no code implementations • 16 Dec 2022 • Giorgian Borca-Tasciuc, Xingzhi Guo, Stanley Bak, Steven Skiena
Machine learning models are increasingly deployed for critical decision-making tasks, making it important to verify that they do not contain gender or racial biases picked up from training data.
no code implementations • 2 Nov 2022 • Xingzhi Guo, Steven Skiena
Word and graph embeddings are widely used in deep learning applications.
no code implementations • 29 Sep 2021 • Xingzhi Guo, Baojian Zhou, Haochen Chen, Sergiy Verstyuk, Steven Skiena
The power of embedding representations is a curious phenomenon.
no code implementations • 29 Sep 2020 • Kellen Gillespie, Ioannis C. Konstantakopoulos, Xingzhi Guo, Vishal Thanvantri Vasudevan, Abhinav Sethy
User interactions with personal assistants like Alexa, Google Home and Siri are typically initiated by a wake term or wakeword.
no code implementations • 28 Jul 2020 • Zhuoyi Lin, Lei Feng, Xingzhi Guo, Yu Zhang, Rui Yin, Chee Keong Kwoh, Chi Xu
In this paper, we propose a novel representation learning-based model called COMET (COnvolutional diMEnsion inTeraction), which simultaneously models the high-order interaction patterns among historical interactions and embedding dimensions.