Pith. sign in

Paper Citation Record · LEDGER

Faster Tensor Train Decomposition for Sparse Data

As of 24 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 0 inbound Pith citation observations for arXiv:1908.02721.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
1908.02721 v2

Coverage vector

measured 28 of 28 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T14:44:08.285481Z

measured 28 of 28 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

28 of 28 outbound references displayed

  • verified exact1
  • verified fuzzy25
  • unresolved2
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 3f06bf98-b159-4dc9-988c-41475b7db7ff · outbound

This paper cites The expression of a tensor or a polyadic as a sum of products.

Faster Tensor Train Decomposition for Sparse Data The expression of a tensor or a polyadic as a sum of products

Reference 1

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T14:44:08.590125Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-14T14:44:08.191640Z digest=sha256:deff36e9447a377d28e1e1dcf1a8526975b36c75088c718032f0d3096d408244

Observation daac341a-4035-4e61-8c1e-804bd4b0994d · outbound

This paper cites Some mathematical notes on three-mode factor analysis.

Faster Tensor Train Decomposition for Sparse Data Some mathematical notes on three-mode factor analysis

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-14T14:44:08.195634Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T14:44:08.195634Z digest=sha256:cc7e7b806d761e667a53385eeb27b6150b6bf083ff9d8ba51992be9b3d5efab0

Observation 8205237e-1e68-4ef8-92fc-c36ec3780621 · outbound

This paper cites Tensor-train decomposition.

Faster Tensor Train Decomposition for Sparse Data Tensor-train decomposition

Reference 3

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T14:44:08.572969Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-14T14:44:08.199057Z digest=sha256:c43815e41c4c59f7b5da2c67f9db40a9f747428d9475d77668b0f11cc8f15327

Observation 6af4177c-641b-4806-8865-714882673315 · outbound

This paper cites Solution of linear systems and ma- trix inversion in the TT-format.

Faster Tensor Train Decomposition for Sparse Data Solution of linear systems and ma- trix inversion in the TT-format

Reference 4

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T14:44:08.563387Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-14T14:44:08.202588Z digest=sha256:78dd7fc279e9b10fe3ec98093b13c1e4072795894adbb7ae7a664e5f22640c4d

Observation 55546d0a-e4a2-4201-90cb-24743eea07a9 · outbound

This paper cites Enabling high-dimensional hierarchical uncertainty quantifi- cation by ANOVA and tensor-train decomposition.

Faster Tensor Train Decomposition for Sparse Data Enabling high-dimensional hierarchical uncertainty quantifi- cation by ANOVA and tensor-train decomposition

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T14:44:08.552775Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-14T14:44:08.206274Z digest=sha256:2ffbbc96c11a58d82cbc30a39d390e2f8851479d89330630560e6d05516f1ce1

Observation e93a6c72-8849-4159-9ce3-559dab59f9d5 · outbound

This paper cites Model reduction and simu- lation of nonlinear circuits via tensor decomposition.

Faster Tensor Train Decomposition for Sparse Data Model reduction and simu- lation of nonlinear circuits via tensor decomposition

Reference 6

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T14:44:08.542464Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-14T14:44:08.209795Z digest=sha256:92772acf1e6255db91ffe0e1c397463423ae0a5b0b45c013f82620ac42c99fda

Observation 282e37da-0688-47de-af12-b909eace802f · outbound

This paper cites Tensor computation: A new framework for high-dimensional problems in EDA.

Faster Tensor Train Decomposition for Sparse Data Tensor computation: A new framework for high-dimensional problems in EDA

Reference 7

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T14:44:08.531299Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-14T14:44:08.213467Z digest=sha256:4b5746b1d85256f805b130f4d09716879784b2beb1cc97b730adaa6564ea0bf0

Observation 71a0545a-4154-448f-9163-24dd81a017b6 · outbound

This paper cites Tensor network alter- nating linear scheme for MIMO Volterra system identification.

Faster Tensor Train Decomposition for Sparse Data Tensor network alter- nating linear scheme for MIMO Volterra system identification

Reference 8

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T14:44:08.520161Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-14T14:44:08.216584Z digest=sha256:adb8848075fa6539a8a989aa0f28c30e0ea9f6403aea149089ad62be13d33391

Observation 0f523925-fdbd-4cae-8afe-a5c5d42c59cc · outbound

This paper cites Approximation of 2 d× 2d matrices using tensor decom- position.

