WebCurrent approaches cast the problem in a meta-learning framework, where the model needs to be first jointly trained over many training few-shot tasks, each being defined by its own relation, so that learning/prediction on the target few-shot task can be effective. WebSep 4, 2024 · Link prediction is an important way to complete knowledge graphs (KGs), while embedding-based methods, effective for link prediction in KGs, perform poorly on relations that only have a few associative triples. In this work, we propose a Meta Relational Learning (MetaR) framework to do the common but challenging few-shot link …
MetaRF: attention-based random forest for reaction yield prediction ...
WebJul 26, 2024 · Compared to humans, machine learning models generally require significantly more training examples and fail to extrapolate from experience to solve … WebMeta-Graph: Few shot Link Prediction via Meta-Learning Avishek Joey Bose, Ankit Jain, Piero Molino, and William L. Hamilton NeurIPS Graph Representation Learning Workshop 2024. pdf (arxiv) CLUTRR: A Diagnostic Benchmark for Inductive Reasoning from Text Koustuv Sinha, Shagun Sodhani, Jin Dong, Joelle Pineau, and William L. Hamilton … deer lake northern ontario
Meta-Graph: Few Shot Link Prediction via Meta Learning
WebDec 20, 2024 · unexplored problem on graph-structured data. Few-Shot Link Prediction is a challenging task representative of real world data with evolving sub-graphs or entirely new graphs with shared structure. In this work, we present a meta-learning approach to Few Shot Link-Prediction. We further introduce WebDec 20, 2024 · Few-Shot Link Prediction is a challenging task representative of real world data with evolving sub-graphs or entirely new graphs with shared structure. In this work, … WebThis work proposes a Meta Relational Learning (MetaR) framework to do the common but challenging few-shot link prediction in KGs, namely predicting new triples about a relation by only observing a few associative triples. Link prediction is an important way to complete knowledge graphs (KGs), while embedding-based methods, effective for link prediction … fedex warehouse long island city