Yelp, to detect venues who