Search Results for author: Bin Cui

Found 69 papers, 36 papers with code

Don't Waste Your Bits! Squeeze Activations and Gradients for Deep Neural Networks via TinyScript

no code implementations ICML 2020 Fangcheng Fu, Yuzheng Hu, Yihan He, Jiawei Jiang, Yingxia Shao, Ce Zhang, Bin Cui

Recent years have witnessed intensive research interests on training deep neural networks (DNNs) more efficiently by quantization-based compression methods, which facilitate DNNs training in two ways: (1) activations are quantized to shrink the memory consumption, and (2) gradients are quantized to decrease the communication cost.


SpotServe: Serving Generative Large Language Models on Preemptible Instances

1 code implementation27 Nov 2023 Xupeng Miao, Chunan Shi, Jiangfei Duan, Xiaoli Xi, Dahua Lin, Bin Cui, Zhihao Jia

This paper aims to reduce the monetary cost for serving LLMs by leveraging preemptible GPU instances on modern clouds, which offer accesses to spare GPUs at a much cheaper price than regular instances but may be preempted by the cloud at any time.

Graph Matching

Experimental Analysis of Large-scale Learnable Vector Storage Compression

1 code implementation27 Nov 2023 Hailin Zhang, Penghao Zhao, Xupeng Miao, Yingxia Shao, Zirui Liu, Tong Yang, Bin Cui

Learnable embedding vector is one of the most important applications in machine learning, and is widely used in various database-related domains.

Accelerating Scalable Graph Neural Network Inference with Node-Adaptive Propagation

no code implementations17 Oct 2023 Xinyi Gao, Wentao Zhang, Junliang Yu, Yingxia Shao, Quoc Viet Hung Nguyen, Bin Cui, Hongzhi Yin

To further accelerate Scalable GNNs inference in this inductive setting, we propose an online propagation framework and two novel node-adaptive propagation methods that can customize the optimal propagation depth for each node based on its topological information and thereby avoid redundant feature propagation.

Model-enhanced Vector Index

1 code implementation23 Sep 2023 Hailin Zhang, Yujing Wang, Qi Chen, Ruiheng Chang, Ting Zhang, Ziming Miao, Yingyan Hou, Yang Ding, Xupeng Miao, Haonan Wang, Bochen Pang, Yuefeng Zhan, Hao Sun, Weiwei Deng, Qi Zhang, Fan Yang, Xing Xie, Mao Yang, Bin Cui

We empirically show that our model achieves better performance on the commonly used academic benchmarks MSMARCO Passage and Natural Questions, with comparable serving latency to dense retrieval solutions.

Natural Questions Quantization +1

Towards General and Efficient Online Tuning for Spark

no code implementations5 Sep 2023 Yang Li, Huaijun Jiang, Yu Shen, Yide Fang, Xiaofeng Yang, Danqing Huang, Xinyi Zhang, Wentao Zhang, Ce Zhang, Peng Chen, Bin Cui

The distributed data analytic system -- Spark is a common choice for processing massive volumes of heterogeneous data, while it is challenging to tune its parameters to achieve high performance.

Bayesian Optimization Meta-Learning

Mitigating Semantic Confusion from Hostile Neighborhood for Graph Active Learning

no code implementations17 Aug 2023 Tianmeng Yang, Min Zhou, Yujing Wang, Zhengjie Lin, Lujia Pan, Bin Cui, Yunhai Tong

Graph Active Learning (GAL), which aims to find the most informative nodes in graphs for annotation to maximize the Graph Neural Networks (GNNs) performance, has attracted many research efforts but remains non-trivial challenges.

Active Learning Node Classification

VQGraph: Rethinking Graph Representation Space for Bridging GNNs and MLPs

1 code implementation4 Aug 2023 Ling Yang, Ye Tian, Minkai Xu, Zhongyi Liu, Shenda Hong, Wei Qu, Wentao Zhang, Bin Cui, Muhan Zhang, Jure Leskovec

To address this issue, we propose to learn a new powerful graph representation space by directly labeling nodes' diverse local structures for GNN-to-MLP distillation.

