Search Results for author: Yuchen Fang

Found 19 papers, 10 papers with code

CodeApex: A Bilingual Programming Evaluation Benchmark for Large Language Models

1 code implementation5 Sep 2023 Lingyue Fu, Huacan Chai, Shuang Luo, Kounianhua Du, Weiming Zhang, Longteng Fan, Jiayi Lei, Renting Rui, Jianghao Lin, Yuchen Fang, Yifan Liu, Jingkuan Wang, Siyuan Qi, Kangning Zhang, Weinan Zhang, Yong Yu

CodeApex comprises three types of multiple-choice questions: conceptual understanding, commonsense reasoning, and multi-hop reasoning, designed to evaluate LLMs on programming comprehension tasks.

Code Generation Multiple-choice

HUTFormer: Hierarchical U-Net Transformer for Long-Term Traffic Forecasting

no code implementations27 Jul 2023 Zezhi Shao, Fei Wang, Zhao Zhang, Yuchen Fang, Guangyin Jin, Yongjun Xu

Then, we propose a novel Hierarchical U-net TransFormer (HUTFormer) to address the issues of long-term traffic forecasting.

Time Series Time Series Forecasting

Learning Multi-Agent Intention-Aware Communication for Optimal Multi-Order Execution in Finance

no code implementations6 Jul 2023 Yuchen Fang, Zhenggang Tang, Kan Ren, Weiqing Liu, Li Zhao, Jiang Bian, Dongsheng Li, Weinan Zhang, Yong Yu, Tie-Yan Liu

Order execution is a fundamental task in quantitative finance, aiming at finishing acquisition or liquidation for a number of trading orders of the specific assets.

Reinforcement Learning (RL)

Protecting the Future: Neonatal Seizure Detection with Spatial-Temporal Modeling

no code implementations2 Jul 2023 Ziyue Li, Yuchen Fang, You Li, Kan Ren, Yansen Wang, Xufang Luo, Juanyong Duan, Congrui Huang, Dongsheng Li, Lili Qiu

A timely detection of seizures for newborn infants with electroencephalogram (EEG) has been a common yet life-saving practice in the Neonatal Intensive Care Unit (NICU).

EEG Electroencephalogram (EEG) +1

Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Survey

no code implementations25 Mar 2023 Guangyin Jin, Yuxuan Liang, Yuchen Fang, Zezhi Shao, Jincai Huang, Junbo Zhang, Yu Zheng

STGNNs enable the extraction of complex spatio-temporal dependencies by integrating graph neural networks (GNNs) and various temporal learning methods.


Fully Stochastic Trust-Region Sequential Quadratic Programming for Equality-Constrained Optimization Problems

1 code implementation29 Nov 2022 Yuchen Fang, Sen Na, Michael W. Mahoney, Mladen Kolar

We propose a trust-region stochastic sequential quadratic programming algorithm (TR-StoSQP) to solve nonlinear optimization problems with stochastic objectives and deterministic equality constraints.

Fine-Grained Trajectory-based Travel Time Estimation for Multi-city Scenarios Based on Deep Meta-Learning

1 code implementation20 Jan 2022 Chenxing Wang, Fang Zhao, Haichao Zhang, Haiyong Luo, Yanjun Qin, Yuchen Fang

To tackle these challenges, we propose a meta learning based framework, MetaTTE, to continuously provide accurate travel time estimation over time by leveraging well-designed deep neural network model called DED, which consists of Data preprocessing module and Encoder-Decoder network module.

Meta-Learning Travel Time Estimation

Spatio-Temporal meets Wavelet: Disentangled Traffic Flow Forecasting via Efficient Spectral Graph Attention Network

no code implementations6 Dec 2021 Yuchen Fang, Yanjun Qin, Haiyong Luo, Fang Zhao, Bingbing Xu, Chenxing Wang, Liang Zeng

Traffic forecasting is crucial for public safety and resource optimization, yet is very challenging due to three aspects: i) current existing works mostly exploit intricate temporal patterns (e. g., the short-term thunderstorm and long-term daily trends) within a single method, which fail to accurately capture spatio-temporal dependencies under different schemas; ii) the under-exploration of the graph positional encoding limit the extraction of spatial information in the commonly used full graph attention network; iii) the quadratic complexity of the full graph attention introduces heavy computational needs.

