Search Results for author: Jingren Zhou

Found 54 papers, 19 papers with code

Edge-Cloud Polarization and Collaboration: A Comprehensive Survey

no code implementations11 Nov 2021 Jiangchao Yao, Shengyu Zhang, Yang Yao, Feng Wang, Jianxin Ma, Jianwei Zhang, Yunfei Chu, Luo Ji, Kunyang Jia, Tao Shen, Anpeng Wu, Fengda Zhang, Ziqi Tan, Kun Kuang, Chao Wu, Fei Wu, Jingren Zhou, Hongxia Yang

However, edge computing, especially edge and cloud collaborative computing, are still in its infancy to announce their success due to the resource-constrained IoT scenarios with very limited algorithms deployed.


M6-10T: A Sharing-Delinking Paradigm for Efficient Multi-Trillion Parameter Pretraining

no code implementations8 Oct 2021 Junyang Lin, An Yang, Jinze Bai, Chang Zhou, Le Jiang, Xianyan Jia, Ang Wang, Jie Zhang, Yong Li, Wei Lin, Jingren Zhou, Hongxia Yang

Recent expeditious developments in deep learning algorithms, distributed training, and even hardware design for large models have enabled training extreme-scale models, say GPT-3 and Switch Transformer possessing hundreds of billions or even trillions of parameters.

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.

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

Reliable Adversarial Distillation with Unreliable Teachers

no code implementations9 Jun 2021 Jianing Zhu, Jiangchao Yao, Bo Han, Jingfeng Zhang, Tongliang Liu, Gang Niu, Jingren Zhou, Jianliang Xu, Hongxia Yang

However, when considering adversarial robustness, teachers may become unreliable and adversarial distillation may not work: teachers are pretrained on their own adversarial data, and it is too demanding to require that teachers are also good at every adversarial data queried by students.

Adversarial Robustness

Low-Rank Subspaces in GANs

1 code implementation NeurIPS 2021 Jiapeng Zhu, Ruili Feng, Yujun Shen, Deli Zhao, ZhengJun Zha, Jingren Zhou, Qifeng Chen

Concretely, given an arbitrary image and a region of interest (e. g., eyes of face images), we manage to relate the latent space to the image region with the Jacobian matrix and then use low-rank factorization to discover steerable latent subspaces.

Learning to Rehearse in Long Sequence Memorization

no code implementations2 Jun 2021 Zhu Zhang, Chang Zhou, Jianxin Ma, Zhijie Lin, Jingren Zhou, Hongxia Yang, Zhou Zhao

Further, we design a history sampler to select informative fragments for rehearsal training, making the memory focus on the crucial information.

Question Answering Video Question Answering

M6-T: Exploring Sparse Expert Models and Beyond

no code implementations31 May 2021 An Yang, Junyang Lin, Rui Men, Chang Zhou, Le Jiang, Xianyan Jia, Ang Wang, Jie Zhang, Jiamang Wang, Yong Li, Di Zhang, Wei Lin, Lin Qu, Jingren Zhou, Hongxia Yang

Mixture-of-Experts (MoE) models can achieve promising results with outrageous large amount of parameters but constant computation cost, and thus it has become a trend in model scaling.

Sketch and Refine: Towards Faithful and Informative Table-to-Text Generation

no code implementations Findings (ACL) 2021 Peng Wang, Junyang Lin, An Yang, Chang Zhou, Yichang Zhang, Jingren Zhou, Hongxia Yang

Experimental results demonstrate that our method outperforms the previous state-of-the-art methods in both automatic and human evaluation, especially on coverage and faithfulness.

Table-to-Text Generation

M6-UFC: Unifying Multi-Modal Controls for Conditional Image Synthesis

no code implementations NeurIPS 2021 Zhu Zhang, Jianxin Ma, Chang Zhou, Rui Men, Zhikang Li, Ming Ding, Jie Tang, Jingren Zhou, Hongxia Yang

Conditional image synthesis aims to create an image according to some multi-modal guidance in the forms of textual descriptions, reference images, and image blocks to preserve, as well as their combinations.

Image Generation

Linear-Time Self Attention with Codeword Histogram for Efficient Recommendation

1 code implementation28 May 2021 Yongji Wu, Defu Lian, Neil Zhenqiang Gong, Lu Yin, Mingyang Yin, Jingren Zhou, Hongxia Yang

Inspired by the idea of vector quantization that uses cluster centroids to approximate items, we propose LISA (LInear-time Self Attention), which enjoys both the effectiveness of vanilla self-attention and the efficiency of sparse attention.


