In this paper, we explore a new research direction to perform knowledge base (KB) representation learning grounded with the recent theoretical framework of ...
In this paper, we explore a new research direction to perform knowledge base (KB) representation learning grounded with the recent theoretical framework of ...
In this paper, we explore a new research direction to perform knowledge base (KB) representation learning grounded with the recent theoretical framework of ...
Experimental results on two standard datasets show that knowledge distillation between KBs through entity and relation inference is actually observed and ...
Oct 20, 2020 · mkb is a library dedicated to knowledge graph embeddings. The purpose of this library is to provide modular tools using PyTorch. Table of ...
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Knowledge distillation is a simple but powerful way to transfer knowledge between a teacher model to a student model. ... We formulate a novel form of knowledge ...
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Knowledge distillation is a simple but powerful way to transfer knowledge between a teacher model to a student model. Existing work suffers from at least ...
Given the heterogeneous knowledge modelled above, a good meta- path based embedding is expected to distill regional knowledge and global knowledge from all ...
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Feb 2, 2024 · Knowledge distillation is a simple and elegant approach that allows one machine (the teacher) to instruct another machine (the student).
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