no code implementations • 26 May 2024 • Peiwen Li, Xin Wang, Zeyang Zhang, Yijian Qin, Ziwei Zhang, Jialong Wang, Yang Li, Wenwu Zhu
We propose to handle the distribution shifts in the graph architecture search process by discovering and exploiting the causal relationship between graphs and architectures to search for the optimal architectures that can generalize under distribution shifts.
no code implementations • 21 May 2024 • Hong Chen, Xin Wang, YiPeng Zhang, Yuwei Zhou, Zeyang Zhang, Siao Tang, Wenwu Zhu
To tackle the problems, in this paper, we propose DisenStudio, a novel framework that can generate text-guided videos for customized multiple subjects, given few images for each subject.
no code implementations • 15 Apr 2024 • Haojun Sun, Chen Tang, Zhi Wang, Yuan Meng, Jingyan Jiang, Xinzhu Ma, Wenwu Zhu
Diffusion models have emerged as preeminent contenders in the realm of generative models.
no code implementations • 21 Mar 2024 • Yang Yao, Xin Wang, Zeyang Zhang, Yijian Qin, Ziwei Zhang, Xu Chu, Yuekui Yang, Wenwu Zhu, Hong Mei
In this paper, we propose LLM4GraphGen to explore the ability of LLMs for graph generation with systematical task designs and extensive experiments.
1 code implementation • NeurIPS 2023 • Zeyang Zhang, Xin Wang, Ziwei Zhang, Zhou Qin, Weigao Wen, Hui Xue, Haoyang Li, Wenwu Zhu
In this paper, we discover that there exist cases with distribution shifts unobservable in the time domain while observable in the spectral domain, and propose to study distribution shifts on dynamic graphs in the spectral domain for the first time.
no code implementations • NeurIPS 2023 • Zeyang Zhang, Xin Wang, Ziwei Zhang, Guangyao Shen, Shiqi Shen, Wenwu Zhu
To address the challenge, we propose a novel Disentangled Self-supervised Graph Neural Architecture Search (DSGAS) model, which is able to discover the optimal architectures capturing various latent graph factors in a self-supervised fashion based on unlabeled graph data.
1 code implementation • 14 Jan 2024 • Zhihao Yu, Xu Chu, Liantao Ma, Yasha Wang, Wenwu Zhu
To bridge this gap, we propose PRIME, a Prototype Recurrent Imputation ModEl, which integrates both intra-series and inter-series information for imputing missing values in irregularly sampled time series.
no code implementations • CVPR 2024 • Chen Tang, Yuan Meng, Jiacheng Jiang, Shuzhao Xie, Rongwei Lu, Xinzhu Ma, Zhi Wang, Wenwu Zhu
Conversely, mixed-precision quantization (MPQ) is advocated to compress the model effectively by allocating heterogeneous bit-width for layers.
no code implementations • 28 Dec 2023 • Houlun Chen, Xin Wang, Hong Chen, Zihan Song, Jia Jia, Wenwu Zhu
To tackle these challenges, in this work we propose a Grounding-Prompter method, which is capable of conducting TSG in long videos through prompting LLM with multimodal information.
no code implementations • 21 Dec 2023 • Wei Feng, Xin Wang, Hong Chen, Zeyang Zhang, Zihan Song, Yuwei Zhou, Wenwu Zhu
Recently, researchers have attempted to investigate the capability of LLMs in handling videos and proposed several video LLM models.
1 code implementation • CVPR 2024 • Bin Huang, Xin Wang, Hong Chen, Zihan Song, Wenwu Zhu
Large language models (LLMs) have shown remarkable text understanding capabilities, which have been extended as Video LLMs to handle video data for comprehending visual details.
