Search Results for author:
Found 23 papers, 10 papers with code
MetaWeighting: Learning to Weight Tasks in Multi-Task Learning
Task weighting, which assigns weights on the including tasks during training, significantly matters the performance of Multi-task Learning (MTL); thus, recently, there has been an explosive interest in it.
Adaptive Adversarial Multi-task Representation Learning
Adversarial Multi-task Representation Learning (AMTRL) methods are able to boost the performance of Multi-task Representation Learning (MTRL) models.
Hypergraph Self-supervised Learning with Sampling-efficient Signals
Self-supervised learning (SSL) provides a promising alternative for representation learning on hypergraphs without costly labels.
Diffusion-based graph generative methods
Being the most cutting-edge generative methods, diffusion methods have shown great advances in wide generation tasks.
Denoising Variational Graph of Graphs Auto-Encoder for Predicting Structured Entity Interactions
The interactions between structured entities play important roles in a wide range of applications such as chemistry, material science, biology, and medical science.
Unleashing the Potential of Unsupervised Deep Outlier Detection through Automated Training Stopping
To our knowledge, our approach is the first to enable reliable identification of the optimal training iteration during training without requiring any labels.
Unsupervised Graph Outlier Detection: Problem Revisit, New Insight, and Superior Method
In addition, we observe that existing algorithms have a performance drop with the mitigated data leakage issue.
Towards Higher-order Topological Consistency for Unsupervised Network Alignment
Network alignment task, which aims to identify corresponding nodes in different networks, is of great significance for many subsequent applications.
Reinforcement Learning Based Query Vertex Ordering Model for Subgraph Matching
In recent years, many advanced techniques for query vertex ordering (i. e., matching order generation) have been proposed to reduce the unpromising intermediate results according to the preset heuristic rules.
GridTuner: Reinvestigate Grid Size Selection for Spatiotemporal Prediction Models [Technical Report]
In this paper, we study a region partitioning problem, namely optimal grid size selection problem (OGSS), which aims to minimize the real error of spatiotemporal prediction models by selecting the optimal grid size.
BanditMTL: Bandit-based Multi-task Learning for Text Classification
Task variance regularization, which can be used to improve the generalization of Multi-task Learning (MTL) models, remains unexplored in multi-task text classification.
A Queueing-Theoretic Framework for Vehicle Dispatching in Dynamic Car-Hailing [technical report]
In this paper, we consider an important dynamic car-hailing problem, namely \textit{maximum revenue vehicle dispatching} (MRVD), in which rider requests dynamically arrive and drivers need to serve as many riders as possible such that the entire revenue of the platform is maximized.
Reinforcement Learning based Collective Entity Alignment with Adaptive Features
Entity alignment (EA) is the task of identifying the entities that refer to the same real-world object but are located in different knowledge graphs (KGs).
PEFP: Efficient k-hop Constrained s-t Simple Path Enumeration on FPGA
Motivated by this, in this paper, we propose the first FPGA-based algorithm PEFP to solve the problem of k-hop constrained s-t simple path enumeration efficiently.
Databases
GoGNN: Graph of Graphs Neural Network for Predicting Structured Entity Interactions
We observe that existing works on structured entity interaction prediction cannot properly exploit the unique graph of graphs model.
Taming the Expressiveness and Programmability of Graph Analytical Queries
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.
Binarized Graph Neural Network
Our proposed method can be seamlessly integrated into the existing GNN-based embedding approaches to binarize the model parameters and learn the compact embedding.
Collective Entity Alignment via Adaptive Features
Entity alignment (EA) identifies entities that refer to the same real-world object but locate in different knowledge graphs (KGs), and has been harnessed for KG construction and integration.
A Survey and Experimental Analysis of Distributed Subgraph Matching
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.
Databases
Efficient Graph Edit Distance Computation and Verification via Anchor-aware Lower Bound Estimation
Graph edit distance (GED) is an important similarity measure adopted in a similarity-based analysis between two graphs, and computing GED is a primitive operator in graph database analysis.
Databases Data Structures and Algorithms
Effective Multi-Query Expansions: Collaborative Deep Networks for Robust Landmark Retrieval
Given a query photo issued by a user (q-user), the landmark retrieval is to return a set of photos with their landmarks similar to those of the query, while the existing studies on the landmark retrieval focus on exploiting geometries of landmarks for similarity matches between candidate photos and a query photo.
Approximate Nearest Neighbor Search on High Dimensional Data --- Experiments, Analyses, and Improvement (v1.0)
Approximate Nearest neighbor search (ANNS) is fundamental and essential operation in applications from many domains, such as databases, machine learning, multimedia, and computer vision.
Databases
Iterative Views Agreement: An Iterative Low-Rank based Structured Optimization Method to Multi-View Spectral Clustering
Multi-view spectral clustering, which aims at yielding an agreement or consensus data objects grouping across multi-views with their graph laplacian matrices, is a fundamental clustering problem.