Knowledge Distillation Quantization +1

BOURNE: Bootstrapped Self-supervised Learning Framework for Unified Graph Anomaly Detection

no code implementations28 Jul 2023 Jie Liu, Mengting He, Xuequn Shang, Jieming Shi, Bin Cui, Hongzhi Yin

By swapping the context embeddings between nodes and edges and measuring the agreement in the embedding space, we enable the mutual detection of node and edge anomalies.

CoLA Contrastive Learning +2

Improving Automatic Parallel Training via Balanced Memory Workload Optimization

no code implementations5 Jul 2023 Yujie Wang, Youhe Jiang, Xupeng Miao, Fangcheng Fu, Xiaonan Nie, Bin Cui

Transformer models have emerged as the leading approach for achieving state-of-the-art performance across various application domains, serving as the foundation for advanced large-scale deep learning (DL) models.


Individual and Structural Graph Information Bottlenecks for Out-of-Distribution Generalization

1 code implementation28 Jun 2023 Ling Yang, Jiayi Zheng, Heyuan Wang, Zhongyi Liu, Zhilin Huang, Shenda Hong, Wentao Zhang, Bin Cui

To remove class spurious feature caused by distribution shifts, we propose Individual Graph Information Bottleneck (I-GIB) which discards irrelevant information by minimizing the mutual information between the input graph and its embeddings.

Graph Learning Out-of-Distribution Generalization

FISEdit: Accelerating Text-to-image Editing via Cache-enabled Sparse Diffusion Inference

no code implementations27 May 2023 Zihao Yu, Haoyang Li, Fangcheng Fu, Xupeng Miao, Bin Cui

The key intuition behind our approach is to utilize the semantic mapping between the minor modifications on the input text and the affected regions on the output image.

OpenBox: A Python Toolkit for Generalized Black-box Optimization

1 code implementation26 Apr 2023 Huaijun Jiang, Yu Shen, Yang Li, Wentao Zhang, Ce Zhang, Bin Cui

Black-box optimization (BBO) has a broad range of applications, including automatic machine learning, experimental design, and database knob tuning.

Experimental Design

FlexMoE: Scaling Large-scale Sparse Pre-trained Model Training via Dynamic Device Placement

no code implementations8 Apr 2023 Xiaonan Nie, Xupeng Miao, Zilong Wang, Zichao Yang, Jilong Xue, Lingxiao Ma, Gang Cao, Bin Cui

We first present an empirical analysis on the problems and opportunities of training MoE models, which motivates us to overcome the routing imbalance and fluctuation problems by a dynamic expert management and device placement mechanism.


A Unified and Efficient Coordinating Framework for Autonomous DBMS Tuning

no code implementations10 Mar 2023 Xinyi Zhang, Zhuo Chang, Hong Wu, Yang Li, Jia Chen, Jian Tan, Feifei Li, Bin Cui

To tune different components for DBMS, a coordinating mechanism is needed to make the multiple agents cognizant of each other.

Thompson Sampling

Angel-PTM: A Scalable and Economical Large-scale Pre-training System in Tencent

no code implementations6 Mar 2023 Xiaonan Nie, Yi Liu, Fangcheng Fu, Jinbao Xue, Dian Jiao, Xupeng Miao, Yangyu Tao, Bin Cui

Recent years have witnessed the unprecedented achievements of large-scale pre-trained models, especially the Transformer models.

Management Scheduling

Transfer Learning for Bayesian Optimization: A Survey

no code implementations12 Feb 2023 Tianyi Bai, Yang Li, Yu Shen, Xinyi Zhang, Wentao Zhang, Bin Cui

A wide spectrum of design and decision problems, including parameter tuning, A/B testing and drug design, intrinsically are instances of black-box optimization.

