Search Results for author: Xiaowei Wang

Found 13 papers, 3 papers with code

Graph Contrastive Learning with Generative Adversarial Network

no code implementations1 Aug 2023 Cheng Wu, Chaokun Wang, Jingcao Xu, Ziyang Liu, Kai Zheng, Xiaowei Wang, Yang song, Kun Gai

Specifically, we present GACN, a novel Generative Adversarial Contrastive learning Network for graph representation learning.

Contrastive Learning Data Augmentation +2

Multi-behavior Self-supervised Learning for Recommendation

1 code implementation22 May 2023 Jingcao Xu, Chaokun Wang, Cheng Wu, Yang song, Kai Zheng, Xiaowei Wang, Changping Wang, Guorui Zhou, Kun Gai

Secondly, existing methods utilizing self-supervised learning (SSL) to tackle the data sparsity neglect the serious optimization imbalance between the SSL task and the target task.

Recommendation Systems Self-Supervised Learning

Instant Representation Learning for Recommendation over Large Dynamic Graphs

1 code implementation22 May 2023 Cheng Wu, Chaokun Wang, Jingcao Xu, Ziwei Fang, Tiankai Gu, Changping Wang, Yang song, Kai Zheng, Xiaowei Wang, Guorui Zhou

Furthermore, the Neighborhood Disturbance existing in dynamic graphs deteriorates the performance of neighbor-aggregation based graph models.

Recommendation Systems Representation Learning

Delayed Rewards Calibration via Reward Empirical Sufficiency

no code implementations21 Feb 2021 Yixuan Liu, Hu Wang, Xiaowei Wang, Xiaoyue Sun, Liuyue Jiang, Minhui Xue

Therefore, a purify-trained classifier is designed to obtain the distribution and generate the calibrated rewards.

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 Time Series Analysis

Some estimates of the generalized beukers integral with techniques of partial fraction decomposition

no code implementations27 Jan 2021 Xiaowei Wang

In the second section of this paper, we provide some estimates of the upper and lower bound of the value $J_{3}$, which involves the generalized Beukers integral and is related to $\zeta(5)$.

Number Theory 11J72, 11J82, 11M06

Variational Autoencoders for Highly Multivariate Spatial Point Processes Intensities

no code implementations ICLR 2020 Baichuan Yuan, Xiaowei Wang, Jianxin Ma, Chang Zhou, Andrea L. Bertozzi, Hongxia Yang

To bridge this gap, we introduce a declustering based hidden variable model that leads to an efficient inference procedure via a variational autoencoder (VAE).

Collaborative Filtering Point Processes +1

Active Lighting Recurrence by Parallel Lighting Analogy for Fine-Grained Change Detection

no code implementations22 Feb 2020 Qian Zhang, Wei Feng, Liang Wan, Fei-Peng Tian, Xiaowei Wang, Ping Tan

Besides, we also theoretically prove the invariance of our ALR approach to the ambiguity of normal and lighting decomposition.

Change Detection Navigate

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 Time Series Analysis

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

Neural Cache: Bit-Serial In-Cache Acceleration of Deep Neural Networks

no code implementations9 May 2018 Charles Eckert, Xiaowei Wang, Jingcheng Wang, Arun Subramaniyan, Ravi Iyer, Dennis Sylvester, David Blaauw, Reetuparna Das

This paper presents the Neural Cache architecture, which re-purposes cache structures to transform them into massively parallel compute units capable of running inferences for Deep Neural Networks.


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