Search Results for author: Yanru Qu

Found 18 papers, 14 papers with code

MolCRAFT: Structure-Based Drug Design in Continuous Parameter Space

2 code implementations18 Apr 2024 Yanru Qu, Keyue Qiu, Yuxuan Song, Jingjing Gong, Jiawei Han, Mingyue Zheng, Hao Zhou, Wei-Ying Ma

Generative models for structure-based drug design (SBDD) have shown promising results in recent years.

Unified Generative Modeling of 3D Molecules via Bayesian Flow Networks

1 code implementation17 Mar 2024 Yuxuan Song, Jingjing Gong, Yanru Qu, Hao Zhou, Mingyue Zheng, Jingjing Liu, Wei-Ying Ma

Advanced generative model (e. g., diffusion model) derived from simplified continuity assumptions of data distribution, though showing promising progress, has been difficult to apply directly to geometry generation applications due to the multi-modality and noise-sensitive nature of molecule geometry.

3D Molecule Generation

MAP: A Model-agnostic Pretraining Framework for Click-through Rate Prediction

1 code implementation3 Aug 2023 Jianghao Lin, Yanru Qu, Wei Guo, Xinyi Dai, Ruiming Tang, Yong Yu, Weinan Zhang

The large capacity of neural models helps digest such massive amounts of data under the supervised learning paradigm, yet they fail to utilize the substantial data to its full potential, since the 1-bit click signal is not sufficient to guide the model to learn capable representations of features and instances.

Binary Classification Click-Through Rate Prediction +1

Task-wise Split Gradient Boosting Trees for Multi-center Diabetes Prediction

1 code implementation16 Aug 2021 Mingcheng Chen, Zhenghui Wang, Zhiyun Zhao, Weinan Zhang, Xiawei Guo, Jian Shen, Yanru Qu, Jieli Lu, Min Xu, Yu Xu, Tiange Wang, Mian Li, Wei-Wei Tu, Yong Yu, Yufang Bi, Weiqing Wang, Guang Ning

To tackle the above challenges, we employ gradient boosting decision trees (GBDT) to handle data heterogeneity and introduce multi-task learning (MTL) to solve data insufficiency.

Diabetes Prediction Multi-Task Learning

Event Time Extraction and Propagation via Graph Attention Networks

1 code implementation NAACL 2021 Haoyang Wen, Yanru Qu, Heng Ji, Qiang Ning, Jiawei Han, Avi Sil, Hanghang Tong, Dan Roth

Grounding events into a precise timeline is important for natural language understanding but has received limited attention in recent work.

Graph Attention Natural Language Understanding +3

GIKT: A Graph-based Interaction Model for Knowledge Tracing

3 code implementations13 Sep 2020 Yang Yang, Jian Shen, Yanru Qu, Yunfei Liu, Kerong Wang, Yaoming Zhu, Wei-Nan Zhang, Yong Yu

With the rapid development in online education, knowledge tracing (KT) has become a fundamental problem which traces students' knowledge status and predicts their performance on new questions.

Knowledge Tracing

An End-to-End Neighborhood-based Interaction Model for Knowledge-enhanced Recommendation

1 code implementation12 Aug 2019 Yanru Qu, Ting Bai, Wei-Nan Zhang, Jian-Yun Nie, Jian Tang

This paper studies graph-based recommendation, where an interaction graph is constructed from historical records and is lever-aged to alleviate data sparsity and cold start problems.

Click-Through Rate Prediction Knowledge Graphs

Dynamically Fused Graph Network for Multi-hop Reasoning

1 code implementation ACL 2019 Yunxuan Xiao, Yanru Qu, Lin Qiu, Hao Zhou, Lei LI, Wei-Nan Zhang, Yong Yu

However, many difficult questions require multiple supporting evidence from scattered text among two or more documents.

Question Answering

Sampled in Pairs and Driven by Text: A New Graph Embedding Framework

no code implementations12 Sep 2018 Liheng Chen, Yanru Qu, Zhenghui Wang, Lin Qiu, Wei-Nan Zhang, Ken Chen, Shaodian Zhang, Yong Yu

TGE-PS uses Pairs Sampling (PS) to improve the sampling strategy of RW, being able to reduce ~99% training samples while preserving competitive performance.

Graph Embedding Link Prediction

Product-based Neural Networks for User Response Prediction over Multi-field Categorical Data

8 code implementations1 Jul 2018 Yanru Qu, Bohui Fang, Wei-Nan Zhang, Ruiming Tang, Minzhe Niu, Huifeng Guo, Yong Yu, Xiuqiang He

User response prediction is a crucial component for personalized information retrieval and filtering scenarios, such as recommender system and web search.

Click-Through Rate Prediction Feature Engineering +3

QA4IE: A Question Answering based Framework for Information Extraction

1 code implementation10 Apr 2018 Lin Qiu, Hao Zhou, Yanru Qu, Wei-Nan Zhang, Suoheng Li, Shu Rong, Dongyu Ru, Lihua Qian, Kewei Tu, Yong Yu

Information Extraction (IE) refers to automatically extracting structured relation tuples from unstructured texts.

Question Answering Relation +2

Wasserstein Distance Guided Representation Learning for Domain Adaptation

8 code implementations5 Jul 2017 Jian Shen, Yanru Qu, Wei-Nan Zhang, Yong Yu

Inspired by Wasserstein GAN, in this paper we propose a novel approach to learn domain invariant feature representations, namely Wasserstein Distance Guided Representation Learning (WDGRL).

Domain Adaptation General Classification +2

Product-based Neural Networks for User Response Prediction

11 code implementations1 Nov 2016 Yanru Qu, Han Cai, Kan Ren, Wei-Nan Zhang, Yong Yu, Ying Wen, Jun Wang

Predicting user responses, such as clicks and conversions, is of great importance and has found its usage in many Web applications including recommender systems, web search and online advertising.

Click-Through Rate Prediction Recommendation Systems

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