Search Results for author: Chen Zheng

Found 19 papers, 5 papers with code

Relevant CommonSense Subgraphs for “What if...” Procedural Reasoning

no code implementations Findings (ACL) 2022 Chen Zheng, Parisa Kordjamshidi

We study the challenge of learning causal reasoning over procedural text to answer “What if...” questions when external commonsense knowledge is required.

GCF: Generalized Causal Forest for Heterogeneous Treatment Effect Estimation in Online Marketplace

no code implementations21 Mar 2022 Shu Wan, Chen Zheng, Zhonggen Sun, Mengfan Xu, Xiaoqing Yang, Hongtu Zhu, Jiecheng Guo

Uplift modeling is a rapidly growing approach that utilizes machine learning and causal inference methods to estimate the heterogeneous treatment effects.

Causal Inference Decision Making

Relevant CommonSense Subgraphs for "What if..." Procedural Reasoning

1 code implementation21 Mar 2022 Chen Zheng, Parisa Kordjamshidi

We study the challenge of learning causal reasoning over procedural text to answer "What if..." questions when external commonsense knowledge is required.

GCF: Generalized Causal Forest for Heterogeneous Treatment Effect Estimation Using Nonparametric Methods

no code implementations29 Sep 2021 Shu Wan, Chen Zheng, Zhonggen Sun, Mengfan Xu, Xiaoqing Yang, Jiecheng Guo, Hongtu Zhu

Heterogeneous treatment effect (HTE) estimation with continuous treatment is essential in multiple disciplines, such as the online marketplace and pharmaceutical industry.

Relational Gating for "What If" Reasoning

1 code implementation27 May 2021 Chen Zheng, Parisa Kordjamshidi

We propose a novel relational gating network that learns to filter the key entities and relationships and learns contextual and cross representations of both procedure and question for finding the answer.

Cross-Modality Relevance for Reasoning on Language and Vision

1 code implementation ACL 2020 Chen Zheng, Quan Guo, Parisa Kordjamshidi

This work deals with the challenge of learning and reasoning over language and vision data for the related downstream tasks such as visual question answering (VQA) and natural language for visual reasoning (NLVR).

Question Answering Visual Question Answering +2

AIBench: An Industry Standard Internet Service AI Benchmark Suite

no code implementations13 Aug 2019 Wanling Gao, Fei Tang, Lei Wang, Jianfeng Zhan, Chunxin Lan, Chunjie Luo, Yunyou Huang, Chen Zheng, Jiahui Dai, Zheng Cao, Daoyi Zheng, Haoning Tang, Kunlin Zhan, Biao Wang, Defei Kong, Tong Wu, Minghe Yu, Chongkang Tan, Huan Li, Xinhui Tian, Yatao Li, Junchao Shao, Zhenyu Wang, Xiaoyu Wang, Hainan Ye

On the basis of the AIBench framework, abstracting the real-world data sets and workloads from one of the top e-commerce providers, we design and implement the first end-to-end Internet service AI benchmark, which contains the primary modules in the critical paths of an industry scale application and is scalable to deploy on different cluster scales.

Learning-To-Rank

Edge AIBench: Towards Comprehensive End-to-end Edge Computing Benchmarking

no code implementations6 Aug 2019 Tianshu Hao, Yunyou Huang, Xu Wen, Wanling Gao, Fan Zhang, Chen Zheng, Lei Wang, Hainan Ye, Kai Hwang, Zujie Ren, Jianfeng Zhan

In edge computing scenarios, the distribution of data and collaboration of workloads on different layers are serious concerns for performance, privacy, and security issues.

Performance Distributed, Parallel, and Cluster Computing

RLTM: An Efficient Neural IR Framework for Long Documents

no code implementations22 Jun 2019 Chen Zheng, Yu Sun, Shengxian Wan, dianhai yu

This paper proposes a novel End-to-End neural ranking framework called Reinforced Long Text Matching (RLTM) which matches a query with long documents efficiently and effectively.

Information Retrieval Text Matching

Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals

3 code implementations Chem. Mater. 2018 Chi Chen, Weike Ye, Yunxing Zuo, Chen Zheng, Shyue Ping Ong

Similarly, we show that MEGNet models trained on $\sim 60, 000$ crystals in the Materials Project substantially outperform prior ML models in the prediction of the formation energies, band gaps and elastic moduli of crystals, achieving better than DFT accuracy over a much larger data set.

Drug Discovery Formation Energy Materials Science Computational Physics

BigDataBench: A Scalable and Unified Big Data and AI Benchmark Suite

no code implementations23 Feb 2018 Wanling Gao, Jianfeng Zhan, Lei Wang, Chunjie Luo, Daoyi Zheng, Xu Wen, Rui Ren, Chen Zheng, Xiwen He, Hainan Ye, Haoning Tang, Zheng Cao, Shujie Zhang, Jiahui Dai

On the basis of our previous work that identifies eight data motifs taking up most of the run time of a wide variety of big data and AI workloads, we propose a scalable benchmarking methodology that uses the combination of one or more data motifs---to represent diversity of big data and AI workloads.

A Deep Learning Approach for Expert Identification in Question Answering Communities

no code implementations14 Nov 2017 Chen Zheng, Shuangfei Zhai, Zhongfei Zhang

This approach uses the convolutional neural network and combines user feature representations with question feature representations to compute scores that the user who gets the highest score is the expert on this question.

Question Answering

Machine Learning Based Fast Power Integrity Classifier

no code implementations8 Nov 2017 HuaChun Zhang, Lynden Kagan, Chen Zheng

In this paper, we proposed a new machine learning based fast power integrity classifier that quickly flags the EM/IR hotspots.

Automated Generation and Ensemble-Learned Matching of X-ray Absorption Spectra

no code implementations6 Nov 2017 Chen Zheng, Kiran Mathew, Chi Chen, Yiming Chen, Hanmei Tang, Alan Dozier, Joshua J. Kas, Fernando D. Vila, John J. Rehr, Louis F. J. Piper, Kristin Persson, Shyue Ping Ong

We report the development of XASdb, a large database of computed reference X-ray absorption spectra (XAS), and a novel Ensemble-Learned Spectra IdEntification (ELSIE) algorithm for the matching of spectra.

Materials Science

Customized Routing Optimization Based on Gradient Boost Regressor Model

no code implementations28 Oct 2017 Chen Zheng, Clara Grzegorz Kasprowicz, Carol Saunders

Customized routing optimizations are applied to the URNs and results show clear timing improvement and trend to converge toward timing closure.

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