Dense Video Captioning Video-based Generative Performance Benchmarking (Consistency) +5
no code implementations • 27 Nov 2023 • Zeyang Zhang, Xingwang Li, Fei Teng, Ning Lin, Xueling Zhu, Xin Wang, Wenwu Zhu
We first model human albumin prediction as a dynamic graph regression problem to model the dynamics and patient relationship.
no code implementations • 24 Nov 2023 • Zeyang Zhang, Xin Wang, Ziwei Zhang, Haoyang Li, Wenwu Zhu
In this paper, we propose Disentangled Intervention-based Dynamic graph Attention networks with Invariance Promotion (I-DIDA) to handle spatio-temporal distribution shifts in dynamic graphs by discovering and utilizing invariant patterns, i. e., structures and features whose predictive abilities are stable across distribution shifts.
no code implementations • 10 Nov 2023 • Siao Tang, Xin Wang, Hong Chen, Chaoyu Guan, Zewen Wu, Yansong Tang, Wenwu Zhu
In this paper, we propose a novel post-training quantization method PCR (Progressive Calibration and Relaxing) for text-to-image diffusion models, which consists of a progressive calibration strategy that considers the accumulated quantization error across timesteps, and an activation relaxing strategy that improves the performance with negligible cost.
no code implementations • 8 Nov 2023 • Siao Tang, Xin Wang, Hong Chen, Chaoyu Guan, Yansong Tang, Wenwu Zhu
When retraining the searched architecture, we adopt a dynamic joint loss to maintain the consistency between supernet training and subnet retraining, which also provides informative objectives for each block and shortens the paths of gradient propagation.
no code implementations • 2 Nov 2023 • Hong Chen, Xin Wang, Guanning Zeng, YiPeng Zhang, Yuwei Zhou, Feilin Han, Wenwu Zhu
The video generator is further customized for the given multiple subjects by the proposed Disen-Mix Finetuning and Human-in-the-Loop Re-finetuning strategy, which can tackle the attribute binding problem of multi-subject generation.
no code implementations • 27 Oct 2023 • Yijian Qin, Xin Wang, Ziwei Zhang, Wenwu Zhu
Text-attributed graphs (TAGs) are prevalent on the web and research over TAGs such as citation networks, e-commerce networks and social networks has attracted considerable attention in the web community.
no code implementations • 26 Oct 2023 • Zeyang Zhang, Xin Wang, Ziwei Zhang, Haoyang Li, Yijian Qin, Wenwu Zhu
Our main observations are: 1) LLMs have preliminary spatial-temporal understanding abilities on dynamic graphs, 2) Dynamic graph tasks show increasing difficulties for LLMs as the graph size and density increase, while not sensitive to the time span and data generation mechanism, 3) the proposed DST2 prompting method can help to improve LLMs' spatial-temporal understanding abilities on dynamic graphs for most tasks.
1 code implementation • 28 Aug 2023 • Ziwei Zhang, Haoyang Li, Zeyang Zhang, Yijian Qin, Xin Wang, Wenwu Zhu
In order to promote applying large models for graphs forward, we present a perspective paper to discuss the challenges and opportunities associated with developing large graph models.
1 code implementation • 5 May 2023 • Hong Chen, YiPeng Zhang, Simin Wu, Xin Wang, Xuguang Duan, Yuwei Zhou, Wenwu Zhu
To tackle the problems, we propose DisenBooth, an identity-preserving disentangled tuning framework for subject-driven text-to-image generation.
no code implementations • 3 May 2023 • Chen Zhu, Liang Du, Hong Chen, Shuang Zhao, Zixun Sun, Xin Wang, Wenwu Zhu
To tackle this problem, inspired by the Global Workspace Theory in conscious processing, which posits that only a specific subset of the product features are pertinent while the rest can be noisy and even detrimental to human-click behaviors, we propose a CTR model that enables Dynamic Embedding Learning with Truncated Conscious Attention for CTR prediction, termed DELTA.
no code implementations • CVPR 2023 • Beini Xie, Heng Chang, Ziwei Zhang, Xin Wang, Daixin Wang, Zhiqiang Zhang, Rex Ying, Wenwu Zhu
To tackle these challenges, we propose a novel Robust Neural Architecture search framework for GNNs (G-RNA).
no code implementations • 14 Feb 2023 • Chen Tang, Kai Ouyang, Zenghao Chai, Yunpeng Bai, Yuan Meng, Zhi Wang, Wenwu Zhu
This general and dataset-independent property makes us search for the MPQ policy over a rather small-scale proxy dataset and then the policy can be directly used to quantize the model trained on a large-scale dataset.
no code implementations • 6 Feb 2023 • Haoyang Li, Xin Wang, Wenwu Zhu
To the best of our knowledge, this paper is the first survey for curriculum graph machine learning.
