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Found 7 papers, 2 papers with code
LLM-R2: A Large Language Model Enhanced Rule-based Rewrite System for Boosting Query Efficiency
In order to maintain equivalence between the rewritten query and the original one during rewriting, traditional query rewrite methods always rewrite the queries following certain rewrite rules.
STMGF: An Effective Spatial-Temporal Multi-Granularity Framework for Traffic Forecasting
Accurate Traffic Prediction is a challenging task in intelligent transportation due to the spatial-temporal aspects of road networks.
Towards Effective Next POI Prediction: Spatial and Semantic Augmentation with Remote Sensing Data
The next point-of-interest (POI) prediction is a significant task in location-based services, yet its complexity arises from the consolidation of spatial and semantic intent.
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
Towards Practical Few-shot Federated NLP
To address these challenges, we first introduce a data generator for federated few-shot learning tasks, which encompasses the quantity and skewness of scarce labeled data in a realistic setting.
DuETA: Traffic Congestion Propagation Pattern Modeling via Efficient Graph Learning for ETA Prediction at Baidu Maps
In this work, we focus on modeling traffic congestion propagation patterns to improve ETA performance.
Effective Few-Shot Named Entity Linking by Meta-Learning
In this paper, we endeavor to solve the problem of few-shot entity linking, which only requires a minimal amount of in-domain labeled data and is more practical in real situations.
