arXiv AI

EinSort: Sorting is All We Need for Tensorizing LLM

arXiv:2606. 08565v1 Announce Type: cross Abstract: Tensor networks provide efficient representations for compressing large neural networks.

arXiv AI

Jun 3

Rethinking the Role of Tensor Decompositions in Post-Training LLM Compression

arXiv:2606. 03465v1 Announce Type: cross Abstract: Post-training compression is essential for deploying large language models (LLMs) under tight resource constraints.

By Artur Zagitov, Alexander Miasnikov, Maxim Krutikov, Vladimir Aletov, Gleb Molodtsov, Nail Bashirov, Artem Tsedenov, Aleksandr Beznosikov

llms

Hugging Face Trending Papers

Jun 2

Rethinking the Role of Tensor Decompositions in Post-Training LLM Compression

Post-training compression is essential for deploying large language models (LLMs) under tight resource constraints. Tensor decompositions have emerged as a promising direction, offering compact parameterizations well suited to Transformer weight structures.

llms

arXiv Machine Learning

Aug 5

Robust Low-Tubal-Rank Tensor Completion under Cross-Concentrated Sampling

arXiv:2608. 03928v1 Announce Type: cross Abstract: Tensor cross-concentrated sampling (t-CCS) bridges entrywise sampling and t-CUR slice-wise sampling by observing entries only within selected horizontal and lateral slices.

By Hanqin Cai, Longxiu Huang, Jing Qin, Chengyue Wu

arXiv AI

22h ago

Iterative tensor network transformations for element-wise evaluation of elementary and filtering functions

arXiv:2608. 17135v1 Announce Type: cross Abstract: Tensor networks are powerful formats for compressing large-scale data.

By Xiao Wang, Tomohiro Hashizume, Pia Siegl, Dieter Jaksch

arXiv AI

Jul 3

MetaTT: A Global Tensor-Train Adapter for Parameter-Efficient Fine-Tuning

arXiv:2506. 09105v3 Announce Type: replace-cross Abstract: We present MetaTT, a Tensor Train (TT) adapter framework for fine-tuning of pre-trained transformers.

By Javier Lopez-Piqueres, Pranav Deshpande, Archan Ray, Mattia J. Villani, Marco Pistoia, Niraj Kumar

llms fine-tuning benchmarks

arXiv Machine Learning

Aug 3

Tensor Data Scattering and the Impossibility of Slicing Theorem

arXiv:2012. 01982v3 Announce Type: replace Abstract: This paper proposes a standard way to represent sparse tensors.

By Wuming Pan

efficiency

arXiv Machine Learning

Jun 10

Hyperparameter Learning for Latent Factorization of Tensors for Representation Learning to Large-scale Dynamic Weighted Directed Network

arXiv:2606. 09880v1 Announce Type: new Abstract: Large-scale dynamic weighted directed networks (DWDNs) are widely used to model time-varying interactions among nodes.

By Yaqian Zhan, Jialan He, Tianzhu Chen

rag

arXiv Machine Learning

Aug 12

Accelerated Learning of High Dimensional Functions with a Tensor-Featured Training Network

arXiv:2608. 10351v1 Announce Type: new Abstract: In this work we present a method to accelerate the optimization of learning high dimensional functions using deep neural network (DNN).

By Karl Pierce, Yuehaw Khoo, Haizhao Yang

arXiv AI

Jul 7

Automatically Differentiable Nonlinear Tensor Networks (ADNTNs) for Exponential Parameter Compression of Deep Neural Networks

arXiv:2606. 00130v2 Announce Type: replace-cross Abstract: Large deep neural networks are costly to store and deploy because inference must move and evaluate many parameters.

By Andrzej Cichocki, Michal Wietczak

efficiency

arXiv AI

Jun 2

Automatically Differentiable Nonlinear Tensor Networks (ADNTNs) for Exponential Compression of Deep Neural Networks

arXiv:2606. 00130v1 Announce Type: cross Abstract: We study Automatically Differentiable Nonlinear Tensor Networks (ADNTNs), a family of structured weight generators whose compact core tensors are trained end-to-end by reverse-mode automatic differentiation (AD).

By Andrzej Cichocki, Michal Wietczak

arXiv AI

Jul 22

Compressing What Matters: Neuron Importance Meets Data-Aware Low Rank Approximation for Language Model Compression

arXiv:2607. 18284v1 Announce Type: cross Abstract: To excel at their domain large language models are comprised of billions of parameters.

By Athanasios Ntovas, Alexandros Doumanoglou, Petros Drakoulis, Dimitris Zarpalas

llms efficiency benchmarks

arXiv Machine Learning

Jul 1

Online TT-ALS for Streaming Tensor Decomposition with Incremental Orthogonalization

arXiv:2606. 31061v1 Announce Type: cross Abstract: Tensor Train (TT) decomposition is a powerful technique for analyzing high-dimensional data.

By Hiroki Takeda, Yuto Miyatake, Daisuke Furihata

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