no code implementations • CVPR 2023 • Lianzhe Wang, Shiji Zhou, Shanghang Zhang, Xu Chu, Heng Chang, Wenwu Zhu
Despite the broad interest in meta-learning, the generalization problem remains one of the significant challenges in this field.
no code implementations • ICCV 2023 • Zi Qian, Xin Wang, Xuguang Duan, Pengda Qin, Yuhong Li, Wenwu Zhu
Based on our formulation, we further propose MulTi-Modal PRompt LearnIng with DecouPLing bEfore InTeraction (TRIPLET), a novel approach that builds on a pre-trained vision-language model and consists of decoupled prompts and prompt interaction strategies to capture the complex interactions between modalities.
no code implementations • 21 Nov 2022 • Xin Wang, Hong Chen, Si'ao Tang, Zihao Wu, Wenwu Zhu
Disentangled Representation Learning (DRL) aims to learn a model capable of identifying and disentangling the underlying factors hidden in the observable data in representation form.
1 code implementation • 28 Oct 2022 • Yujie Jin, Xu Chu, Yasha Wang, Wenwu Zhu
Based on the proposed term of invariance, we propose a novel deep DG method called Angular Invariance Domain Generalization Network (AIDGN).
no code implementations • 31 Aug 2022 • Dustin Carrión-Ojeda, Hong Chen, Adrian El Baz, Sergio Escalera, Chaoyu Guan, Isabelle Guyon, Ihsan Ullah, Xin Wang, Wenwu Zhu
We present the design and baseline results for a new challenge in the ChaLearn meta-learning series, accepted at NeurIPS'22, focusing on "cross-domain" meta-learning.
no code implementations • 13 Aug 2022 • Xin Wang, Heng Chang, Beini Xie, Tian Bian, Shiji Zhou, Daixin Wang, Zhiqiang Zhang, Wenwu Zhu
Graph neural networks (GNNs) have achieved tremendous success in the task of graph classification and its diverse downstream real-world applications.
1 code implementation • 18 Jun 2022 • Yijian Qin, Ziwei Zhang, Xin Wang, Zeyang Zhang, Wenwu Zhu
To the best of our knowledge, our work is the first benchmark for graph neural architecture search.
no code implementations • 15 Jun 2022 • Adrian El Baz, Ihsan Ullah, Edesio Alcobaça, André C. P. L. F. Carvalho, Hong Chen, Fabio Ferreira, Henry Gouk, Chaoyu Guan, Isabelle Guyon, Timothy Hospedales, Shell Hu, Mike Huisman, Frank Hutter, Zhengying Liu, Felix Mohr, Ekrem Öztürk, Jan N. van Rijn, Haozhe Sun, Xin Wang, Wenwu Zhu
Although deep neural networks are capable of achieving performance superior to humans on various tasks, they are notorious for requiring large amounts of data and computing resources, restricting their success to domains where such resources are available.
1 code implementation • 15 Jun 2022 • Sheng Zhou, Hongjia Xu, Zhuonan Zheng, Jiawei Chen, Zhao Li, Jiajun Bu, Jia Wu, Xin Wang, Wenwu Zhu, Martin Ester
Motivated by the tremendous success of deep learning in clustering, one of the most fundamental machine learning tasks, and the large number of recent advances in this direction, in this paper we conduct a comprehensive survey on deep clustering by proposing a new taxonomy of different state-of-the-art approaches.
no code implementations • 21 Apr 2022 • Chen Tang, Haoyu Zhai, Kai Ouyang, Zhi Wang, Yifei Zhu, Wenwu Zhu
We propose to feed different data samples with varying quantization schemes to achieve a data-dependent dynamic inference, at a fine-grained layer level.
1 code implementation • 7 Apr 2022 • Zeyang Zhang, Ziwei Zhang, Xin Wang, Wenwu Zhu
To solve these challenges, we first propose a principled hardness measurement to quantify the hardness of TSP instances.
