Paper Citation Record · LEDGER

Secure and Storage-Efficient Deep Learning Models for Edge AI Using Automatic Weight Generation

As of 21 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 0 inbound Pith citation observations for arXiv:2507.06380.

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

pith.paper-citation-record.v1
2507.06380 v1

Coverage vector

measured 28 of 28 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T19:13:01.820101Z

measured 28 of 28 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+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 exact0
verified fuzzy18
unresolved10
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 7c217ea7-5261-4d7d-ac6c-4b3dd51bca97 · outbound

This paper cites Eie: Efficient inference engine on compressed deep neural network,.

Secure and Storage-Efficient Deep Learning Models for Edge AI Using Automatic Weight Generation Eie: Efficient inference engine on compressed deep neural network,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:13:02.296140Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 04641fc7-5f34-4d44-856e-b7f171380d75 · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Secure and Storage-Efficient Deep Learning Models for Edge AI Using Automatic Weight Generation BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-06T19:13:01.698807Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 34fb2d6f-e4ba-4c75-8b98-dd0fea7cffe5 · outbound

This paper cites Language Models are Few-Shot Learners.

Secure and Storage-Efficient Deep Learning Models for Edge AI Using Automatic Weight Generation Language Models are Few-Shot Learners

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-06T19:13:01.704191Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 07dd447e-efa5-4b35-87b9-5c724e854dc8 · outbound

This paper cites Deep residual learning for image recognition,.

Secure and Storage-Efficient Deep Learning Models for Edge AI Using Automatic Weight Generation Deep residual learning for image recognition,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:13:02.281107Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation f83c0eea-22b0-4f71-a1b7-ce071d91ec34 · outbound

This paper cites Xnor- net: Imagenet classification using binary convolutional neu- ral networks,.

Secure and Storage-Efficient Deep Learning Models for Edge AI Using Automatic Weight Generation Xnor- net: Imagenet classification using binary convolutional neu- ral networks,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:13:02.265640Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation beb5fc3d-5fae-453a-8b81-133fa10deae7 · outbound

This paper cites To prune, or not to prune: exploring the efficacy of pruning for model compression.

Secure and Storage-Efficient Deep Learning Models for Edge AI Using Automatic Weight Generation To prune, or not to prune: exploring the efficacy of pruning for model compression

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-06T19:13:01.718617Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 66e56dd8-4eeb-47fd-944c-72311774dc6b · outbound

This paper cites Low-rank matrix factorization for deep neu- ral network training with high-dimensional output targets,.

Secure and Storage-Efficient Deep Learning Models for Edge AI Using Automatic Weight Generation Low-rank matrix factorization for deep neu- ral network training with high-dimensional output targets,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:13:02.250862Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 4fc27185-6b63-4bba-ace9-feec2a734394 · outbound

This paper cites MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications.

Secure and Storage-Efficient Deep Learning Models for Edge AI Using Automatic Weight Generation MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-06T19:13:01.733030Z

Source-reported events for the cited work

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Observation 2a323e59-7fcb-4c09-b88c-733fd01a7284 · outbound

This paper cites SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size.

Secure and Storage-Efficient Deep Learning Models for Edge AI Using Automatic Weight Generation SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T19:13:01.737548Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 68f923d3-50d0-4724-b333-6c404ef08f60 · outbound

This paper cites HyperNetworks.

Secure and Storage-Efficient Deep Learning Models for Edge AI Using Automatic Weight Generation HyperNetworks

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T19:13:01.741955Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0bd3f7a9-af6e-493b-a586-57488ed08aff · outbound

This paper cites Adaptive weight compression for memory-efficient neural net- works,.

Secure and Storage-Efficient Deep Learning Models for Edge AI Using Automatic Weight Generation Adaptive weight compression for memory-efficient neural net- works,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:13:02.221342Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 34ebeef6-b9ec-46be-b591-08ebd593898c · outbound

This paper cites Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1.

Secure and Storage-Efficient Deep Learning Models for Edge AI Using Automatic Weight Generation Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-06T19:13:01.750947Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7b914718-3074-4635-b75f-71802163e1f0 · outbound

This paper cites Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding.

Secure and Storage-Efficient Deep Learning Models for Edge AI Using Automatic Weight Generation Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-06T19:13:01.756224Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 80751ea6-48ee-4047-b8da-e3ac80621412 · outbound

This paper cites Flipping bits in memory without accessing them: An experimental study of dram disturbance errors,.

