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Found 32 papers, 9 papers with code
Exploring Progress in Multivariate Time Series Forecasting: Comprehensive Benchmarking and Heterogeneity Analysis
Moreover, based on the proposed BasicTS and rich heterogeneous MTS datasets, we conduct an exhaustive and reproducible performance and efficiency comparison of popular models, providing insights for researchers in selecting and designing MTS forecasting models.
Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecasting
However, intuitively, traffic data encompasses two different kinds of hidden time series signals, namely the diffusion signals and inherent signals.
Ranked #3 on
Traffic Prediction
on PEMS-BAY
MDERank: A Masked Document Embedding Rank Approach for Unsupervised Keyphrase Extraction
In this work, we propose a novel unsupervised embedding-based KPE approach, Masked Document Embedding Rank (MDERank), to address this problem by leveraging a mask strategy and ranking candidates by the similarity between embeddings of the source document and the masked document.
Multi-level Cross-view Contrastive Learning for Knowledge-aware Recommender System
Different from traditional contrastive learning methods which generate two graph views by uniform data augmentation schemes such as corruption or dropping, we comprehensively consider three different graph views for KG-aware recommendation, including global-level structural view, local-level collaborative and semantic views.
Detecting Communities from Heterogeneous Graphs: A Context Path-based Graph Neural Network Model
With the surge of graph embedding mechanism, it has also been adopted to community detection.
Inductive Link Prediction for Nodes Having Only Attribute Information
In attributed graphs, both the structure and attribute information can be utilized for link prediction.
On the Power of Gradual Network Alignment Using Dual-Perception Similarities
Network alignment (NA) is the task of finding the correspondence of nodes between two networks based on the network structure and node attributes.
Criteria Tell You More than Ratings: Criteria Preference-Aware Light Graph Convolution for Effective Multi-Criteria Recommendation
The multi-criteria (MC) recommender system, which leverages MC rating information in a wide range of e-commerce areas, is ubiquitous nowadays.
Edgeless-GNN: Unsupervised Representation Learning for Edgeless Nodes
We study the problem of embedding edgeless nodes such as users who newly enter the underlying network, while using graph neural networks (GNNs) widely studied for effective representation learning of graphs.
Activity recognition using ST-GCN with 3D motion data
The recognition model with a tree-structure graph was then created.
Monotonic Cardinality Estimation of Similarity Selection: A Deep Learning Approach
The feature extraction model transforms original data and threshold to a Hamming space, in which a deep learning-based regression model is utilized to exploit the incremental property of cardinality w. r. t.
Multi Voxel-Point Neurons Convolution (MVPConv) for Fast and Accurate 3D Deep Learning
Extensive experiments on benchmark datasets such as ShapeNet Part, S3DIS and KITTI for various tasks show that MVPConv improves the accuracy of the backbone (PointNet) by up to 36%, and achieves higher accuracy than the voxel-based model with up to 34 times speedup.
Unsupervised Segmentation for Terracotta Warrior with Seed-Region-Growing CNN(SRG-Net)
Then we present a supervised segmentation and unsupervised reconstruction networks to learn the characteristics of 3D point clouds.
Multi Point-Voxel Convolution (MPVConv) for Deep Learning on Point Clouds
Extensive experiments on benchmark datasets such as ShapeNet Part, S3DIS and KITTI for various tasks show that MPVConv improves the accuracy of the backbone (PointNet) by up to \textbf{36\%}, and achieves higher accuracy than the voxel-based model with up to \textbf{34}$\times$ speedups.
Off-Dynamics Inverse Reinforcement Learning from Hetero-Domain
To achieve this, the widely used GAN-inspired IRL method is adopted, and its discriminator, recognizing policy-generating trajectories, is modified with the quantification of dynamics difference.
KS-GNN: Keywords Search over Incomplete Graphs via Graphs Neural Network
Keyword search is a fundamental task to retrieve information that is the most relevant to the query keywords.
Machine Learning Solar Wind Driving Magnetospheric Convection in Tail Lobes
To quantitatively study the driving mechanisms of magnetospheric convection in the magnetotail lobes on a global scale, we utilize data from the ARTEMIS spacecraft in the deep tail and the Cluster spacecraft in the near tail.
BCOT: A Markerless High-Precision 3D Object Tracking Benchmark
Our new benchmark dataset contains 20 textureless objects, 22 scenes, 404 video sequences and 126K images captured in real scenes.
BFE and AdaBFE: A New Approach in Learning Rate Automation for Stochastic Optimization
In this paper, a new gradient-based optimization approach by automatically adjusting the learning rate is proposed.
Improved Binary Forward Exploration: Learning Rate Scheduling Method for Stochastic Optimization
A new gradient-based optimization approach by automatically scheduling the learning rate has been proposed recently, which is called Binary Forward Exploration (BFE).
GSim: A Graph Neural Network based Relevance Measure for Heterogeneous Graphs
Most of existing relevance measures focus on homogeneous networks where objects are of the same type, and a few measures are developed for heterogeneous graphs, but they often need the pre-defined meta-path.
Grad-Align+: Empowering Gradual Network Alignment Using Attribute Augmentation
Network alignment (NA) is the task of discovering node correspondences across different networks.
Weighted Sampling for Masked Language Modeling
Experiments on the Semantic Textual Similarity benchmark (STS) show that WSBERT significantly improves sentence embeddings over BERT.
WISK: A Workload-aware Learned Index for Spatial Keyword Queries
In this paper, we propose WISK, a learned index for spatial keyword queries, which self-adapts for optimizing querying costs given a query workload.
Node Feature Augmentation Vitaminizes Network Alignment
Network alignment (NA) is the task of discovering node correspondences across multiple networks.
Exploiting Correlations Between Contexts and Definitions with Multiple Definition Modeling
In this paper, we carefully design a new task called Multiple Definition Modeling (MDM) that pool together all contexts and definition of target words.
Graph Neural Networks Provably Benefit from Structural Information: A Feature Learning Perspective
Graph neural networks (GNNs) have pioneered advancements in graph representation learning, exhibiting superior feature learning and performance over multilayer perceptrons (MLPs) when handling graph inputs.
SR-R$^2$KAC: Improving Single Image Defocus Deblurring
To further alleviate the contingent effect of recursive stacking, i. e., ringing artifacts, we add identity shortcuts between atrous convolutions to simulate residual deconvolutions.
PointMoment:Mixed-Moment-based Self-Supervised Representation Learning for 3D Point Clouds
Large and rich data is a prerequisite for effective training of deep neural networks.
PointJEM: Self-supervised Point Cloud Understanding for Reducing Feature Redundancy via Joint Entropy Maximization
To reduce redundant information in the features, PointJEM maximizes the joint entropy between the different parts, thereby rendering the learned feature variables pairwise independent.
Exact analytical algorithm for solvent accessible surface area and derivatives in implicit solvent molecular simulations on GPUs
In this paper, we present dSASA (differentiable SASA), an exact geometric method to calculate solvent accessible surface area (SASA) analytically along with atomic derivatives on GPUs.
Deep Structural Knowledge Exploitation and Synergy for Estimating Node Importance Value on Heterogeneous Information Networks
Node importance estimation problem has been studied conventionally with homogeneous network topology analysis.