1 code implementation • 16 Mar 2022 • Chen Tang, Kai Ouyang, Zhi Wang, Yifei Zhu, YaoWei Wang, Wen Ji, Wenwu Zhu
For example, MPQ search on ResNet18 with our indicators takes only 0. 06 s, which improves time efficiency exponentially compared to iterative search methods.
no code implementations • 10 Mar 2022 • Xiaohan Lan, Yitian Yuan, Xin Wang, Long Chen, Zhi Wang, Lin Ma, Wenwu Zhu
New benchmarking results indicate that our proposed evaluation protocols can better monitor the research progress.
no code implementations • 24 Feb 2022 • YuAn Wang, Wei Zhuo, Yucong Li, Zhi Wang, Qi Ju, Wenwu Zhu
To solve this problem, we proposed a bootstrapped training scheme for semantic segmentation, which fully leveraged the global semantic knowledge for self-supervision with our proposed PGG strategy and CAE module.
Ranked #15 on Unsupervised Semantic Segmentation on COCO-Stuff-27
1 code implementation • 16 Feb 2022 • Haoyang Li, Xin Wang, Ziwei Zhang, Wenwu Zhu
This paper is the first systematic and comprehensive review of OOD generalization on graphs, to the best of our knowledge.
1 code implementation • 4 Jan 2022 • Xin Wang, Ziwei Zhang, Haoyang Li, Wenwu Zhu
However, as the literature on graph learning booms with a vast number of emerging methods and techniques, it becomes increasingly difficult to manually design the optimal machine learning algorithm for different graph-related tasks.
no code implementations • 4 Jan 2022 • Wenwu Zhu, Xin Wang, Pengtao Xie
Inspired by the concept of self-directed human learning, we introduce the principal concept of Self-directed Machine Learning (SDML) and propose a framework for SDML.
no code implementations • 23 Dec 2021 • Ziwei Zhang, Xin Wang, Zeyang Zhang, Peng Cui, Wenwu Zhu
Based on the experimental results, we advocate that TinvNN should be considered a new starting point and an essential baseline for further studies of transformation-invariant geometric deep learning.
no code implementations • 7 Dec 2021 • Haoyang Li, Xin Wang, Ziwei Zhang, Wenwu Zhu
Our proposed OOD-GNN employs a novel nonlinear graph representation decorrelation method utilizing random Fourier features, which encourages the model to eliminate the statistical dependence between relevant and irrelevant graph representations through iteratively optimizing the sample graph weights and graph encoder.
1 code implementation • 2 Dec 2021 • Yitian Yuan, Lin Ma, Wenwu Zhu
Enhancing the diversity of sentences to describe video contents is an important problem arising in recent video captioning research.
1 code implementation • 2 Dec 2021 • Yitian Yuan, Lin Ma, Jingwen Wang, Wenwu Zhu
In this paper, we investigate a novel and challenging task, namely controllable video captioning with an exemplar sentence.
no code implementations • NeurIPS 2021 • Jiyan Jiang, Wenpeng Zhang, Jinjie Gu, Wenwu Zhu
To overcome this problem, we study decentralized online learning in the asynchronous setting, which allows different learners to work at their own pace.
no code implementations • NeurIPS 2021 • Yijian Qin, Xin Wang, Zeyang Zhang, Wenwu Zhu
Extensive experiments on real-world graph datasets demonstrate that our proposed GASSO model is able to achieve state-of-the-art performance compared with existing baselines.
1 code implementation • NeurIPS 2021 • Hong Chen, Yudong Chen, Xin Wang, Ruobing Xie, Rui Wang, Feng Xia, Wenwu Zhu
However, learning such disentangled representations from multi-feedback data is challenging because i) multi-feedback is complex: there exist complex relations among different types of feedback (e. g., click, unclick, and dislike, etc) as well as various user intentions, and ii) multi-feedback is noisy: there exists noisy (useless) information both in features and labels, which may deteriorate the recommendation performance.
no code implementations • NeurIPS 2021 • Haoyang Li, Xin Wang, Ziwei Zhang, Zehuan Yuan, Hang Li, Wenwu Zhu
Then we propose a novel factor-wise discrimination objective in a contrastive learning manner, which can force the factorized representations to independently reflect the expressive information from different latent factors.
