Search Results for author:
Found 8 papers, 3 papers with code
DistJoin: A Decoupled Join Cardinality Estimator based on Adaptive Neural Predicate Modulation
We conceptualize these challenges as the "Trilemma of Cardinality Estimation", where learned cardinality estimation methods struggle to balance generality, accuracy, and updatability.
MERLIN: Multi-stagE query performance prediction for dynamic paRallel oLap pIpeliNe
High-performance OLAP database technology has emerged with the growing demand for massive data analysis.
All-in-one Weather-degraded Image Restoration via Adaptive Degradation-aware Self-prompting Model
Specifically, our model employs the contrastive language-image pre-training model (CLIP) to facilitate the training of our proposed latent prompt generators (LPGs), which represent three types of latent prompts to characterize the degradation type, degradation property and image caption.
Encoder-minimal and Decoder-minimal Framework for Remote Sensing Image Dehazing
Haze obscures remote sensing images, hindering valuable information extraction.
GeoShapley: A Game Theory Approach to Measuring Spatial Effects in Machine Learning Models
This paper introduces GeoShapley, a game theory approach to measuring spatial effects in machine learning models.
Multi-dimension Queried and Interacting Network for Stereo Image Deraining
This module leverages dimension-wise queries that are independent of the input features and employs global context-aware attention (GCA) to capture essential features while avoiding the entanglement of redundant or irrelevant information.
GeoExplainer: A Visual Analytics Framework for Spatial Modeling Contextualization and Report Generation
As analysts create their spatial models, our framework flags potential issues with model parameter selections, utilizes template-based text generation to summarize model outputs, and links with external knowledge repositories to provide annotations that help to explain the model results.
Duet: efficient and scalable hybriD neUral rElation undersTanding
Although both data-driven and hybrid methods are proposed to avoid this problem, most of them suffer from high training and estimation costs, limited scalability, instability, and long-tail distribution problems on high-dimensional tables, which seriously affects the practical application of learned cardinality estimators.