Bayesian Optimization Transfer Learning

Rover: An online Spark SQL tuning service via generalized transfer learning

no code implementations8 Feb 2023 Yu Shen, Xinyuyang Ren, Yupeng Lu, Huaijun Jiang, Huanyong Xu, Di Peng, Yang Li, Wentao Zhang, Bin Cui

When applying transfer learning to accelerate the tuning process, we notice two domain-specific challenges: 1) most previous work focus on transferring tuning history, while expert knowledge from Spark engineers is of great potential to improve the tuning performance but is not well studied so far; 2) history tasks should be carefully utilized, where using dissimilar ones lead to a deteriorated performance in production.

Bayesian Optimization Transfer Learning

DivBO: Diversity-aware CASH for Ensemble Learning

no code implementations7 Feb 2023 Yu Shen, Yupeng Lu, Yang Li, Yaofeng Tu, Wentao Zhang, Bin Cui

To tackle this issue and further enhance the ensemble performance, we propose DivBO, a diversity-aware framework to inject explicit search of diversity into the CASH problems.

AutoML Bayesian Optimization +1

Galvatron: Efficient Transformer Training over Multiple GPUs Using Automatic Parallelism

2 code implementations25 Nov 2022 Xupeng Miao, Yujie Wang, Youhe Jiang, Chunan Shi, Xiaonan Nie, Hailin Zhang, Bin Cui

Transformer models have achieved state-of-the-art performance on various domains of applications and gradually becomes the foundations of the advanced large deep learning (DL) models.

Diffusion-Based Scene Graph to Image Generation with Masked Contrastive Pre-Training

1 code implementation21 Nov 2022 Ling Yang, Zhilin Huang, Yang song, Shenda Hong, Guohao Li, Wentao Zhang, Bin Cui, Bernard Ghanem, Ming-Hsuan Yang

Generating images from graph-structured inputs, such as scene graphs, is uniquely challenging due to the difficulty of aligning nodes and connections in graphs with objects and their relations in images.

Image Generation

Efficient Graph Neural Network Inference at Large Scale

no code implementations1 Nov 2022 Xinyi Gao, Wentao Zhang, Yingxia Shao, Quoc Viet Hung Nguyen, Bin Cui, Hongzhi Yin

Graph neural networks (GNNs) have demonstrated excellent performance in a wide range of applications.

Distributed Graph Neural Network Training: A Survey

no code implementations1 Nov 2022 Yingxia Shao, Hongzheng Li, Xizhi Gu, Hongbo Yin, Yawen Li, Xupeng Miao, Wentao Zhang, Bin Cui, Lei Chen

In recent years, many efforts have been made on distributed GNN training, and an array of training algorithms and systems have been proposed.

Distributed Computing

CALIP: Zero-Shot Enhancement of CLIP with Parameter-free Attention

1 code implementation28 Sep 2022 Ziyu Guo, Renrui Zhang, Longtian Qiu, Xianzheng Ma, Xupeng Miao, Xuming He, Bin Cui

Contrastive Language-Image Pre-training (CLIP) has been shown to learn visual representations with great transferability, which achieves promising accuracy for zero-shot classification.

Training-free 3D Point Cloud Classification Transfer Learning +1

Diffusion Models: A Comprehensive Survey of Methods and Applications

2 code implementations2 Sep 2022 Ling Yang, Zhilong Zhang, Yang song, Shenda Hong, Runsheng Xu, Yue Zhao, Yingxia Shao, Wentao Zhang, Bin Cui, Ming-Hsuan Yang

This survey aims to provide a contextualized, in-depth look at the state of diffusion models, identifying the key areas of focus and pointing to potential areas for further exploration.

Image Super-Resolution Video Generation

Towards Communication-efficient Vertical Federated Learning Training via Cache-enabled Local Updates

no code implementations29 Jul 2022 Fangcheng Fu, Xupeng Miao, Jiawei Jiang, Huanran Xue, Bin Cui

Vertical federated learning (VFL) is an emerging paradigm that allows different parties (e. g., organizations or enterprises) to collaboratively build machine learning models with privacy protection.