1 code implementation • NeurIPS 2021 • Heng Chang, Yu Rong, Tingyang Xu, Yatao Bian, Shiji Zhou, Xin Wang, Junzhou Huang, Wenwu Zhu
Graph Convolutional Networks (GCNs) are promising deep learning approaches in learning representations for graph-structured data.
no code implementations • 3 Nov 2021 • Ke Tu, Peng Cui, Daixin Wang, Zhiqiang Zhang, Jun Zhou, Yuan Qi, Wenwu Zhu
Knowledge graph is generally incorporated into recommender systems to improve overall performance.
no code implementations • 29 Sep 2021 • Xuguang Duan, Xin Wang, Ziwei Zhang, Wenwu Zhu
Program induction serves as one way to analog the ability of human thinking.
no code implementations • 29 Sep 2021 • Jiyan Jiang, Wenpeng Zhang, Shiji Zhou, Lihong Gu, Xiaodong Zeng, Wenwu Zhu
This paper presents a systematic study of multi-objective online learning.
no code implementations • 29 Sep 2021 • Lianzhe Wang, Shiji Zhou, Shanghang Zhang, Wenpeng Zhang, Heng Chang, Wenwu Zhu
Even though meta-learning has attracted research wide attention in recent years, the generalization problem of meta-learning is still not well addressed.
no code implementations • 16 Sep 2021 • Xiaohan Lan, Yitian Yuan, Xin Wang, Zhi Wang, Wenwu Zhu
In this survey, we give a comprehensive overview for TSGV, which i) summarizes the taxonomy of existing methods, ii) provides a detailed description of the evaluation protocols(i. e., datasets and metrics) to be used in TSGV, and iii) in-depth discusses potential problems of current benchmarking designs and research directions for further investigations.
no code implementations • 11 Jun 2021 • Shiji Zhou, Han Zhao, Shanghang Zhang, Lianzhe Wang, Heng Chang, Zhi Wang, Wenwu Zhu
Our theoretical results show that OSAMD can fast adapt to changing environments with active queries.
no code implementations • 26 May 2021 • Heng Chang, Yu Rong, Tingyang Xu, Wenbing Huang, Honglei Zhang, Peng Cui, Xin Wang, Wenwu Zhu, Junzhou Huang
We investigate the theoretical connections between graph signal processing and graph embedding models and formulate the graph embedding model as a general graph signal process with a corresponding graph filter.
2 code implementations • ICLR Workshop GTRL 2021 • Ziwei Zhang, Yijian Qin, Zeyang Zhang, Chaoyu Guan, Jie Cai, Heng Chang, Jiyan Jiang, Haoyang Li, Zixin Sun, Beini Xie, Yang Yao, YiPeng Zhang, Xin Wang, Wenwu Zhu
To fill this gap, we present Automated Graph Learning (AutoGL), the first dedicated library for automated machine learning on graphs.
2 code implementations • 1 Mar 2021 • Ziwei Zhang, Xin Wang, Wenwu Zhu
Machine learning on graphs has been extensively studied in both academic and industry.
1 code implementation • 22 Feb 2021 • Yudong Chen, Chaoyu Guan, Zhikun Wei, Xin Wang, Wenwu Zhu
Meta-learning aims at learning quickly on novel tasks with limited data by transferring generic experience learned from previous tasks.
no code implementations • 22 Jan 2021 • Yitian Yuan, Xiaohan Lan, Xin Wang, Long Chen, Zhi Wang, Wenwu Zhu
All the results demonstrate that the re-organized dataset splits and new metric can better monitor the progress in TSGV.
no code implementations • 25 Oct 2020 • Xin Wang, Yudong Chen, Wenwu Zhu
We discuss works on curriculum learning within a general CL framework, elaborating on how to design a manually predefined curriculum or an automatic curriculum.
1 code implementation • NeurIPS 2020 • Fangda Gu, Heng Chang, Wenwu Zhu, Somayeh Sojoudi, Laurent El Ghaoui
Graph Neural Networks (GNNs) are widely used deep learning models that learn meaningful representations from graph-structured data.
1 code implementation • 5 Sep 2020 • Ziwei Zhang, Chenhao Niu, Peng Cui, Jian Pei, Bo Zhang, Wenwu Zhu
Graph neural networks (GNNs) are emerging machine learning models on graphs.
