Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Speeding up Convolutional Neural Networks with Low Rank Expansions
As of 4 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:1405.3866.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-04T06:34:03.388597+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-06-28T06:53:07.296863Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-07-02T22:57:26.748490Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation 2a8d11cf-ef3b-499e-826d-c2f0c25fa3b3 · inbound
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications cites this paper.
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications Speeding up Convolutional Neural Networks with Low Rank Expansions
Reference 14
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T02:50:40.353109Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation a6a07106-6e7b-4e86-abb2-a6911a35feee · inbound
LoRA: Low-Rank Adaptation of Large Language Models cites this paper.
LoRA: Low-Rank Adaptation of Large Language Models Speeding up Convolutional Neural Networks with Low Rank Expansions
Reference 23
- Resolution
- verified exact
- arxiv_id, observed 2026-05-09T05:01:40.674378Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation eec05eea-3be3-458e-ae32-286548a38234 · inbound
ASVD: Activation-aware Singular Value Decomposition for Compressing Large Language Models cites this paper.
ASVD: Activation-aware Singular Value Decomposition for Compressing Large Language Models Speeding up Convolutional Neural Networks with Low Rank Expansions
Reference 12
- Resolution
- verified exact
- local_arxiv, observed 2026-05-20T13:49:33.846268Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation bc0c2bb3-0b64-4ada-81b1-2a11193d5a3e · inbound
Reclaiming Residual Knowledge: A Novel Paradigm to Low-Bit Quantization cites this paper.
Reclaiming Residual Knowledge: A Novel Paradigm to Low-Bit Quantization Speeding up Convolutional Neural Networks with Low Rank Expansions
Reference 15
- Resolution
- verified exact
- local_arxiv, observed 2026-05-23T22:28:31.091364Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 35c2bafa-8581-4106-b7ba-9b72d5bdd7c6 · inbound
FedSpy-LLM: Towards Scalable and Generalizable Data Reconstruction Attacks from Gradients on LLMs cites this paper.
FedSpy-LLM: Towards Scalable and Generalizable Data Reconstruction Attacks from Gradients on LLMs Speeding up Convolutional Neural Networks with Low Rank Expansions
Reference 30
- Resolution
- verified exact
- arxiv_id, observed 2026-05-10T23:30:51.389404Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 454aa3e5-88ef-4672-a17e-760454ef77bb · inbound
Hierarchical Spatio-Channel Clustering for Efficient Model Compression in Medical Image Analysis cites this paper.
Hierarchical Spatio-Channel Clustering for Efficient Model Compression in Medical Image Analysis Speeding up Convolutional Neural Networks with Low Rank Expansions
Reference 9
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T20:36:10.456354Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 1bfa614d-8084-4c67-b229-6ee3da4cf91b · inbound
Importance-Guided Basis Selection for Low-Rank Decomposition of Large Language Models cites this paper.
Importance-Guided Basis Selection for Low-Rank Decomposition of Large Language Models Speeding up Convolutional Neural Networks with Low Rank Expansions
Reference 14
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T16:56:06.736715Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 6832371b-f4d3-4ab4-8945-752d52d21dc5 · inbound
Recent Advances and Trends in Learning-based 3D Representations cites this paper.
Recent Advances and Trends in Learning-based 3D Representations Speeding up Convolutional Neural Networks with Low Rank Expansions
Reference 112
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-07-02T07:36:44.807864Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 3ac16a52-80b3-40fe-ad10-7de7ced2a15d · inbound
EinSort: Sorting is All We Need for Tensorizing LLM cites this paper.
EinSort: Sorting is All We Need for Tensorizing LLM Speeding up Convolutional Neural Networks with Low Rank Expansions
Reference 40
- Resolution
- verified exact
- local_arxiv, observed 2026-07-02T22:57:26.749992Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.