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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

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6 results for “subtensor”

Property Inheritance for Subtensors in Tensor Train Decompositionscs.IT

This paper studies how key tensor properties are inherited in subtensors of tensor train decompositions.

problem Theoretical development of property inheritance for subtensors in tensor train decompositions.

method Theoretical analysis of incoherence and condition number preservation, and tensor train rank preservation through fiber-wise sampling.

result Key tensor properties (incoherence and condition number) can be well preserved to subtensors formed via fiber-wise sampling.

tensor train subtensor incoherence condition number fiber-wise sampling

2025-04-15abs ↗pdf ↗

GrateTile: Efficient Sparse Tensor Tiling for CNN Processingcs.LG

GrateTile optimizes CNN feature map storage for efficient data access.

problem Efficient storage and access of sparse CNN feature maps.

method Divides feature maps into uneven-sized subtensors, compresses and stores them in a compressed yet accessible format.

result Average 55% DRAM bandwidth reduction with minimal indexing overhead.

CNN Sparse Tensors Data Compression DRAM Bandwidth Feature Maps

2020-09-18abs ↗pdf ↗

Scalable and Robust Tensor Ring Decomposition for Large-scale Datacs.LG

Scalable and robust TR decomposition for large-scale data with missing entries and outliers.

problem Handling large-scale tensor data with missing entries and outliers.

method Auto-weighted steepest descent method for missing entries and outliers identification, FGMC and RStS strategies.

result Outperforms existing TR decomposition methods in the presence of outliers and runs faster than robust tensor completion algorithms.

Tensor Ring Decomposition Robust Tensor Completion Large-scale Data Missing Entries Outliers

2023-05-15abs ↗pdf ↗

Convolutional Neural Networks with Transformed Input based on Robust Tensor Network Decompositioncs.LG

Tensor network decomposition, originated from quantum physics to model entangled many-particle quantum systems, turns out to be a promising mathematical technique to efficiently represent and process big data in parsimonious manner. In this study, we show that tensor networks can systematically partition structured dat…

2018-11-20abs ↗pdf ↗

Guaranteed Sampling Flexibility for Low-tubal-rank Tensor Completioncs.LG

Introduces t-CCS for flexible tensor sampling.

problem Lack of flexibility in tensor sampling methods.

method Tensor Cross-Concentrated Sampling (t-CCS).

result Effective tensor recovery from t-CCS samples.

tensor completion flexible sampling t-CUR Bernoulli sampling ITCURTC algorithm

2024-06-16abs ↗pdf ↗

AugSplicing: Synchronized Behavior Detection in Streaming Tensorscs.DB

Detects synchronized behavior in streaming data.

problem Tracking synchronized behavior in time-stamped tuples.

method AugSplicing algorithm for streaming dense block detection.

result Effective and robust in detecting anomalous behavior.

streaming tensors synchronized behavior dense blocks app store ranking fraud detection

2020-12-03abs ↗pdf ↗