Rethinking Lifelong Sequential Recommendation with Incremental Multi-Interest Attention

no code implementations28 May 2021 Yongji Wu, Lu Yin, Defu Lian, Mingyang Yin, Neil Zhenqiang Gong, Jingren Zhou, Hongxia Yang

With the rapid development of these services in the last two decades, users have accumulated a massive amount of behavior data.

Learning Relation Alignment for Calibrated Cross-modal Retrieval

1 code implementation ACL 2021 Shuhuai Ren, Junyang Lin, Guangxiang Zhao, Rui Men, An Yang, Jingren Zhou, Xu sun, Hongxia Yang

To bridge the semantic gap between the two modalities, previous studies mainly focus on word-region alignment at the object level, lacking the matching between the linguistic relation among the words and the visual relation among the regions.

Cross-Modal Retrieval

UFC-BERT: Unifying Multi-Modal Controls for Conditional Image Synthesis

no code implementations NeurIPS 2021 Zhu Zhang, Jianxin Ma, Chang Zhou, Rui Men, Zhikang Li, Ming Ding, Jie Tang, Jingren Zhou, Hongxia Yang

Conditional image synthesis aims to create an image according to some multi-modal guidance in the forms of textual descriptions, reference images, and image blocks to preserve, as well as their combinations.

Image Generation

TCL: Transformer-based Dynamic Graph Modelling via Contrastive Learning

no code implementations17 May 2021 Lu Wang, xiaofu Chang, Shuang Li, Yunfei Chu, Hui Li, Wei zhang, Xiaofeng He, Le Song, Jingren Zhou, Hongxia Yang

Secondly, on top of the proposed graph transformer, we introduce a two-stream encoder that separately extracts representations from temporal neighborhoods associated with the two interaction nodes and then utilizes a co-attentional transformer to model inter-dependencies at a semantic level.

Contrastive Learning Graph Learning +2

Contrastive Attraction and Contrastive Repulsion for Representation Learning

no code implementations8 May 2021 Huangjie Zheng, Xu Chen, Jiangchao Yao, Hongxia Yang, Chunyuan Li, Ya zhang, Hao Zhang, Ivor Tsang, Jingren Zhou, Mingyuan Zhou

Extensive large-scale experiments on standard vision tasks show that CACR not only consistently outperforms existing CL methods on benchmark datasets in representation learning, but also provides interpretable contrastive weights, demonstrating the efficacy of the proposed doubly contrastive strategy.

Contrastive Learning Representation Learning

A Unified Transferable Model for ML-Enhanced DBMS

no code implementations6 May 2021 Ziniu Wu, Peilun Yang, Pei Yu, Rong Zhu, Yuxing Han, Yaliang Li, Defu Lian, Kai Zeng, Jingren Zhou

We propose to explore the transferabilities of the ML methods both across tasks and across DBs to tackle these fundamental drawbacks.

Device-Cloud Collaborative Learning for Recommendation

no code implementations14 Apr 2021 Jiangchao Yao, Feng Wang, Kunyang Jia, Bo Han, Jingren Zhou, Hongxia Yang

With the rapid development of storage and computing power on mobile devices, it becomes critical and popular to deploy models on devices to save onerous communication latencies and to capture real-time features.

M6: A Chinese Multimodal Pretrainer

no code implementations1 Mar 2021 Junyang Lin, Rui Men, An Yang, Chang Zhou, Ming Ding, Yichang Zhang, Peng Wang, Ang Wang, Le Jiang, Xianyan Jia, Jie Zhang, Jianwei Zhang, Xu Zou, Zhikang Li, Xiaodong Deng, Jie Liu, Jinbao Xue, Huiling Zhou, Jianxin Ma, Jin Yu, Yong Li, Wei Lin, Jingren Zhou, Jie Tang, Hongxia Yang

In this work, we construct the largest dataset for multimodal pretraining in Chinese, which consists of over 1. 9TB images and 292GB texts that cover a wide range of domains.

Image Generation

Dynamic Memory based Attention Network for Sequential Recommendation

1 code implementation18 Feb 2021 Qiaoyu Tan, Jianwei Zhang, Ninghao Liu, Xiao Huang, Hongxia Yang, Jingren Zhou, Xia Hu

It segments the overall long behavior sequence into a series of sub-sequences, then trains the model and maintains a set of memory blocks to preserve long-term interests of users.