Federated Learning

Efficient End-to-End AutoML via Scalable Search Space Decomposition

1 code implementation19 Jun 2022 Yang Li, Yu Shen, Wentao Zhang, Ce Zhang, Bin Cui

End-to-end AutoML has attracted intensive interests from both academia and industry which automatically searches for ML pipelines in a space induced by feature engineering, algorithm/model selection, and hyper-parameter tuning.

AutoML Feature Engineering +1

NAFS: A Simple yet Tough-to-beat Baseline for Graph Representation Learning

2 code implementations17 Jun 2022 Wentao Zhang, Zeang Sheng, Mingyu Yang, Yang Li, Yu Shen, Zhi Yang, Bin Cui

First, GNNs can learn higher-order structural information by stacking more layers but can not deal with large depth due to the over-smoothing issue.

Graph Representation Learning Link Prediction +1

BlindFL: Vertical Federated Machine Learning without Peeking into Your Data

no code implementations16 Jun 2022 Fangcheng Fu, Huanran Xue, Yong Cheng, Yangyu Tao, Bin Cui

First, to address the functionality of VFL models, we propose the federated source layers to unite the data from different parties.

BIG-bench Machine Learning Federated Learning

Model Degradation Hinders Deep Graph Neural Networks

1 code implementation9 Jun 2022 Wentao Zhang, Zeang Sheng, Ziqi Yin, Yuezihan Jiang, Yikuan Xia, Jun Gao, Zhi Yang, Bin Cui

Graph Neural Networks (GNNs) have achieved great success in various graph mining tasks. However, drastic performance degradation is always observed when a GNN is stacked with many layers.

Graph Mining

TransBO: Hyperparameter Optimization via Two-Phase Transfer Learning

no code implementations6 Jun 2022 Yang Li, Yu Shen, Huaijun Jiang, Wentao Zhang, Zhi Yang, Ce Zhang, Bin Cui

With the extensive applications of machine learning models, automatic hyperparameter optimization (HPO) has become increasingly important.

Hyperparameter Optimization Neural Architecture Search +2

Transfer Learning based Search Space Design for Hyperparameter Tuning

no code implementations6 Jun 2022 Yang Li, Yu Shen, Huaijun Jiang, Tianyi Bai, Wentao Zhang, Ce Zhang, Bin Cui

The extensive experiments show that our approach considerably boosts BO by designing a promising and compact search space instead of using the entire space, and outperforms the state-of-the-arts on a wide range of benchmarks, including machine learning and deep learning tuning tasks, and neural architecture search.

Bayesian Optimization BIG-bench Machine Learning +2

Instance-wise Prompt Tuning for Pretrained Language Models

no code implementations4 Jun 2022 Yuezihan Jiang, Hao Yang, Junyang Lin, Hanyu Zhao, An Yang, Chang Zhou, Hongxia Yang, Zhi Yang, Bin Cui

Prompt Learning has recently gained great popularity in bridging the gap between pretraining tasks and various downstream tasks.

AutoML for Deep Recommender Systems: A Survey

no code implementations25 Mar 2022 Ruiqi Zheng, Liang Qu, Bin Cui, Yuhui Shi, Hongzhi Yin

To tackle this problem, Automated Machine Learning (AutoML) is introduced to automatically search for the proper candidates for different parts of deep recommender systems.

AutoML feature selection +1

ZOOMER: Boosting Retrieval on Web-scale Graphs by Regions of Interest

1 code implementation20 Mar 2022 Yuezihan Jiang, Yu Cheng, Hanyu Zhao, Wentao Zhang, Xupeng Miao, Yu He, Liang Wang, Zhi Yang, Bin Cui

We introduce ZOOMER, a system deployed at Taobao, the largest e-commerce platform in China, for training and serving GNN-based recommendations over web-scale graphs.