Graph Attention Time Series Analysis

CDGNet: A Cross-Time Dynamic Graph-based Deep Learning Model for Traffic Forecasting

no code implementations6 Dec 2021 Yuchen Fang, Yanjun Qin, Haiyong Luo, Fang Zhao, Liang Zeng, Bo Hui, Chenxing Wang

Besides, we propose a novel encoder-decoder architecture to incorporate the cross-time dynamic graph-based GCN for multi-step traffic forecasting.

DMGCRN: Dynamic Multi-Graph Convolution Recurrent Network for Traffic Forecasting

no code implementations4 Dec 2021 Yanjun Qin, Yuchen Fang, Haiyong Luo, Fang Zhao, Chenxing Wang

In this paper, we propose a novel dynamic multi-graph convolution recurrent network (DMGCRN) to tackle above issues, which can model the spatial correlations of distance, the spatial correlations of structure, and the temporal correlations simultaneously.

Universal Trading for Order Execution with Oracle Policy Distillation

no code implementations28 Jan 2021 Yuchen Fang, Kan Ren, Weiqing Liu, Dong Zhou, Weinan Zhang, Jiang Bian, Yong Yu, Tie-Yan Liu

As a fundamental problem in algorithmic trading, order execution aims at fulfilling a specific trading order, either liquidation or acquirement, for a given instrument.

Algorithmic Trading reinforcement-learning +1

An Efficient Neighborhood-based Interaction Model for Recommendation on Heterogeneous Graph

1 code implementation1 Jul 2020 Jiarui Jin, Jiarui Qin, Yuchen Fang, Kounianhua Du, Wei-Nan Zhang, Yong Yu, Zheng Zhang, Alexander J. Smola

To the best of our knowledge, this is the first work providing an efficient neighborhood-based interaction model in the HIN-based recommendations.

Recommendation Systems

User Behavior Retrieval for Click-Through Rate Prediction

1 code implementation28 May 2020 Jiarui Qin, Wei-Nan Zhang, Xin Wu, Jiarui Jin, Yuchen Fang, Yong Yu

These retrieved behaviors are then fed into a deep model to make the final prediction instead of simply using the most recent ones.

Click-Through Rate Prediction Retrieval

A Deep Recurrent Survival Model for Unbiased Ranking

1 code implementation30 Apr 2020 Jiarui Jin, Yuchen Fang, Wei-Nan Zhang, Kan Ren, Guorui Zhou, Jian Xu, Yong Yu, Jun Wang, Xiaoqiang Zhu, Kun Gai

Position bias is a critical problem in information retrieval when dealing with implicit yet biased user feedback data.

Information Retrieval Retrieval +1

Lifelong Sequential Modeling with Personalized Memorization for User Response Prediction

1 code implementation2 May 2019 Kan Ren, Jiarui Qin, Yuchen Fang, Wei-Nan Zhang, Lei Zheng, Weijie Bian, Guorui Zhou, Jian Xu, Yong Yu, Xiaoqiang Zhu, Kun Gai

In order to tackle these challenges, in this paper, we propose a Hierarchical Periodic Memory Network for lifelong sequential modeling with personalized memorization of sequential patterns for each user.


Learning Multi-touch Conversion Attribution with Dual-attention Mechanisms for Online Advertising

1 code implementation11 Aug 2018 Kan Ren, Yuchen Fang, Wei-Nan Zhang, Shuhao Liu, Jiajun Li, Ya zhang, Yong Yu, Jun Wang

To achieve this, we utilize sequence-to-sequence prediction for user clicks, and combine both post-view and post-click attribution patterns together for the final conversion estimation.

Cannot find the paper you are looking for? You can Submit a new open access paper.