Search Results for author: Nan Liu

Found 10 papers, 4 papers with code

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

Fill-in-the-blank as a Challenging Video Understanding Evaluation Framework

no code implementations9 Apr 2021 Santiago Castro, Ruoyao Wang, Pingxuan Huang, Ian Stewart, Nan Liu, Jonathan Stroud, Rada Mihalcea

Work to date on language-informed video understanding has primarily addressed two tasks: (1) video question answering using multiple-choice questions, where models perform relatively well because they exploit the fact that candidate answers are readily available; and (2) video captioning, which relies on an open-ended evaluation framework that is often inaccurate because system answers may be perceived as incorrect if they differ in form from the ground truth.

Language Modelling Question Answering +3

Fast Estimation for Privacy and Utility in Differentially Private Machine Learning

no code implementations1 Jan 2021 Yuzhe Li, Yong liu, Weipinng Wang, Bo Li, Nan Liu

In this paper, we deduce the influence of $\epsilon$ on utility private learning models through strict mathematical derivation, and propose a novel approximate approach for estimating the utility of any $\epsilon$ value.

A Blockchain-based Decentralized Federated Learning Framework with Committee Consensus

no code implementations2 Apr 2020 Yuzheng Li, Chuan Chen, Nan Liu, Huawei Huang, Zibin Zheng, Qiang Yan

To address these security issues, we proposed a decentralized federated learning framework based on blockchain, i. e., a Blockchain-based Federated Learning framework with Committee consensus (BFLC).

Federated Learning

FASPell: A Fast, Adaptable, Simple, Powerful Chinese Spell Checker Based On DAE-Decoder Paradigm

1 code implementation WS 2019 Yuzhong Hong, Xianguo Yu, Neng He, Nan Liu, Junhui Liu

We propose a Chinese spell checker {--} FASPell based on a new paradigm which consists of a denoising autoencoder (DAE) and a decoder.

Chinese Spell Checking Denoising +1

Time-sync Video Tag Extraction Using Semantic Association Graph

no code implementations3 May 2019 Wenmian Yang, Kun Wang, Na Ruan, Wenyuan Gao, Weijia Jia, Wei Zhao, Nan Liu, Yunyong Zhang

Finally, we gain the weight of each word by combining Semantic Weight (SW) and Inverse Document Frequency (IDF).

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