Faster Tensor Train Decomposition for Sparse Data Approximation of 2 d× 2d matrices using tensor decom- position

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T14:44:08.511210Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-14T14:44:08.220127Z digest=sha256:b0726d1b90788b661ce7994603285c388b3444c4675f6ab2aee790edc5690263

Observation db17c826-754a-46b5-8a05-850894fd0bd7 · outbound

This paper cites On low-rank approximability of solutions to high-dimensional operator equations and eigenvalue problems.

Faster Tensor Train Decomposition for Sparse Data On low-rank approximability of solutions to high-dimensional operator equations and eigenvalue problems

Reference 10

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T14:44:08.501248Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-14T14:44:08.223324Z digest=sha256:cb1be631e83a10d6cd5574fe4b93ff942abfe078e1e767e2a8d8f04616b5a3bc

Observation 7945de5b-be83-4134-835d-a018aa927fbf · outbound

This paper cites Computing low-rank approximations of large-scale matrices with the tensor network randomized SVD.

Faster Tensor Train Decomposition for Sparse Data Computing low-rank approximations of large-scale matrices with the tensor network randomized SVD

Reference 11

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T14:44:08.491110Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-14T14:44:08.226674Z digest=sha256:8ed56fbed33536c8f47253457b0fbeffe44e15092690b2273cc83ee5b75e902b

Observation 46b63751-955b-4c34-878c-4982f07aa70a · outbound

This paper cites Efficient low rank tensor ring completion.

Faster Tensor Train Decomposition for Sparse Data Efficient low rank tensor ring completion

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T14:44:08.480947Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-14T14:44:08.230009Z digest=sha256:4734e4d1f06f63e97e0f66c7d4194f9819fd3a1a4d04e8b6a04a439c9a731975

Observation c53d07a6-a1cf-4abd-bd6a-8bd43853d86b · outbound

This paper cites Fast and Accurate Tensor Completion with Total Variation Regularized Tensor Trains.

Faster Tensor Train Decomposition for Sparse Data Fast and Accurate Tensor Completion with Total Variation Regularized Tensor Trains

Reference 13

Resolution
verified exact
local_arxiv, observed 2026-08-14T14:44:08.319598Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-14T14:44:08.234339Z digest=sha256:8441cc2daba6ff4c6009a572da52efb0ee3dfad223d2c3da2bb0aff819a111ca

Observation 3b994df1-0ace-4f59-b03e-fa574286a5fd · outbound

This paper cites Tensor train neigh- borhood preserving embedding.

Faster Tensor Train Decomposition for Sparse Data Tensor train neigh- borhood preserving embedding

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T14:44:08.470825Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-14T14:44:08.238150Z digest=sha256:d42deaa50109e923b7d8a49dec934597114e52b3f0cbaa4075b211630700c703

Observation 53419364-7fc4-4a64-a937-910f5f0f510c · outbound

This paper cites Support vector machine based on low-rank tensor train decomposition for big data applications.

Faster Tensor Train Decomposition for Sparse Data Support vector machine based on low-rank tensor train decomposition for big data applications

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T14:44:08.461063Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-14T14:44:08.241489Z digest=sha256:549069d888f758423978d05ad06b385c6ea59d8cf93b7c2c410ff308da4fd6f9

Observation ab5ad543-5685-49e1-a618-5cfef66d7ba1 · outbound

This paper cites Par- allelized tensor train learning of polynomial classifiers.

Faster Tensor Train Decomposition for Sparse Data Par- allelized tensor train learning of polynomial classifiers

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T14:44:08.451379Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-14T14:44:08.244821Z digest=sha256:ceb2c6226327b89a2a05d5208af2748f3760ae440d1a2a205aa483f4d1111250

Observation 7eb9719b-2bb0-467d-9b18-316017608352 · outbound

This paper cites Whole brain fMRI pattern analysis based on tensor neural network.

Faster Tensor Train Decomposition for Sparse Data Whole brain fMRI pattern analysis based on tensor neural network

Reference 17

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T14:44:08.440379Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-14T14:44:08.248082Z digest=sha256:b641c1b3e3a7bb432dfa56c82052c3d51d494077f1ea13504b33f63d198b0088

Observation b563d1e5-fb54-419f-8906-aa7b557e7d18 · outbound

This paper cites TT-cross approximation for mul- tidimensional arrays.

Faster Tensor Train Decomposition for Sparse Data TT-cross approximation for mul- tidimensional arrays

Reference 18

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T14:44:08.430594Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-14T14:44:08.251450Z digest=sha256:936bda46f4359075e39f087a88d6fd339af6befe77987a097f8636b80b78ff8b

Observation 0579e4ec-39ee-4e50-9c7b-c3c2f802a9bb · outbound

This paper cites Fast adaptive interpolation of multi-dimensional arrays in tensor train format.

