Search Results for author:
Found 10 papers, 3 papers with code
Location-Centered House Price Prediction: A Multi-Task Learning Approach
Regarding the choice of prediction model, we observe that a variety of approaches either consider the entire house data for modeling, or split the entire data and model each partition independently.
Crowdsourced Collective Entity Resolution with Relational Match Propagation
Knowledge bases (KBs) store rich yet heterogeneous entities and facts.
Temporal Network Representation Learning via Historical Neighborhoods Aggregation
More specifically, we first propose a temporal random walk that can identify relevant nodes in historical neighborhoods which have impact on edge formations.
On the Efficiency of K-Means Clustering: Evaluation, Optimization, and Algorithm Selection
This paper presents a thorough evaluation of the existing methods that accelerate Lloyd's algorithm for fast k-means clustering.
A Survey on Advancing the DBMS Query Optimizer: Cardinality Estimation, Cost Model, and Plan Enumeration
A cost-based optimizer introduces a plan enumeration algorithm to find a (sub)plan, and then uses a cost model to obtain the cost of that plan, and selects the plan with the lowest cost.
Spatial Object Recommendation with Hints: When Spatial Granularity Matters
We design a novel multi-task learning model called MPR (short for Multi-level POI Recommendation), where each task aims to return the top-k POIs at a certain spatial granularity level.
Points-of-Interest Relationship Inference with Spatial-enriched Graph Neural Networks
As a fundamental component in location-based services, inferring the relationship between points-of-interests (POIs) is very critical for service providers to offer good user experience to business owners and customers.
Towards Data-centric Graph Machine Learning: Review and Outlook
Data-centric AI, with its primary focus on the collection, management, and utilization of data to drive AI models and applications, has attracted increasing attention in recent years.
GraphRARE: Reinforcement Learning Enhanced Graph Neural Network with Relative Entropy
To address this limitation, this paper presents GraphRARE, a general framework built upon node relative entropy and deep reinforcement learning, to strengthen the expressive capability of GNNs.
Ranked #2 on
Node Classification
on Cornell
Semantic-Enhanced Representation Learning for Road Networks with Temporal Dynamics
In this study, we introduce a novel framework called Toast for learning general-purpose representations of road networks, along with its advanced counterpart DyToast, designed to enhance the integration of temporal dynamics to boost the performance of various time-sensitive downstream tasks.
