arXiv AI
Jun 9
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.
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.
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arXiv Machine Learning
Jul 9
VTC: DNN Compilation with Virtual Tensors for Data Movement Elimination
arXiv:2604. 09558v2 Announce Type: replace-cross Abstract: With the widening gap between compute and memory operation latencies, data movement optimizations have become increasingly important for DNN compilation.
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.
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.
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.
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.
arXiv Machine Learning
Jul 8
Separation Capacity of Scattering Networks on Low-Dimensional Datasets
arXiv:2607. 06048v1 Announce Type: cross Abstract: We aim to identify scattering network architectures that maximize the separation capacity on data with low intrinsic dimension.
Hugging Face Trending Papers
Aug 3
CMuon: Accelerating and Stabilizing Diffusion Transformer Training via Chunked Momentum Orthogonalization
Diffusion Transformers (DiTs) have achieved state-of-the-art (SOTA) performance in visual generative modeling, yet their training remains computationally prohibitive. While the recently proposed Momentum Orthogonalization (Muon) optimizer offers a promising alternative to AdamW, its direct application to DiTs yields suboptimal late-stage convergence.
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).
arXiv AI
Jun 15
Squeeze-Release: Iterative Pruning with Exact Structural Minimization
arXiv:2606. 14346v1 Announce Type: cross Abstract: Unstructured pruning produces sparse weight tensors, but the standard implementation keeps tensor shapes unchanged so the deployed model is no smaller than before pruning.
arXiv Machine Learning
Jun 2
Learning Fine-grained Parameter Sharing via Sparse Tensor Decomposition
arXiv:2411. 09816v5 Announce Type: replace Abstract: Large neural networks achieve state-of-the-art performance on many tasks, yet their sheer size hinders deployment on resource-constrained devices.
arXiv AI
Jul 29
At-the-Roofline Sparse Tensor Contractions on Vector Processors for Transformer Inference
arXiv:2607. 25504v1 Announce Type: cross Abstract: Fine-grained weight pruning and activation sparsification have emerged as effective approaches for reducing the compute and memory cost of inference for Transformer models.