Sparse-Interest Network for Sequential Recommendation

no code implementations18 Feb 2021 Qiaoyu Tan, Jianwei Zhang, Jiangchao Yao, Ninghao Liu, Jingren Zhou, Hongxia Yang, Xia Hu

Our sparse-interest module can adaptively infer a sparse set of concepts for each user from the large concept pool and output multiple embeddings accordingly.

Inductive Granger Causal Modeling for Multivariate Time Series

no code implementations10 Feb 2021 Yunfei Chu, Xiaowei Wang, Jianxin Ma, Kunyang Jia, Jingren Zhou, Hongxia Yang

To bridge this gap, we propose an Inductive GRanger cAusal modeling (InGRA) framework for inductive Granger causality learning and common causal structure detection on multivariate time series, which exploits the shared commonalities underlying the different individuals.

Time Series

FlashP: An Analytical Pipeline for Real-time Forecasting of Time-Series Relational Data

no code implementations9 Jan 2021 Shuyuan Yan, Bolin Ding, Wei Guo, Jingren Zhou, Zhewei Wei, Xiaowei Jiang, Sheng Xu

Our scalable real-time forecasting system FlashP (Flash Prediction) is built based on this idea, with two major challenges to be resolved in this paper: first, we need to figure out how approximate aggregations affect the fitting of forecasting models, and forecasting results; and second, accordingly, what sampling algorithms we should use to obtain these approximate aggregations and how large the samples are.

Time Series

A Pluggable Learned Index Method via Sampling and Gap Insertion

no code implementations4 Jan 2021 Yaliang Li, Daoyuan Chen, Bolin Ding, Kai Zeng, Jingren Zhou

In this paper, we propose a formal machine learning based framework to quantify the index learning objective, and study two general and pluggable techniques to enhance the learning efficiency and learning effectiveness for learned indexes.

Local Clustering Graph Neural Networks

no code implementations1 Jan 2021 Jiezhong Qiu, Yukuo Cen, Qibin Chen, Chang Zhou, Jingren Zhou, Hongxia Yang, Jie Tang

Based on the theoretical analysis, we propose Local Clustering Graph Neural Networks (LCGNN), a GNN learning paradigm that utilizes local clustering to efficiently search for small but compact subgraphs for GNN training and inference.

Continual Memory: Can We Reason After Long-Term Memorization?

no code implementations1 Jan 2021 Zhu Zhang, Chang Zhou, Zhou Zhao, Zhijie Lin, Jingren Zhou, Hongxia Yang

Existing reasoning tasks often follow the setting of "reasoning while experiencing", which has an important assumption that the raw contents can be always accessed while reasoning.

BayesCard: Revitilizing Bayesian Frameworks for Cardinality Estimation

1 code implementation29 Dec 2020 Ziniu Wu, Amir Shaikhha, Rong Zhu, Kai Zeng, Yuxing Han, Jingren Zhou

Recently proposed deep learning based methods largely improve the estimation accuracy but their performance can be greatly affected by data and often difficult for system deployment.

Probabilistic Programming

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

Learning to Mutate with Hypergradient Guided Population

no code implementations NeurIPS 2020 Zhiqiang Tao, Yaliang Li, Bolin Ding, Ce Zhang, Jingren Zhou, Yun Fu

Computing the gradient of model hyperparameters, i. e., hypergradient, enables a promising and natural way to solve the hyperparameter optimization task.

Hyperparameter Optimization

FSPN: A New Class of Probabilistic Graphical Model

no code implementations18 Nov 2020 Ziniu Wu, Rong Zhu, Andreas Pfadler, Yuxing Han, Jiangneng Li, Zhengping Qian, Kai Zeng, Jingren Zhou

We introduce factorize sum split product networks (FSPNs), a new class of probabilistic graphical models (PGMs).

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.

MicroRec: Efficient Recommendation Inference by Hardware and Data Structure Solutions

no code implementations12 Oct 2020 Wenqi Jiang, Zhenhao He, Shuai Zhang, Thomas B. Preußer, Kai Zeng, Liang Feng, Jiansong Zhang, Tongxuan Liu, Yong Li, Jingren Zhou, Ce Zhang, Gustavo Alonso

MicroRec accelerates recommendation inference by (1) redesigning the data structures involved in the embeddings to reduce the number of lookups needed and (2) taking advantage of the availability of High-Bandwidth Memory (HBM) in FPGA accelerators to tackle the latency by enabling parallel lookups.

