Paper Citation Record · LEDGER

HalfNet: Randomized Neural Networks with Learned Subspace Geometry

As of 9 August 2026, this Paper Citation Record lists 24 of 24 outbound references and 1 inbound Pith citation observation for arXiv:2606.04583.

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

pith.paper-citation-record.v1
2606.04583 v1

Coverage vector

measured 24 of 24 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-28T07:34:30.802855Z

measured 25 of 25 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-28T07:34:30.802855Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-02T06:06:41.570593Z

Reference resolution

24 of 24 outbound references displayed

verified exact2
verified fuzzy0
unresolved22
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a136a387-0387-4576-8d8b-525db3f82742 · outbound

This paper cites This leads to a simpler model and a simpler optimization problem.

HalfNet: Randomized Neural Networks with Learned Subspace Geometry This leads to a simpler model and a simpler optimization problem

Reference 1

Resolution: unresolved; no resolver link, observed 2026-06-28T07:34:30.802855Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 633fa8dd-3518-40a9-ba52-2526d741c680 · outbound

This paper cites an unresolved cited work.

HalfNet: Randomized Neural Networks with Learned Subspace Geometry Unresolved cited work

Reference 2

Resolution: unresolved; no resolver link, observed 2026-06-28T07:34:30.802855Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 728c8859-d37a-490c-971c-2ece4e1ddb05 · outbound

This paper cites an unresolved cited work.

HalfNet: Randomized Neural Networks with Learned Subspace Geometry Unresolved cited work

Reference 3

Resolution: unresolved; no resolver link, observed 2026-06-28T07:34:30.802855Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 84a25787-445b-44a9-9345-94291f6504ba · outbound

This paper cites an unresolved cited work.

HalfNet: Randomized Neural Networks with Learned Subspace Geometry Unresolved cited work

Reference 4

Resolution: unresolved; no resolver link, observed 2026-06-28T07:34:30.802855Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 280ad27f-b404-4fe3-a59a-36ea2b1228d7 · outbound

This paper cites This paper is organized as follows: We present our model in Section 2 and our experimental results on MNIST in Sec- tion 3.

HalfNet: Randomized Neural Networks with Learned Subspace Geometry This paper is organized as follows: We present our model in Section 2 and our experimental results on MNIST in Sec- tion 3

Reference 5

Resolution: unresolved; no resolver link, observed 2026-06-28T07:34:30.802855Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 75b3c28b-0c97-4b99-aac7-af4ca3bc34b5 · outbound

This paper cites HalfNet: Randomized Neural Networks with Learned Subspace Geometry.

HalfNet: Randomized Neural Networks with Learned Subspace Geometry HalfNet: Randomized Neural Networks with Learned Subspace Geometry

Reference 6

Resolution: verified exact; local_arxiv, observed 2026-07-02T06:06:41.571805Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b4824e2f-694a-4e9b-a146-ca0af319638b · outbound

This paper cites We use ReLU as the activation function at the hidden units and softmax at the output; training is done to minimize the cross-entropy using Adam.

HalfNet: Randomized Neural Networks with Learned Subspace Geometry We use ReLU as the activation function at the hidden units and softmax at the output; training is done to minimize the cross-entropy using Adam

Reference 7

Resolution: unresolved; no resolver link, observed 2026-06-28T07:34:30.802855Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8c91ec14-36e2-4c16-945a-fd6c64d5988d · outbound

This paper cites We can generate binary hyperplanes by placing the sign function after Equation 3.

HalfNet: Randomized Neural Networks with Learned Subspace Geometry We can generate binary hyperplanes by placing the sign function after Equation 3

Reference 8

Resolution: unresolved; no resolver link, observed 2026-06-28T07:34:30.802855Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bd528f8f-cf18-4179-a8b6-26a302f8d082 · outbound

This paper cites CIFAR-10 consists of 60,000 color images of size32×32belonging to ten classes, with 50,000 training and 10,000 test instances.

HalfNet: Randomized Neural Networks with Learned Subspace Geometry CIFAR-10 consists of 60,000 color images of size32×32belonging to ten classes, with 50,000 training and 10,000 test instances

Reference 9

Resolution: unresolved; no resolver link, observed 2026-06-28T07:34:30.802855Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ba99c224-9194-4eca-8c9b-74a5b71ed41d · outbound

This paper cites Empirical and theoretical stud- ies have shown that deep networks often learn representa- tions that are effectively low-dimensional [7, 8].

HalfNet: Randomized Neural Networks with Learned Subspace Geometry Empirical and theoretical stud- ies have shown that deep networks often learn representa- tions that are effectively low-dimensional [7, 8]

Reference 10

Resolution: unresolved; no resolver link, observed 2026-06-28T07:34:30.802855Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 733797aa-f788-4cff-b18f-1271773268e9 · outbound

This paper cites The rank of the co- variance factor provides a direct control over model capacity, yielding a smooth trade-off between fixed random features and fully trainable neural networks.

