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Found 9 papers, 2 papers with code
Identifying Causal Effects under Kink Setting: Theory and Evidence
This paper develops a generalized framework for identifying causal impacts in a reduced-form manner under kinked settings when agents can manipulate their choices around the threshold.
A Survey of Learned Indexes for the Multi-dimensional Space
We present a taxonomy that classifies and categorizes each learned multi-dimensional index, and survey the existing literature on learned multi-dimensional indexes according to this taxonomy.
Computing in the Era of Large Generative Models: From Cloud-Native to AI-Native
In this paper, we investigate the intersection of large generative AI models and cloud-native computing architectures.
SUREL+: Moving from Walks to Sets for Scalable Subgraph-based Graph Representation Learning
Subgraph-based graph representation learning (SGRL) has recently emerged as a powerful tool in many prediction tasks on graphs due to its advantages in model expressiveness and generalization ability.
Ranked #1 on
Link Property Prediction
on ogbl-ppa
The "AI+R"-tree: An Instance-optimized R-tree
This paper investigates to leverage ML techniques to enhance the performance of spatial indexes, particularly the R-tree, for a given data and query workloads.
Algorithm and System Co-design for Efficient Subgraph-based Graph Representation Learning
Subgraph-based graph representation learning (SGRL) has been recently proposed to deal with some fundamental challenges encountered by canonical graph neural networks (GNNs), and has demonstrated advantages in many important data science applications such as link, relation and motif prediction.
Ranked #1 on
Link Property Prediction
on ogbl-citation2
Deep learning for prediction of hepatocellular carcinoma recurrence after resection or liver transplantation: a discovery and validation study
LT set contained patients with HCC treated by LT.
Balance Scene Learning Mechanism for Offshore and Inshore Ship Detection in SAR Images
Huge imbalance of different scenes' sample numbers seriously reduces Synthetic Aperture Radar (SAR) ship detection accuracy.
Sherlock: Sparse Hierarchical Embeddings for Visually-aware One-class Collaborative Filtering
Building successful recommender systems requires uncovering the underlying dimensions that describe the properties of items as well as users' preferences toward them.