Recommendation Systems

Poet: Product-oriented Video Captioner for E-commerce

1 code implementation16 Aug 2020 Shengyu Zhang, Ziqi Tan, Jin Yu, Zhou Zhao, Kun Kuang, Jie Liu, Jingren Zhou, Hongxia Yang, Fei Wu

Then, based on the aspects of the video-associated product, we perform knowledge-enhanced spatial-temporal inference on those graphs for capturing the dynamic change of fine-grained product-part characteristics.

Video Captioning

FIVES: Feature Interaction Via Edge Search for Large-Scale Tabular Data

no code implementations29 Jul 2020 Yuexiang Xie, Zhen Wang, Yaliang Li, Bolin Ding, Nezihe Merve Gürel, Ce Zhang, Minlie Huang, Wei. Lin, Jingren Zhou

Then we instantiate this search strategy by optimizing both a dedicated graph neural network (GNN) and the adjacency tensor associated with the defined feature graph.

Recommendation Systems

Comprehensive Information Integration Modeling Framework for Video Titling

1 code implementation24 Jun 2020 Shengyu Zhang, Ziqi Tan, Jin Yu, Zhou Zhao, Kun Kuang, Tan Jiang, Jingren Zhou, Hongxia Yang, Fei Wu

In e-commerce, consumer-generated videos, which in general deliver consumers' individual preferences for the different aspects of certain products, are massive in volume.

Video Captioning

Contrastive Learning for Debiased Candidate Generation in Large-Scale Recommender Systems

no code implementations20 May 2020 Chang Zhou, Jianxin Ma, Jianwei Zhang, Jingren Zhou, Hongxia Yang

Deep candidate generation (DCG) that narrows down the collection of relevant items from billions to hundreds via representation learning has become prevalent in industrial recommender systems.

Contrastive Learning Fairness +3

Understanding Negative Sampling in Graph Representation Learning

2 code implementations20 May 2020 Zhen Yang, Ming Ding, Chang Zhou, Hongxia Yang, Jingren Zhou, Jie Tang

To the best of our knowledge, we are the first to derive the theory and quantify that the negative sampling distribution should be positively but sub-linearly correlated to their positive sampling distribution.

Graph Learning Graph Representation Learning +2

Learning Efficient Parameter Server Synchronization Policies for Distributed SGD

no code implementations ICLR 2020 Rong Zhu, Sheng Yang, Andreas Pfadler, Zhengping Qian, Jingren Zhou

We apply a reinforcement learning (RL) based approach to learning optimal synchronization policies used for Parameter Server-based distributed training of machine learning models with Stochastic Gradient Descent (SGD).


Taming the Expressiveness and Programmability of Graph Analytical Queries

no code implementations20 Apr 2020 Lu Qin, Longbin Lai, Kongzhang Hao, Zhongxin Zhou, Yiwei Zhao, Yuxing Han, Xuemin Lin, Zhengping Qian, Jingren Zhou

Graph database has enjoyed a boom in the last decade, and graph queries accordingly gain a lot of attentions from both the academia and industry.

Code Generation

InterBERT: Vision-and-Language Interaction for Multi-modal Pretraining

no code implementations30 Mar 2020 Junyang Lin, An Yang, Yichang Zhang, Jie Liu, Jingren Zhou, Hongxia Yang

We pretrain the model with three pretraining tasks, including masked segment modeling (MSM), masked region modeling (MRM) and image-text matching (ITM); and finetune the model on a series of vision-and-language downstream tasks.

Image Retrieval Text Matching +1

Learning to Hash with Graph Neural Networks for Recommender Systems

no code implementations4 Mar 2020 Qiaoyu Tan, Ninghao Liu, Xing Zhao, Hongxia Yang, Jingren Zhou, Xia Hu

In this work, we investigate the problem of hashing with graph neural networks (GNNs) for high quality retrieval, and propose a simple yet effective discrete representation learning framework to jointly learn continuous and discrete codes.

Graph Representation Learning Recommendation Systems

AdaBERT: Task-Adaptive BERT Compression with Differentiable Neural Architecture Search

1 code implementation13 Jan 2020 Daoyuan Chen, Yaliang Li, Minghui Qiu, Zhen Wang, Bofang Li, Bolin Ding, Hongbo Deng, Jun Huang, Wei. Lin, Jingren Zhou

Motivated by the necessity and benefits of task-oriented BERT compression, we propose a novel compression method, AdaBERT, that leverages differentiable Neural Architecture Search to automatically compress BERT into task-adaptive small models for specific tasks.

