Search Results for author: Xingyu Fu

Found 12 papers, 8 papers with code

There’s a Time and Place for Reasoning Beyond the Image

1 code implementation ACL 2022 Xingyu Fu, Ben Zhou, Ishaan Chandratreya, Carl Vondrick, Dan Roth

Images are often more significant than only the pixels to human eyes, as we can infer, associate, and reason with contextual information from other sources to establish a more complete picture.

Image Clustering

There is a Time and Place for Reasoning Beyond the Image

1 code implementation1 Mar 2022 Xingyu Fu, Ben Zhou, Ishaan Preetam Chandratreya, Carl Vondrick, Dan Roth

For example, in Figure 1, we can find a way to identify the news articles related to the picture through segment-wise understandings of the signs, the buildings, the crowds, and more.

Image Clustering

An FEA surrogate model with Boundary Oriented Graph Embedding approach

no code implementations30 Aug 2021 Xingyu Fu, Fengfeng Zhou, Dheeraj Peddireddy, Zhengyang Kang, Martin Byung-Guk Jun, Vaneet Aggarwal

In this work, we present a Boundary Oriented Graph Embedding (BOGE) approach for the Graph Neural Network (GNN) to serve as a general surrogate model for regressing physical fields and solving boundary value problems.

Cantilever Beam Decision Making +1

SRQA: Synthetic Reader for Factoid Question Answering

1 code implementation2 Sep 2020 Jiuniu Wang, Wenjia Xu, Xingyu Fu, Yang Wei, Li Jin, Ziyan Chen, Guangluan Xu, Yirong Wu

This model enhances the question answering system in the multi-document scenario from three aspects: model structure, optimization goal, and training method, corresponding to Multilayer Attention (MA), Cross Evidence (CE), and Adversarial Training (AT) respectively.

Question Answering

ASTRAL: Adversarial Trained LSTM-CNN for Named Entity Recognition

1 code implementation2 Sep 2020 Jiuniu Wang, Wenjia Xu, Xingyu Fu, Guangluan Xu, Yirong Wu

Under such circumstances, how to make full use of the information extracted by word embedding requires more in-depth research.

named-entity-recognition NER

Design Challenges in Low-resource Cross-lingual Entity Linking

1 code implementation EMNLP 2020 Xingyu Fu, Weijia Shi, Xiaodong Yu, Zian Zhao, Dan Roth

Cross-lingual Entity Linking (XEL), the problem of grounding mentions of entities in a foreign language text into an English knowledge base such as Wikipedia, has seen a lot of research in recent years, with a range of promising techniques.

Cross-Lingual Entity Linking Entity Linking

AlphaGomoku: An AlphaGo-based Gomoku Artificial Intelligence using Curriculum Learning

1 code implementation27 Sep 2018 Zheng Xie, Xingyu Fu, JinYuan Yu

In this project, we combine AlphaGo algorithm with Curriculum Learning to crack the game of Gomoku.

A Machine Learning Framework for Stock Selection

1 code implementation5 Jun 2018 XingYu Fu, JinHong Du, Yifeng Guo, Mingwen Liu, Tao Dong, XiuWen Duan

The effectiveness of the stock selection strategy is validated in Chinese stock market in both statistical and practical aspects, showing that: 1) Stacking outperforms other models reaching an AUC score of 0. 972; 2) Genetic Algorithm picks a subset of 114 features and the prediction performances of all models remain almost unchanged after the selection procedure, which suggests some features are indeed redundant; 3) LR and DNN are radical models; RF is risk-neutral model; Stacking is somewhere between DNN and RF.

BIG-bench Machine Learning

Robust Log-Optimal Strategy with Reinforcement Learning

1 code implementation1 May 2018 Yifeng Guo, Xingyu Fu, Yuyan Shi, Mingwen Liu

We proposed a new Portfolio Management method termed as Robust Log-Optimal Strategy (RLOS), which ameliorates the General Log-Optimal Strategy (GLOS) by approximating the traditional objective function with quadratic Taylor expansion.

Management reinforcement-learning

Language Distribution Prediction based on Batch Markov Monte Carlo Simulation with Migration

no code implementations26 Feb 2018 XingYu Fu, ZiYi Yang, XiuWen Duan

To model the randomness of language spreading, we propose the Batch Markov Monte Carlo Simulation with Migration(BMMCSM) algorithm, in which each agent is treated as a language stack.

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