Search Results for author: Han Yuan

Found 7 papers, 3 papers with code

An empirical study of the effect of background data size on the stability of SHapley Additive exPlanations (SHAP) for deep learning models

no code implementations24 Apr 2022 Han Yuan, Mingxuan Liu, Lican Kang, Chenkui Miao, Ying Wu

In our empirical study on the MIMIC-III dataset, we show that the two core explanations - SHAP values and variable rankings fluctuate when using different background datasets acquired from random sampling, indicating that users cannot unquestioningly trust the one-shot interpretation from SHAP.

You Ought to Look Around: Precise, Large Span Action Detection

no code implementations 25th International Conference on Pattern Recognition (ICPR) 2021 Ge Pan, Han Zhang, Fan Yu, Yonghong Song, Yuanlin Zhang, Han Yuan

In this paper, we propose a method called YOLA (You Ought to Look Around) which includes three parts: 1) a robust backbone SPN-I3D for extracting spatio-temporal features.

Action Detection Action Localization

AutoScore-Imbalance: An interpretable machine learning tool for development of clinical scores with rare events data

1 code implementation13 Jul 2021 Han Yuan, Feng Xie, Marcus Eng Hock Ong, Yilin Ning, Marcel Lucas Chee, Seyed Ehsan Saffari, Hairil Rizal Abdullah, Benjamin Alan Goldstein, Bibhas Chakraborty, Nan Liu

All scoring models were evaluated on the basis of their area under the curve (AUC) in the receiver operating characteristic analysis and balanced accuracy (i. e., mean value of sensitivity and specificity).

Decision Making Interpretable Machine Learning

AutoScore-Survival: Developing interpretable machine learning-based time-to-event scores with right-censored survival data

1 code implementation13 Jun 2021 Feng Xie, Yilin Ning, Han Yuan, Benjamin Alan Goldstein, Marcus Eng Hock Ong, Nan Liu, Bibhas Chakraborty

We illustrated our method in a real-life study of 90-day mortality of patients in intensive care units and compared its performance with survival models (i. e., Cox) and the random survival forest.

Interpretable Machine Learning Variable Selection

Elastic Deep Learning in Multi-Tenant GPU Clusters

no code implementations IEEE Transactions on Parallel and Distributed Systems 2021 Yidi Wu, Kaihao Ma, Xiao Yan, Zhi Liu, Zhenkun Cai, Yuzhen Huang, James Cheng, Han Yuan, Fan Yu

We study how to support elasticity, that is, the ability to dynamically adjust the parallelism (i. e., the number of GPUs), for deep neural network (DNN) training in a GPU cluster.

A Framework for Recommending Accurate and Diverse ItemsUsing Bayesian Graph Convolutional Neural Networks

1 code implementation Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining 2020 Jianing Sun, Wei Guo, Dengcheng Zhang, Yingxue Zhang, Florence Regol, Yaochen Hu, Huifeng Guo, Ruiming Tang, Han Yuan, Xiuqiang He, Mark Coates

Because of the multitude of relationships existing in recommender systems, Graph Neural Networks (GNNs) based approaches have been proposed to better characterize the various relationships between a user and items while modeling a user's preferences.

Recommendation Systems

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