Knowledge Distillation Neural Architecture Search

Granger Causal Structure Reconstruction from Heterogeneous Multivariate Time Series

no code implementations25 Sep 2019 Yunfei Chu, Xiaowei Wang, Chunyan Feng, Jianxin Ma, Jingren Zhou, Hongxia Yang

Granger causal structure reconstruction is an emerging topic that can uncover causal relationship behind multivariate time series data.

Time Series

Improving Utility and Security of the Shuffler-based Differential Privacy

1 code implementation30 Aug 2019 Tianhao Wang, Bolin Ding, Min Xu, Zhicong Huang, Cheng Hong, Jingren Zhou, Ninghui Li, Somesh Jha

When collecting information, local differential privacy (LDP) alleviates privacy concerns of users because their private information is randomized before being sent it to the central aggregator.

Bayes EMbedding (BEM): Refining Representation by Integrating Knowledge Graphs and Behavior-specific Networks

1 code implementation28 Aug 2019 Yuting Ye, Xuwu Wang, Jiangchao Yao, Kunyang Jia, Jingren Zhou, Yanghua Xiao, Hongxia Yang

Low-dimensional embeddings of knowledge graphs and behavior graphs have proved remarkably powerful in varieties of tasks, from predicting unobserved edges between entities to content recommendation.

General Classification Knowledge Graph Embedding +3

A Minimax Game for Instance based Selective Transfer Learning

no code implementations1 Jul 2019 Bo wang, Minghui Qiu, Xisen Wang, Yaliang Li, Yu Gong, Xiaoyi Zeng, Jung Huang, Bo Zheng, Deng Cai, Jingren Zhou

To the best of our knowledge, this is the first to build a minimax game based model for selective transfer learning.

Transfer Learning

A Survey and Experimental Analysis of Distributed Subgraph Matching

1 code implementation27 Jun 2019 Longbin Lai, Zhu Qing, Zhengyi Yang, Xin Jin, Zhengmin Lai, Ran Wang, Kongzhang Hao, Xuemin Lin, Lu Qin, Wenjie Zhang, Ying Zhang, Zhengping Qian, Jingren Zhou

We conduct extensive experiments for both unlabelled matching and labelled matching to analyze the performance of distributed subgraph matching under various settings, which is finally summarized as a practical guide.


Sequential Scenario-Specific Meta Learner for Online Recommendation

1 code implementation2 Jun 2019 Zhengxiao Du, Xiaowei Wang, Hongxia Yang, Jingren Zhou, Jie Tang

Our approach is based on the insight that having a good generalization from a few examples relies on both a generic model initialization and an effective strategy for adapting this model to newly arising tasks.

Few-Shot Learning

Is a Single Vector Enough? Exploring Node Polysemy for Network Embedding

1 code implementation25 May 2019 Ninghao Liu, Qiaoyu Tan, Yuening Li, Hongxia Yang, Jingren Zhou, Xia Hu

Network embedding models are powerful tools in mapping nodes in a network into continuous vector-space representations in order to facilitate subsequent tasks such as classification and link prediction.

General Classification Language Modelling +3

Towards Knowledge-Based Personalized Product Description Generation in E-commerce

4 code implementations29 Mar 2019 Qibin Chen, Junyang Lin, Yichang Zhang, Hongxia Yang, Jingren Zhou, Jie Tang

In order to make the description both informative and personalized, KOBE considers a variety of important factors during text generation, including product aspects, user categories, and knowledge base, etc.

Text Generation

AliGraph: A Comprehensive Graph Neural Network Platform

no code implementations23 Feb 2019 Rong Zhu, Kun Zhao, Hongxia Yang, Wei. Lin, Chang Zhou, Baole Ai, Yong Li, Jingren Zhou

An increasing number of machine learning tasks require dealing with large graph datasets, which capture rich and complex relationship among potentially billions of elements.

Distributed, Parallel, and Cluster Computing

PANDA: Facilitating Usable AI Development

no code implementations26 Apr 2018 Jinyang Gao, Wei Wang, Meihui Zhang, Gang Chen, H. V. Jagadish, Guoliang Li, Teck Khim Ng, Beng Chin Ooi, Sheng Wang, Jingren Zhou

In many complex applications such as healthcare, subject matter experts (e. g. Clinicians) are the ones who appreciate the importance of features that affect health, and their knowledge together with existing knowledge bases are critical to the end results.

Autonomous Driving

Large-scale L-BFGS using MapReduce

no code implementations NeurIPS 2014 Weizhu Chen, Zhenghao Wang, Jingren Zhou

L-BFGS has been applied as an effective parameter estimation method for various machine learning algorithms since 1980s.

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