Paper Citation Record · LEDGER

RandNet: deep learning with compressed measurements of images

As of 16 August 2026, this Paper Citation Record lists 20 of 20 outbound references and 1 inbound Pith citation observation for arXiv:1908.09258.

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

pith.paper-citation-record.v1
1908.09258 v1

Coverage vector

measured 20 of 20 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T11:22:49.954571Z

measured 21 of 21 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-14T11:22:49.884343Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-14T11:22:50.017622Z

Reference resolution

20 of 20 outbound references displayed

verified exact2
verified fuzzy16
unresolved2
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6d77659f-6b68-4e90-bdec-626f3e773c12 · outbound

This paper cites In signal processing, dictionary learning (DL) [1] is the de facto method for learning adaptive data rep- resentations.

RandNet: deep learning with compressed measurements of images In signal processing, dictionary learning (DL) [1] is the de facto method for learning adaptive data rep- resentations

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:22:50.221169Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation ed7b1354-6e5c-46d0-8ee8-9c32009d3234 · outbound

This paper cites RandNet: deep learning with compressed measurements of images.

RandNet: deep learning with compressed measurements of images RandNet: deep learning with compressed measurements of images

Reference 2

Resolution: verified exact; local_arxiv, observed 2026-08-14T11:22:50.022035Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 937b62f4-2689-4360-9e4e-b17ca69b2918 · outbound

This paper cites First, we in- troduce the architecture in unsupervised and supervised set- tings.

RandNet: deep learning with compressed measurements of images First, we in- troduce the architecture in unsupervised and supervised set- tings

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:22:50.209576Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 45c9b1f2-7984-4688-b6e1-d2482ad7f7a4 · outbound

This paper cites G 0.1” stands for RandNet with Gaussian Φ andβ = 0.1. “S 0.5.

RandNet: deep learning with compressed measurements of images G 0.1” stands for RandNet with Gaussian Φ andβ = 0.1. “S 0.5

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:22:50.198304Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 5bf1acc0-3b15-44ee-848f-ea4928ceeb72 · outbound

This paper cites Speciﬁcally, we introduced RandNet, a class of networks that, in the unsupervised setting, per- forms dictionary learning from random projections of the original data.

RandNet: deep learning with compressed measurements of images Speciﬁcally, we introduced RandNet, a class of networks that, in the unsupervised setting, per- forms dictionary learning from random projections of the original data

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:22:50.186834Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 27b76fa5-8c4c-4227-ae02-48dcac566551 · outbound

This paper cites an unresolved cited work.

RandNet: deep learning with compressed measurements of images Unresolved cited work

Reference 6

Resolution: unresolved; raw_fallback, observed 2026-08-14T11:22:50.175167Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation ea38ccd3-3954-43fc-a53c-efcc4959a698 · outbound

This paper cites Learning sparsely used overcomplete dic- tionaries via alternating minimization,.

RandNet: deep learning with compressed measurements of images Learning sparsely used overcomplete dic- tionaries via alternating minimization,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:22:50.163783Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 2f711987-a644-47cc-984c-17bb8060d614 · outbound

This paper cites Scalable convo- lutional dictionary learning with constrained recurrent sparse auto-encoders,.

RandNet: deep learning with compressed measurements of images Scalable convo- lutional dictionary learning with constrained recurrent sparse auto-encoders,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:22:50.151571Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 4d126f4d-20aa-4af8-a653-df07c43e4674 · outbound

This paper cites Deep Residual Autoencoders for Expectation Maximization-inspired Dictionary Learning.

RandNet: deep learning with compressed measurements of images Deep Residual Autoencoders for Expectation Maximization-inspired Dictionary Learning

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-08-14T11:22:50.004770Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation ff628603-9ce8-4159-97b9-ce5bc2248bfc · outbound

This paper cites Convolutional neu- ral networks analyzed via convolutional sparse coding,.

RandNet: deep learning with compressed measurements of images Convolutional neu- ral networks analyzed via convolutional sparse coding,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:22:50.139140Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation e3a91abe-3bc7-47fb-b998-0dacc2353441 · outbound

This paper cites Deeply-Sparse Signal rePresentations ($\text{D}\text{S}^2\text{P}$).

RandNet: deep learning with compressed measurements of images Deeply-Sparse Signal rePresentations ($\text{D}\text{S}^2\text{P}$)

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-14T11:22:49.921483Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5cc4392b-b387-4796-8c66-2af447806d5d · outbound

This paper cites Memory and computation efﬁcient pca via very sparse random pro- jections,.

RandNet: deep learning with compressed measurements of images Memory and computation efﬁcient pca via very sparse random pro- jections,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:22:50.128416Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation a52c8c49-6169-40bf-b731-3cd571e8fb6f · outbound

This paper cites Efﬁcient dictionary learning via very sparse random projections,.

RandNet: deep learning with compressed measurements of images Efﬁcient dictionary learning via very sparse random projections,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:22:50.116923Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation ad244359-2c08-4bfd-9da3-4b0b4e618a51 · outbound

This paper cites Gradient-based learning applied to document recogni- tion,.

RandNet: deep learning with compressed measurements of images Gradient-based learning applied to document recogni- tion,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:22:50.105539Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 29bcf7d5-02c6-4c10-9a5c-9bf014149680 · outbound

This paper cites Compressive k-svd,.

RandNet: deep learning with compressed measurements of images Compressive k-svd,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:22:50.094530Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 70ac217d-c31b-4eb2-b6b2-b958cb6341cc · outbound

This paper cites A fast iterative shrinkage- thresholding algorithm for linear inverse problems,.

RandNet: deep learning with compressed measurements of images A fast iterative shrinkage- thresholding algorithm for linear inverse problems,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:22:50.083661Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation e9c243bd-e2c2-45e9-82ea-d79156d92798 · outbound

This paper cites Discriminative recurrent sparse auto-encoders,.

RandNet: deep learning with compressed measurements of images Discriminative recurrent sparse auto-encoders,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:22:50.071763Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation a42b7940-7ae0-4be9-bcf4-d3d00104c843 · outbound

This paper cites Atomic decomposition by basis pursuit,.

RandNet: deep learning with compressed measurements of images Atomic decomposition by basis pursuit,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:22:50.059011Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 2999a761-ce08-4560-8ca8-612705506cc4 · outbound

This paper cites Supervised dictionary learning,.

RandNet: deep learning with compressed measurements of images Supervised dictionary learning,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:22:50.046982Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation add00386-ed4e-4ab7-bc19-055032812c18 · outbound

This paper cites The restricted isometry property and its implications for compressed sensing,.

RandNet: deep learning with compressed measurements of images The restricted isometry property and its implications for compressed sensing,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:22:50.034630Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Pith citing papers

Observation ed7b1354-6e5c-46d0-8ee8-9c32009d3234 · inbound