1 code implementation • 23 Aug 2020 • Jianxin Ma, Chang Zhou, Hongxia Yang, Peng Cui, Xin Wang, Wenwu Zhu
There exist two challenges: i) reconstructing a future sequence containing many behaviors is exponentially harder than reconstructing a single next behavior, which can lead to difficulty in convergence, and ii) the sequence of all future behaviors can involve many intentions, not all of which may be predictable from the sequence of earlier behaviors.
no code implementations • 8 Jun 2020 • Ziwei Zhang, Peng Cui, Jian Pei, Xin Wang, Wenwu Zhu
Graph Neural Networks (GNNs) are emerging machine learning models on graphs.
1 code implementation • 2020 • Mingdong Ou, Peng Cui, Jian Pei, Ziwei Zhang, Wenwu Zhu
In particular, we develop a novel graph embedding algorithm, High-Order Proximity preserved Embedding (HOPE for short), which is scalable to preserve high-order proximities of large scale graphs and capable of capturing the asymmetric transitivity.
no code implementations • 16 Mar 2020 • Heng Chang, Yu Rong, Tingyang Xu, Wenbing Huang, Somayeh Sojoudi, Junzhou Huang, Wenwu Zhu
In this paper, we first introduce the attention mechanism in the spectral domain of graphs and present Spectral Graph Attention Network (SpGAT) that learns representations for different frequency components regarding weighted filters and graph wavelets bases.
no code implementations • 2 Jan 2020 • Wenwu Zhu, Xin Wang, Peng Cui
Mining graph data has become a popular research topic in computer science and has been widely studied in both academia and industry given the increasing amount of network data in the recent years.
no code implementations • NeurIPS 2019 • Jianxin Ma, Chang Zhou, Peng Cui, Hongxia Yang, Wenwu Zhu
Our approach achieves macro disentanglement by inferring the high-level concepts associated with user intentions (e. g., to buy a shirt or a cellphone), while capturing the preference of a user regarding the different concepts separately.
1 code implementation • NeurIPS 2019 • Yitian Yuan, Lin Ma, Jingwen Wang, Wei Liu, Wenwu Zhu
Temporal sentence grounding in videos aims to detect and localize one target video segment, which semantically corresponds to a given sentence.
1 code implementation • 25 Oct 2019 • Li Lyna Zhang, Yuqing Yang, Yuhang Jiang, Wenwu Zhu, Yunxin Liu
Unlike previous approaches that apply search algorithms on a small, human-designed search space without considering hardware diversity, we propose HURRICANE that explores the automatic hardware-aware search over a much larger search space and a two-stage search algorithm, to efficiently generate tailored models for different types of hardware.
Hardware Aware Neural Architecture Search Neural Architecture Search
no code implementations • 11 Oct 2019 • Wenwu Zhu, Xin Wang, Hongzhi Li
To address the two scientific problems, we investigate them from the following aspects: 1) multi-modal correlational representation: multi-modal fusion of data across different modalities, and 2) multi-modal data and knowledge fusion: multi-modal fusion of data with domain knowledge.
no code implementations • 25 Sep 2019 • Ming Jin, Heng Chang, Wenwu Zhu, Somayeh Sojoudi
By challenging the classical graph Laplacian, we propose a new convolution operator that is provably robust in the spectral domain and is incorporated in the GCN architecture to improve expressivity and interpretability.
no code implementations • 25 Sep 2019 • Heng Chang, Yu Rong, Somayeh Sojoudi, Junzhou Huang, Wenwu Zhu
Many variants of Graph Convolutional Networks (GCNs) for representation learning have been proposed recently and have achieved fruitful results in various domains.
1 code implementation • 17 Sep 2019 • Hongshan Li, Yu Guo, Zhi Wang, Shu-Tao Xia, Wenwu Zhu
Then we train the agent in a reinforcement learning way to adapt it for different deep learning cloud services that act as the {\em interactive training environment} and feeding a reward with comprehensive consideration of accuracy and data size.
Multimedia Image and Video Processing
1 code implementation • 12 Aug 2019 • Yitian Yuan, Lin Ma, Wenwu Zhu
With the tremendous growth of videos over the Internet, video thumbnails, providing video content previews, are becoming increasingly crucial to influencing users' online searching experiences.
1 code implementation • 4 Aug 2019 • Heng Chang, Yu Rong, Tingyang Xu, Wenbing Huang, Honglei Zhang, Peng Cui, Wenwu Zhu, Junzhou Huang
To this end, we begin by investigating the theoretical connections between graph signal processing and graph embedding models in a principled way and formulate the graph embedding model as a general graph signal process with corresponding graph filter.
