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Apr 27, 2024 · In this paper, we introduce a novel parameter-efficient framework, KGRP which a) approximates bilinear pooling using Kernelized Random Projection matrix.
Oct 23, 2023 · Abstract—Heterogeneous Graph Neural Networks (HGNNs) are powerful tools for deep learning on heterogeneous graphs.
Efficient Knowledge Graph Embeddings via Kernelized Random Projections. N Goyal, A Goel, T Garg, N Sachdeva, P Kumaraguru. International Conference on Big Data ...
Apr 27, 2024 · Efficient Knowledge Graph Embeddings via Kernelized Random Projections. Crossref DOI link: https://doi.org/10.1007/978-3-031-58502-9_14.
Some papers on Knowledge Graph Embedding(KGE). Contribute to trieu/Knowledge-Graph-Embedding development by creating an account on GitHub.
Sep 3, 2024 · In this paper, we propose a hybrid style pre-computation-based HGNN, named Random Projection Heterogeneous Graph Neural Network (RpHGNN), which ...
Aug 29, 2024 · We introduce a novel method to efficiently reduce the dimension of bilinear pooling descriptors by performing a Random Projection. Conveniently, ...
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Using random projection, a method to speed up both kernel k-means and centroid initialization with k-means++ is proposed.
The sklearn.random_projection module implements a simple and computationally efficient way to reduce the dimensionality of the data by trading a controlled ...
Efficient Heterogeneous Graph Learning via Random Projection pp. 1-14. Online Learning and Detecting Corrupted Users for Conversational Recommendation ...