Information Gain Propagation: a new way to Graph Active Learning with Soft Labels

1 code implementation ICLR 2022 Wentao Zhang, Yexin Wang, Zhenbang You, Meng Cao, Ping Huang, Jiulong Shan, Zhi Yang, Bin Cui

Graph Neural Networks (GNNs) have achieved great success in various tasks, but their performance highly relies on a large number of labeled nodes, which typically requires considerable human effort.

Active Learning

PaSca: a Graph Neural Architecture Search System under the Scalable Paradigm

1 code implementation1 Mar 2022 Wentao Zhang, Yu Shen, Zheyu Lin, Yang Li, Xiaosen Li, Wen Ouyang, Yangyu Tao, Zhi Yang, Bin Cui

Through deconstructing the message passing mechanism, PasCa presents a novel Scalable Graph Neural Architecture Paradigm (SGAP), together with a general architecture design space consisting of 150k different designs.

Neural Architecture Search

Hyper-Tune: Towards Efficient Hyper-parameter Tuning at Scale

no code implementations18 Jan 2022 Yang Li, Yu Shen, Huaijun Jiang, Wentao Zhang, Jixiang Li, Ji Liu, Ce Zhang, Bin Cui

The ever-growing demand and complexity of machine learning are putting pressure on hyper-parameter tuning systems: while the evaluation cost of models continues to increase, the scalability of state-of-the-arts starts to become a crucial bottleneck.


K-Core Decomposition on Super Large Graphs with Limited Resources

no code implementations26 Dec 2021 Shicheng Gao, Jie Xu, Xiaosen Li, Fangcheng Fu, Wentao Zhang, Wen Ouyang, Yangyu Tao, Bin Cui

For example, the distributed K-core decomposition algorithm can scale to a large graph with 136 billion edges without losing correctness with our divide-and-conquer technique.

PointCLIP: Point Cloud Understanding by CLIP

2 code implementations CVPR 2022 Renrui Zhang, Ziyu Guo, Wei zhang, Kunchang Li, Xupeng Miao, Bin Cui, Yu Qiao, Peng Gao, Hongsheng Li

On top of that, we design an inter-view adapter to better extract the global feature and adaptively fuse the few-shot knowledge learned from 3D into CLIP pre-trained in 2D.

Few-Shot Learning Open Vocabulary Object Detection +4

RIM: Reliable Influence-based Active Learning on Graphs

1 code implementation NeurIPS 2021 Wentao Zhang, Yexin Wang, Zhenbang You, Meng Cao, Ping Huang, Jiulong Shan, Zhi Yang, Bin Cui

Message passing is the core of most graph models such as Graph Convolutional Network (GCN) and Label Propagation (LP), which usually require a large number of clean labeled data to smooth out the neighborhood over the graph.

Active Learning

Cardinality Estimation in DBMS: A Comprehensive Benchmark Evaluation

1 code implementation13 Sep 2021 Yuxing Han, Ziniu Wu, Peizhi Wu, Rong Zhu, Jingyi Yang, Liang Wei Tan, Kai Zeng, Gao Cong, Yanzhao Qin, Andreas Pfadler, Zhengping Qian, Jingren Zhou, Jiangneng Li, Bin Cui

Therefore, we propose a new metric P-Error to evaluate the performance of CardEst methods, which overcomes the limitation of Q-Error and is able to reflect the overall end-to-end performance of CardEst methods.

Graph Attention MLP with Reliable Label Utilization

no code implementations23 Aug 2021 Wentao Zhang, Ziqi Yin, Zeang Sheng, Wen Ouyang, Xiaosen Li, Yangyu Tao, Zhi Yang, Bin Cui

Graph neural networks (GNNs) have recently achieved state-of-the-art performance in many graph-based applications.

Graph Attention

Evaluating Deep Graph Neural Networks

1 code implementation2 Aug 2021 Wentao Zhang, Zeang Sheng, Yuezihan Jiang, Yikuan Xia, Jun Gao, Zhi Yang, Bin Cui

Based on the experimental results, we answer the following two essential questions: (1) what actually leads to the compromised performance of deep GNNs; (2) when we need and how to build deep GNNs.

