Search Results for author: Yu

Found 6 papers, 1 papers with code

N-Grammer: Augmenting Transformers with latent n-grams

2 code implementations13 Jul 2022 Aurko Roy, Rohan Anil, Guangda Lai, Benjamin Lee, Jeffrey Zhao, Shuyuan Zhang, Shibo Wang, Ye Zhang, Shen Wu, Rigel Swavely, Tao, Yu, Phuong Dao, Christopher Fifty, Zhifeng Chen, Yonghui Wu

Transformer models have recently emerged as one of the foundational models in natural language processing, and as a byproduct, there is significant recent interest and investment in scaling these models.

Common Sense Reasoning Coreference Resolution +5

Traders in a Strange Land: Agent-based discrete-event market simulation of the Figgie card game

no code implementations2 Oct 2021 Steven DiSilvio, Yu, Luo, Anthony Ozerov

We develop a strategy, the "bottom-feeder", which estimates value by observing orders sent by other agents, and find that it limits the success of fundamentalists.

End-to-End Learning and Intervention in Games

no code implementations NeurIPS 2020 Jiayang Li, Jing Yu, Yu, Nie, Zhaoran Wang

In this paper, we provide a unified framework for learning and intervention in games.

Data-Free Point Cloud Network for 3D Face Recognition

no code implementations12 Nov 2019 Ziyu, Zhang, Feipeng, Da, Yi, Yu

To ease the inconsistent distribution between model data and real faces, different point sampling methods are used in train and test phase.

3D Object Classification Face Recognition +2

Early Predictions for Medical Crowdfunding: A Deep Learning Approach Using Diverse Inputs

no code implementations9 Nov 2019 Tong Wang, Fujie Jin, Yu, Hu, Yuan Cheng

The prediction model and the interpretable insights can be applied to assist fundraisers with better promoting their fundraising campaigns and can potentially help crowdfunding platforms to provide more timely feedback to all fundraisers.

Clustering Time Series +1

GLAD: Group Anomaly Detection in Social Media Analysis- Extended Abstract

no code implementations7 Oct 2014 QI, Yu, Xinran He, Yan Liu

Existing group anomaly detection approaches rely on the assumption that the groups are known, which can hardly be true in real world social media applications.

Group Anomaly Detection

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