Search Results for author:
Found 10 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.
Diverse and Tailored Image Generation for Zero-shot Multi-label Classification
Our approach introduces a novel image generation framework that produces multi-label synthetic images of unseen classes for classifier training.
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.
Positive Label Is All You Need for Multi-Label Classification
Our paper addresses label noise in MLC by introducing a positive and unlabeled multi-label classification (PU-MLC) method.
TENSILE: A Tensor granularity dynamic GPU memory scheduling method toward multiple dynamic workloads system
In this paper, we demonstrated TENSILE, a method of managing GPU memory in tensor granularity to reduce the GPU memory peak, considering the multiple dynamic workloads.
A parallel-network continuous quantitative trading model with GARCH and PPO
It is a difficult task for both professional investors and individual traders continuously making profit in stock market.
Auto-STGCN: Autonomous Spatial-Temporal Graph Convolutional Network Search Based on Reinforcement Learning and Existing Research Results
In recent years, many spatial-temporal graph convolutional network (STGCN) models are proposed to deal with the spatial-temporal network data forecasting problem.
ConsciousControlFlow(CCF): A Demonstration for conscious Artificial Intelligence
In this demo, we present ConsciousControlFlow(CCF), a prototype system to demonstrate conscious Artificial Intelligence (AI).
Automatic Hyper-Parameter Optimization Based on Mapping Discovery from Data to Hyper-Parameters
However, most machine learning algorithms are sensitive to the hyper-parameters.
