Search Results for author: Yuyu Zhang

Found 15 papers, 4 papers with code

GNN is a Counter? Revisiting GNN for Question Answering

no code implementations ICLR 2022 Kuan Wang, Yuyu Zhang, Diyi Yang, Le Song, Tao Qin

To open the black box of GNN and investigate these problems, we dissect state-of-the-art GNN modules for QA and analyze their reasoning capability.

Knowledge Graphs Question Answering

Speeding up Computational Morphogenesis with Online Neural Synthetic Gradients

no code implementations25 Apr 2021 Yuyu Zhang, Heng Chi, Binghong Chen, Tsz Ling Elaine Tang, Lucia Mirabella, Le Song, Glaucio H. Paulino

We successfully apply our ONSG framework to computational morphogenesis, a representative and challenging class of PDE-constrained optimization problems.

Question Directed Graph Attention Network for Numerical Reasoning over Text

no code implementations EMNLP 2020 Kunlong Chen, Weidi Xu, Xingyi Cheng, Zou Xiaochuan, Yuyu Zhang, Le Song, Taifeng Wang, Yuan Qi, Wei Chu

Numerical reasoning over texts, such as addition, subtraction, sorting and counting, is a challenging machine reading comprehension task, since it requires both natural language understanding and arithmetic computation.

Graph Attention Machine Reading Comprehension +2

Answering Any-hop Open-domain Questions with Iterative Document Reranking

no code implementations16 Sep 2020 Ping Nie, Yuyu Zhang, Arun Ramamurthy, Le Song

Existing approaches for open-domain question answering (QA) are typically designed for questions that require either single-hop or multi-hop reasoning, which make strong assumptions of the complexity of questions to be answered.

Multi-hop Question Answering Open-Domain Question Answering

DC-BERT: Decoupling Question and Document for Efficient Contextual Encoding

no code implementations28 Feb 2020 Yuyu Zhang, Ping Nie, Xiubo Geng, Arun Ramamurthy, Le Song, Daxin Jiang

Recent studies on open-domain question answering have achieved prominent performance improvement using pre-trained language models such as BERT.

Open-Domain Question Answering

Efficient Probabilistic Logic Reasoning with Graph Neural Networks

1 code implementation ICLR 2020 Yuyu Zhang, Xinshi Chen, Yuan Yang, Arun Ramamurthy, Bo Li, Yuan Qi, Le Song

In this paper, we explore the combination of MLNs and GNNs, and use graph neural networks for variational inference in MLN.

Variational Inference

Language Modeling with Shared Grammar

no code implementations ACL 2019 Yuyu Zhang, Le Song

Sequential recurrent neural networks have achieved superior performance on language modeling, but overlook the structure information in natural language.

Language Modelling

Can Graph Neural Networks Help Logic Reasoning?

no code implementations5 Jun 2019 Yuyu Zhang, Xinshi Chen, Yuan Yang, Arun Ramamurthy, Bo Li, Yuan Qi, Le Song

Effectively combining logic reasoning and probabilistic inference has been a long-standing goal of machine learning: the former has the ability to generalize with small training data, while the latter provides a principled framework for dealing with noisy data.

Variational Reasoning for Question Answering with Knowledge Graph

1 code implementation12 Sep 2017 Yuyu Zhang, Hanjun Dai, Zornitsa Kozareva, Alexander J. Smola, Le Song

Knowledge graph (KG) is known to be helpful for the task of question answering (QA), since it provides well-structured relational information between entities, and allows one to further infer indirect facts.

Knowledge Graphs Question Answering +1

Learning Combinatorial Optimization Algorithms over Graphs

8 code implementations NeurIPS 2017 Hanjun Dai, Elias B. Khalil, Yuyu Zhang, Bistra Dilkina, Le Song

The design of good heuristics or approximation algorithms for NP-hard combinatorial optimization problems often requires significant specialized knowledge and trial-and-error.

Combinatorial Optimization Graph Embedding

FLASH: Fast Bayesian Optimization for Data Analytic Pipelines

1 code implementation20 Feb 2016 Yuyu Zhang, Mohammad Taha Bahadori, Hang Su, Jimeng Sun

To achieve the best performance, it is often critical to select optimal algorithms and to set appropriate hyperparameters, which requires large computational efforts.

Large Scale Purchase Prediction with Historical User Actions on B2C Online Retail Platform

no code implementations27 Aug 2014 Yuyu Zhang, Liang Pang, Lei Shi, Bin Wang

This paper describes the solution of Bazinga Team for Tmall Recommendation Prize 2014.

Combination of Diverse Ranking Models for Personalized Expedia Hotel Searches

no code implementations29 Nov 2013 Xudong Liu, Bing Xu, Yuyu Zhang, Qiang Yan, Liang Pang, Qiang Li, Hanxiao Sun, Bin Wang

The ICDM Challenge 2013 is to apply machine learning to the problem of hotel ranking, aiming to maximize purchases according to given hotel characteristics, location attractiveness of hotels, user's aggregated purchase history and competitive online travel agency information for each potential hotel choice.

Feature Engineering

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