Graph Mining Node Classification

Grain: Improving Data Efficiency of Graph Neural Networks via Diversified Influence Maximization

1 code implementation31 Jul 2021 Wentao Zhang, Zhi Yang, Yexin Wang, Yu Shen, Yang Li, Liang Wang, Bin Cui

Data selection methods, such as active learning and core-set selection, are useful tools for improving the data efficiency of deep learning models on large-scale datasets.

Active Learning Knowledge Graphs

ROD: Reception-aware Online Distillation for Sparse Graphs

1 code implementation25 Jul 2021 Wentao Zhang, Yuezihan Jiang, Yang Li, Zeang Sheng, Yu Shen, Xupeng Miao, Liang Wang, Zhi Yang, Bin Cui

Unfortunately, many real-world networks are sparse in terms of both edges and labels, leading to sub-optimal performance of GNNs.

Clustering Graph Learning +5

VolcanoML: Speeding up End-to-End AutoML via Scalable Search Space Decomposition

3 code implementations19 Jul 2021 Yang Li, Yu Shen, Wentao Zhang, Jiawei Jiang, Bolin Ding, Yaliang Li, Jingren Zhou, Zhi Yang, Wentao Wu, Ce Zhang, Bin Cui

End-to-end AutoML has attracted intensive interests from both academia and industry, which automatically searches for ML pipelines in a space induced by feature engineering, algorithm/model selection, and hyper-parameter tuning.

AutoML Feature Engineering +1

OpenBox: A Generalized Black-box Optimization Service

6 code implementations1 Jun 2021 Yang Li, Yu Shen, Wentao Zhang, Yuanwei Chen, Huaijun Jiang, Mingchao Liu, Jiawei Jiang, Jinyang Gao, Wentao Wu, Zhi Yang, Ce Zhang, Bin Cui

Black-box optimization (BBO) has a broad range of applications, including automatic machine learning, engineering, physics, and experimental design.

Experimental Design Transfer Learning

CausCF: Causal Collaborative Filtering for RecommendationEffect Estimation

no code implementations28 May 2021 Xu Xie, Zhaoyang Liu, Shiwen Wu, Fei Sun, Cihang Liu, Jiawei Chen, Jinyang Gao, Bin Cui, Bolin Ding

It is based on the idea that similar users not only have a similar taste on items, but also have similar treatment effect under recommendations.

Collaborative Filtering Recommendation Systems

GMLP: Building Scalable and Flexible Graph Neural Networks with Feature-Message Passing

no code implementations20 Apr 2021 Wentao Zhang, Yu Shen, Zheyu Lin, Yang Li, Xiaosen Li, Wen Ouyang, Yangyu Tao, Zhi Yang, Bin Cui

In recent studies, neural message passing has proved to be an effective way to design graph neural networks (GNNs), which have achieved state-of-the-art performance in many graph-based tasks.

Explore User Neighborhood for Real-time E-commerce Recommendation

no code implementations28 Feb 2021 Xu Xie, Fei Sun, Xiaoyong Yang, Zhao Yang, Jinyang Gao, Wenwu Ou, Bin Cui

On the one hand, it utilizes UI relations and user neighborhood to capture both global and local information.

Collaborative Filtering Recommendation Systems

Efficient Automatic CASH via Rising Bandits

no code implementations8 Dec 2020 Yang Li, Jiawei Jiang, Jinyang Gao, Yingxia Shao, Ce Zhang, Bin Cui

In this framework, the BO methods are used to solve the HPO problem for each ML algorithm separately, incorporating a much smaller hyperparameter space for BO methods.

Bayesian Optimization BIG-bench Machine Learning +2

Efficient and Scalable Structure Learning for Bayesian Networks: Algorithms and Applications

no code implementations7 Dec 2020 Rong Zhu, Andreas Pfadler, Ziniu Wu, Yuxing Han, Xiaoke Yang, Feng Ye, Zhenping Qian, Jingren Zhou, Bin Cui

To resolve this, we propose a new structure learning algorithm LEAST, which comprehensively fulfills our business requirements as it attains high accuracy, efficiency and scalability at the same time.