1 code implementation • 24 May 2019 • Ming Jin, Heng Chang, Wenwu Zhu, Somayeh Sojoudi
By challenging the classical graph Laplacian, we propose a new convolution operator that is provably robust in the spectral domain and is incorporated in the GCN architecture to improve expressivity and interpretability.
no code implementations • 1 Jan 2019 • Shengze Yu, Xin Wang, Wenwu Zhu, Peng Cui, Jingdong Wang
However, there remain two unsolved challenges: i) there exist inconsistencies in cross-platform association due to platform-specific disparity, and ii) data from distinct platforms may have different semantic granularities.
1 code implementation • 11 Dec 2018 • Ziwei Zhang, Peng Cui, Wenwu Zhu
Recently, substantial research efforts have been devoted to applying deep learning methods to graphs, resulting in beneficial advances in graph analysis techniques.
no code implementations • NeurIPS 2018 • Xuguang Duan, Wenbing Huang, Chuang Gan, Jingdong Wang, Wenwu Zhu, Junzhou Huang
Dense event captioning aims to detect and describe all events of interest contained in a video.
no code implementations • 22 Jun 2018 • Yihong Chen, Bei Chen, Xuguang Duan, Jian-Guang Lou, Yue Wang, Wenwu Zhu, Yong Cao
Almost all the knowledge empowered applications rely upon accurate knowledge, which has to be either collected manually with high cost, or extracted automatically with unignorable errors.
2 code implementations • 7 May 2018 • Ziwei Zhang, Peng Cui, Haoyang Li, Xiao Wang, Wenwu Zhu
Network embedding, which learns low-dimensional vector representation for nodes in the network, has attracted considerable research attention recently.
no code implementations • 19 Apr 2018 • Yitian Yuan, Tao Mei, Wenwu Zhu
Then, a multi-modal co-attention mechanism is introduced to generate not only video attention which reflects the global video structure, but also sentence attention which highlights the crucial details for temporal localization.
no code implementations • 3 Dec 2017 • Guohao Li, Hang Su, Wenwu Zhu
To address this issue, we propose a novel framework which endows the model capabilities in answering more complex questions by leveraging massive external knowledge with dynamic memory networks.
1 code implementation • 28 Nov 2017 • Ke Tu, Peng Cui, Xiao Wang, Fei Wang, Wenwu Zhu
These hyper-networks pose great challenges to existing network embedding methods when the hyperedges are indecomposable, that is to say, any subset of nodes in a hyperedge cannot form another hyperedge.
Social and Information Networks
1 code implementation • 27 Nov 2017 • Ziwei Zhang, Peng Cui, Jian Pei, Xiao Wang, Wenwu Zhu
By setting a maximum tolerated error as a threshold, we can trigger SVD restart automatically when the margin exceeds this threshold. We prove that the time complexity of our method is linear with respect to the number of local dynamic changes, and our method is general across different types of dynamic networks.
Social and Information Networks
no code implementations • 23 Nov 2017 • Peng Cui, Xiao Wang, Jian Pei, Wenwu Zhu
Network embedding assigns nodes in a network to low-dimensional representations and effectively preserves the network structure.
Social and Information Networks
no code implementations • CVPR 2019 • Linjun Zhou, Peng Cui, Shiqiang Yang, Wenwu Zhu, Qi Tian
We then propose an out-of-sample embedding method to learn the embedding of a new class represented by a few samples through its visual analogy with base classes and derive the classification parameters for the new class.
no code implementations • ICML 2017 • Wenpeng Zhang, Peilin Zhao, Wenwu Zhu, Steven C. H. Hoi, Tong Zhang
The conditional gradient algorithm has regained a surge of research interest in recent years due to its high efficiency in handling large-scale machine learning problems.
2 code implementations • AAAI 2017 • Xiao Wang, Peng Cui, Jing Wang, Jian Pei, Wenwu Zhu, Shiqiang Yang
While previous network embedding methods primarily preserve the microscopic structure, such as the first- and second-order proximities of nodes, the mesoscopic community structure, which is one of the most prominent feature of networks, is largely ignored.