Tensor-Train Decomposition
PulseAugur coverage of Tensor-Train Decomposition — every cluster mentioning Tensor-Train Decomposition across labs, papers, and developer communities, ranked by signal.
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New HMD Framework Offers Efficient Hyperspectral Image Classification
Researchers have developed a new framework called Holistic Multivariance Decomposition (HMD) for hyperspectral image classification. This novel approach aims to improve accuracy and efficiency by capturing complex spati…
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New KReTTaH framework offers data-free imputation via tensor trains
A new framework called KReTTaH has been introduced for multi-way data imputation, utilizing kernel regression with tensor trains and Hadamard overparameterization. This method is designed to be training-data-free, inter…
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New MPO-based framework enhances polynomial function approximation
Researchers have introduced a new framework called Multivariate Polynomial Optimization based on Matrix Product Operators (MPO)$^2$. This approach combines learned MPO feature embeddings with compact polynomial weight t…
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New Tensor-Train Modeling Boosts Discrete Diffusion Model Speed
Researchers have introduced a new framework called Tensor-Train Joint Modeling to improve the speed and efficiency of discrete diffusion models for sequential data. This method addresses a limitation in current models t…
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MetaTT introduces parameter-efficient fine-tuning via Tensor Train adapters
Researchers have introduced MetaTT, a novel framework for parameter-efficient fine-tuning of pre-trained transformer models. MetaTT utilizes a Tensor Train (TT) adapter to factorize transformer sub-modules, allowing for…
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New Graph Transformer Improves Inference in Graphical Models
Researchers have developed In-Context Graphical Inference (ICG-I), a novel autoregressive Graph Transformer designed to improve marginal inference in discrete graphical models. This new method mimics the Variable Elimin…
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Tensor train algorithms offer new approach to anomaly detection
Researchers have developed new algorithms for anomaly detection using tensor network representations, specifically the Tensor Train format. These methods work by compressing normal data while effectively discarding anom…
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New algorithms offer efficient finite initialization for tensorized neural networks
Researchers have developed novel algorithms for initializing layers in tensorized neural networks and tensor network algorithms. These methods utilize partial computations of Frobenius norms and positive lineal entrywis…