HalfNet: Randomized Neural Networks with Learned Subspace Geometry The rank of the co- variance factor provides a direct control over model capacity, yielding a smooth trade-off between fixed random features and fully trainable neural networks

Reference 11

Resolution: unresolved; no resolver link, observed 2026-06-28T07:34:30.802855Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1949b9b6-1162-4629-b0f6-c632527dd4a2 · outbound

This paper cites Random features for large- scale kernel machines,.

HalfNet: Randomized Neural Networks with Learned Subspace Geometry Random features for large- scale kernel machines,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-06-28T07:34:30.802855Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 08e4efd5-0a15-4546-95c7-c5ed4fb0f16d · outbound

This paper cites Extreme learning machine for regression and multiclass classifi- cation,.

HalfNet: Randomized Neural Networks with Learned Subspace Geometry Extreme learning machine for regression and multiclass classifi- cation,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-06-28T07:34:30.802855Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cc7a7057-c686-4a34-bbfb-717113e05083 · outbound

This paper cites A review on neural networks with random weights,.

HalfNet: Randomized Neural Networks with Learned Subspace Geometry A review on neural networks with random weights,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-06-28T07:34:30.802855Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f9039579-1295-4358-8302-067a98c6123b · outbound

This paper cites Randomness in neural net- works: An overview,.

HalfNet: Randomized Neural Networks with Learned Subspace Geometry Randomness in neural net- works: An overview,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-06-28T07:34:30.802855Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f5232e9d-c0cd-4146-bc40-ffa9debf7fe6 · outbound

This paper cites Deep randomized neural networks,.

HalfNet: Randomized Neural Networks with Learned Subspace Geometry Deep randomized neural networks,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-06-28T07:34:30.802855Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 91e46d0f-9fa6-46e8-951b-b358cfa4ceac · outbound

This paper cites Half-Layered Neural Networks.

HalfNet: Randomized Neural Networks with Learned Subspace Geometry Half-Layered Neural Networks

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-07-02T06:06:41.574378Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c7e7a2be-6825-4280-b3b9-ca294e05303b · outbound

This paper cites The low-rank simplicity bias in deep networks,.

HalfNet: Randomized Neural Networks with Learned Subspace Geometry The low-rank simplicity bias in deep networks,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-06-28T07:34:30.802855Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8d1e2d33-e42e-47de-9e0f-e90a41962d0b · outbound

This paper cites Toward moderate overparameterization: Global convergence guarantees for training shallow neural networks,.

HalfNet: Randomized Neural Networks with Learned Subspace Geometry Toward moderate overparameterization: Global convergence guarantees for training shallow neural networks,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-06-28T07:34:30.802855Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7ff2e391-ef7a-48dc-930b-d5bf3cd57728 · outbound

This paper cites Pre- dicting parameters in deep learning,.

HalfNet: Randomized Neural Networks with Learned Subspace Geometry Pre- dicting parameters in deep learning,

Reference 20

Resolution: unresolved; no resolver link, observed 2026-06-28T07:34:30.802855Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9171a0a1-cf6b-469c-b9e9-e0140440cbe1 · outbound

This paper cites Speeding up convolutional neural networks with low rank expansions,.

HalfNet: Randomized Neural Networks with Learned Subspace Geometry Speeding up convolutional neural networks with low rank expansions,

Reference 21

Resolution: unresolved; no resolver link, observed 2026-06-28T07:34:30.802855Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 928a648b-80a7-4873-af92-e6fd5fcece6d · outbound

This paper cites Sharp analysis of low-rank kernel ma- trix approximations,.

HalfNet: Randomized Neural Networks with Learned Subspace Geometry Sharp analysis of low-rank kernel ma- trix approximations,

Reference 22

Resolution: unresolved; no resolver link, observed 2026-06-28T07:34:30.802855Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 675e3114-3294-441b-92f8-06633fb0ae89 · outbound

This paper cites Neal,Bayesian Learning for Neural Net- works, Springer, 1996.

HalfNet: Randomized Neural Networks with Learned Subspace Geometry Neal,Bayesian Learning for Neural Net- works, Springer, 1996

Reference 23

Resolution: unresolved; no resolver link, observed 2026-06-28T07:34:30.802855Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1c600211-9abb-4518-aff4-0a95bafbeb7c · outbound

This paper cites Deep neural networks as gaus- sian processes,.

HalfNet: Randomized Neural Networks with Learned Subspace Geometry Deep neural networks as gaus- sian processes,

Reference 24

Resolution: unresolved; no resolver link, observed 2026-06-28T07:34:30.802855Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 75b3c28b-0c97-4b99-aac7-af4ca3bc34b5 · inbound