Tensor Decomposition
126 papers with code • 0 benchmarks • 0 datasets
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Use these libraries to find Tensor Decomposition models and implementationsMost implemented papers
KGE-CL: Contrastive Learning of Tensor Decomposition Based Knowledge Graph Embeddings
wentao-xu/kge-cl
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COLING 2022
Learning the embeddings of knowledge graphs (KG) is vital in artificial intelligence, and can benefit various downstream applications, such as recommendation and question answering.
Parameter-Efficient Mixture-of-Experts Architecture for Pre-trained Language Models
Recently, Mixture-of-Experts (short as MoE) architecture has achieved remarkable success in increasing the model capacity of large-scale language models.
PIE: a Parameter and Inference Efficient Solution for Large Scale Knowledge Graph Embedding Reasoning
alipay/parameter_inference_efficient_pie
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Meanwhile, the inference time grows log-linearly with the number of entities for all entities are traversed and compared.
Multi-view Tensor Graph Neural Networks Through Reinforced Aggregation
ringbdstack/rtgnn
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journal 2023
Specifically, RTGNN first uses tensor decomposition to extract the graph structure features (GSFs) of each view in the common feature space.
MultiHU-TD: Multifeature Hyperspectral Unmixing Based on Tensor Decomposition
Matrix models become insufficient when the hyperspectral image (HSI) is represented as a high-order tensor with additional features in a multimodal, multifeature framework.
Empirical Evaluation of Four Tensor Decomposition Algorithms
We recommend HOOI for tensors that are small enough for the available RAM and MP for larger tensors.
Temporal Link Prediction using Matrix and Tensor Factorizations
We show how the well-known Katz method for link prediction can be extended to bipartite graphs and, moreover, approximated in a scalable way using a truncated singular value decomposition.
Fourier PCA and Robust Tensor Decomposition
Fourier PCA is Principal Component Analysis of a matrix obtained from higher order derivatives of the logarithm of the Fourier transform of a distribution. We make this method algorithmic by developing a tensor decomposition method for a pair of tensors sharing the same vectors in rank-$1$ decompositions.
Online Tensor Methods for Learning Latent Variable Models
We introduce an online tensor decomposition based approach for two latent variable modeling problems namely, (1) community detection, in which we learn the latent communities that the social actors in social networks belong to, and (2) topic modeling, in which we infer hidden topics of text articles.
Escaping From Saddle Points --- Online Stochastic Gradient for Tensor Decomposition
To the best of our knowledge this is the first work that gives global convergence guarantees for stochastic gradient descent on non-convex functions with exponentially many local minima and saddle points.