Faster Tensor Train Decomposition for Sparse Data Fast adaptive interpolation of multi-dimensional arrays in tensor train format

Reference 19

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T14:44:08.420922Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-14T14:44:08.254547Z digest=sha256:f8c8127ef505579338fb41b632d2d0c7071dda2683c63fa8b4941c4438c48f79

Observation 81d83089-1cc7-41e5-a3a3-36729a642059 · outbound

This paper cites A randomized tensor train singular value decomposition.

Faster Tensor Train Decomposition for Sparse Data A randomized tensor train singular value decomposition

Reference 20

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T14:44:08.411683Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-14T14:44:08.258300Z digest=sha256:21fdbd6865b4a8ca0a225c0930fc693f6c8f6bd431034dc3d4e9e557fa372e45

Observation 488f7003-b92e-41a0-8781-b277f63d62a8 · outbound

This paper cites Finding struc- ture with randomness: Probabilistic algorithms for constructing approxi- mate matrix decompositions.

Faster Tensor Train Decomposition for Sparse Data Finding struc- ture with randomness: Probabilistic algorithms for constructing approxi- mate matrix decompositions

Reference 21

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T14:44:08.401626Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-14T14:44:08.261965Z digest=sha256:e18aed2cd35a17a81a1a6a8a75d567b3a083944dd69166bba645a7a2da165628

Observation 33fb128f-11e9-473a-9cb2-fe03d464869c · outbound

This paper cites A theory of pseudoskeleton approximations.

Faster Tensor Train Decomposition for Sparse Data A theory of pseudoskeleton approximations

Reference 22

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T14:44:08.391762Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-14T14:44:08.264947Z digest=sha256:5b26b3bfd812b7edfe99c4b0216e97bf3c332a4848ffd86cf7a37ff048d35a99

Observation 0a637ae3-86a5-4ce4-b59f-28dbc995a802 · outbound

This paper cites Generic construction of efficient matrix product operators.

Faster Tensor Train Decomposition for Sparse Data Generic construction of efficient matrix product operators

Reference 23

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T14:44:08.382900Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-14T14:44:08.268353Z digest=sha256:f56f974d2e64766022f513a49ca2e0b4420f4d283be5567e12ec073024f6f591

Observation ea5caabd-db72-4ad7-bcb7-ae01ad9ad9ba · outbound

This paper cites Xerus - A general purpose tensor library, 2014–2017.

Faster Tensor Train Decomposition for Sparse Data Xerus - A general purpose tensor library, 2014–2017

Reference 24

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T14:44:08.373275Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-14T14:44:08.271607Z digest=sha256:5dc3eb17b40eb2f214b6066641ae29a8ad5d737b9b586f3d36fdbec9d58874c6

Observation 74ad48fd-5b26-4da5-b1d6-27810a889ec7 · outbound

This paper cites Corrected one-site density matrix renormalization group and alternating minimal energy algorithm.

Faster Tensor Train Decomposition for Sparse Data Corrected one-site density matrix renormalization group and alternating minimal energy algorithm

Reference 25

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T14:44:08.363249Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-14T14:44:08.274797Z digest=sha256:332075ff2baf5f3664a69457737e77f4585e431dfa3e6259beec372595cd1e13

Observation 78d9ea55-725f-480b-a407-a18268fe826f · outbound

This paper cites SNAP Datasets: Stanford large network dataset collection, June 2015.

Faster Tensor Train Decomposition for Sparse Data SNAP Datasets: Stanford large network dataset collection, June 2015

Reference 26

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T14:44:08.353058Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-14T14:44:08.279182Z digest=sha256:66131ab429a29753564a09f7c0de3deb0afd0d0098af83cb686ad17483896772

Observation 790b60c7-d0ec-4ec4-b5ca-e3f4787c36b9 · outbound

This paper cites an unresolved cited work.

Faster Tensor Train Decomposition for Sparse Data Unresolved cited work

Reference 27

Resolution
unresolved
raw_fallback, observed 2026-08-14T14:44:08.341298Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-14T14:44:08.282464Z digest=sha256:cb14c37da6ff3cbbe00b744986e74ed09d50cdf8312cd504331f12ba94d8e566

Observation b99329d1-a54a-4ba6-917b-3ae20744fc47 · outbound

This paper cites Introduction to Algorithms.

Faster Tensor Train Decomposition for Sparse Data Introduction to Algorithms

Reference 28

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T14:44:08.330748Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-14T14:44:08.285481Z digest=sha256:89ca1e0326ec0790537a13aa36cc95a5abd7ab901f083692f38e1edd0d440862

Pith citing papers

No inbound Pith citation observations are available.