Anomaly Detection Explainable Recommendation

MFES-HB: Efficient Hyperband with Multi-Fidelity Quality Measurements

5 code implementations5 Dec 2020 Yang Li, Yu Shen, Jiawei Jiang, Jinyang Gao, Ce Zhang, Bin Cui

Instead of sampling configurations randomly in HB, BOHB samples configurations based on a BO surrogate model, which is constructed with the high-fidelity measurements only.

Bayesian Optimization Hyperparameter Optimization

FLAT: Fast, Lightweight and Accurate Method for Cardinality Estimation

1 code implementation18 Nov 2020 Rong Zhu, Ziniu Wu, Yuxing Han, Kai Zeng, Andreas Pfadler, Zhengping Qian, Jingren Zhou, Bin Cui

Despite decades of research, existing methods either over simplify the models only using independent factorization which leads to inaccurate estimates, or over complicate them by lossless conditional factorization without any independent assumption which results in slow probability computation.

Graph Neural Networks in Recommender Systems: A Survey

1 code implementation4 Nov 2020 Shiwen Wu, Fei Sun, Wentao Zhang, Xu Xie, Bin Cui

With the explosive growth of online information, recommender systems play a key role to alleviate such information overload.

Graph Representation Learning Recommendation Systems

Contrastive Learning for Sequential Recommendation

1 code implementation27 Oct 2020 Xu Xie, Fei Sun, Zhaoyang Liu, Shiwen Wu, Jinyang Gao, Bolin Ding, Bin Cui

Sequential recommendation methods play a crucial role in modern recommender systems because of their ability to capture a user's dynamic interest from her/his historical interactions.

Contrastive Learning Data Augmentation +1

UniNet: Scalable Network Representation Learning with Metropolis-Hastings Sampling

1 code implementation10 Oct 2020 Xingyu Yao, Yingxia Shao, Bin Cui, Lei Chen

Finally, with the new edge sampler and random walk model abstraction, we carefully implement a scalable NRL framework called UniNet.

Representation Learning

DeGNN: Characterizing and Improving Graph Neural Networks with Graph Decomposition

no code implementations10 Oct 2019 Xupeng Miao, Nezihe Merve Gürel, Wentao Zhang, Zhichao Han, Bo Li, Wei Min, Xi Rao, Hansheng Ren, Yinan Shan, Yingxia Shao, Yujie Wang, Fan Wu, Hui Xue, Yaming Yang, Zitao Zhang, Yang Zhao, Shuai Zhang, Yujing Wang, Bin Cui, Ce Zhang

Despite the wide application of Graph Convolutional Network (GCN), one major limitation is that it does not benefit from the increasing depth and suffers from the oversmoothing problem.

X-Forest: Approximate Random Projection Trees for Similarity Measurement

1 code implementation25 Sep 2019 Yikai Zhao, Peiqing Chen, Zidong Zhao, Tong Yang, Jie Jiang, Bin Cui, Gong Zhang, Steve Uhlig

First, we introduced RP Trees into the tasks of similarity measurement such that accuracy is improved.

AHash: A Load-Balanced One Permutation Hash

1 code implementation25 Sep 2019 Chenxingyu Zhao, Jie Gui, Yixiao Guo, Jie Jiang, Tong Yang, Bin Cui, Gong Zhang

Unlike the densification to fill the empty bins after they undesirably occur, our design goal is to balance the load so as to reduce the empty bins in advance.

An Experimental Evaluation of Large Scale GBDT Systems

no code implementations3 Jul 2019 Fangcheng Fu, Jiawei Jiang, Yingxia Shao, Bin Cui

Gradient boosting decision tree (GBDT) is a widely-used machine learning algorithm in both data analytic competitions and real-world industrial applications.


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