Secure and Storage-Efficient Deep Learning Models for Edge AI Using Automatic Weight Generation Flipping bits in memory without accessing them: An experimental study of dram disturbance errors,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:13:02.206120Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation a9884024-4801-4554-b69e-648a38885527 · outbound

This paper cites Exploiting correcting codes: On the effectiveness of ecc memory against rowhammer attacks,.

Secure and Storage-Efficient Deep Learning Models for Edge AI Using Automatic Weight Generation Exploiting correcting codes: On the effectiveness of ecc memory against rowhammer attacks,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:13:02.190504Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation affa8d60-6e8c-4329-be1a-08480e3b76d9 · outbound

This paper cites Defending bit-flip attack through dnn weight reconstruction,.

Secure and Storage-Efficient Deep Learning Models for Edge AI Using Automatic Weight Generation Defending bit-flip attack through dnn weight reconstruction,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:13:02.175153Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 756f73bf-e189-4d88-af77-5b972da417e9 · outbound

This paper cites Liii. on lines and planes of closest fit to systems of points in space,.

Secure and Storage-Efficient Deep Learning Models for Edge AI Using Automatic Weight Generation Liii. on lines and planes of closest fit to systems of points in space,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:13:02.159410Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation e6e41732-0d9a-4b0d-a8cc-de899afb8bc2 · outbound

This paper cites A tutorial on support vector regression,.

Secure and Storage-Efficient Deep Learning Models for Edge AI Using Automatic Weight Generation A tutorial on support vector regression,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:13:02.143879Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 6a099c69-7759-4d2c-9019-e7f812812075 · outbound

This paper cites Secure ai systems: Emerging threats and defense mechanisms,.

Secure and Storage-Efficient Deep Learning Models for Edge AI Using Automatic Weight Generation Secure ai systems: Emerging threats and defense mechanisms,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:13:02.128643Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation b4821d4e-6446-4646-bc10-ff045042a6ec · outbound

This paper cites Bit-flip attack: Crushing neural network with progressive bit search,.

Secure and Storage-Efficient Deep Learning Models for Edge AI Using Automatic Weight Generation Bit-flip attack: Crushing neural network with progressive bit search,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:13:02.112763Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 4344f63a-c289-4aac-a3c2-ae7a5de486db · outbound

This paper cites Low precision arithmetic for deep learning,.

Secure and Storage-Efficient Deep Learning Models for Edge AI Using Automatic Weight Generation Low precision arithmetic for deep learning,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:13:02.095383Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 2cdf8adf-2fc1-4b32-9fac-9f8c2fb3ac91 · outbound

This paper cites an unresolved cited work.

Secure and Storage-Efficient Deep Learning Models for Edge AI Using Automatic Weight Generation Unresolved cited work

Reference 23

Resolution: unresolved; raw_fallback, observed 2026-08-06T19:13:02.079139Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 05ff7f0b-458f-45ea-b099-a2223af92326 · outbound

This paper cites Eyeriss: An energy-efficient reconfigurable accelerator for deep convo- lutional neural networks,.

Secure and Storage-Efficient Deep Learning Models for Edge AI Using Automatic Weight Generation Eyeriss: An energy-efficient reconfigurable accelerator for deep convo- lutional neural networks,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:13:02.062416Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 1b3bd218-4f4b-476a-9751-32dfd834065f · outbound

This paper cites Evolving neural networks in compressed weight space,.

Secure and Storage-Efficient Deep Learning Models for Edge AI Using Automatic Weight Generation Evolving neural networks in compressed weight space,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:13:02.045037Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 421dd2c6-aa5e-4726-a8f9-7ed9d8b703d7 · outbound

This paper cites Compressing convolutional neural networks in the frequency domain,.

Secure and Storage-Efficient Deep Learning Models for Edge AI Using Automatic Weight Generation Compressing convolutional neural networks in the frequency domain,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:13:02.235747Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation c6273426-5331-4e98-b752-4482581a8390 · outbound

This paper cites Simplifying deep neural networks for neuromorphic architectures,.

Secure and Storage-Efficient Deep Learning Models for Edge AI Using Automatic Weight Generation Simplifying deep neural networks for neuromorphic architectures,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:13:02.028694Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 30127c5a-9b5b-4ace-b64d-26741aca10df · outbound

This paper cites an unresolved cited work.

Secure and Storage-Efficient Deep Learning Models for Edge AI Using Automatic Weight Generation Unresolved cited work

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-06T19:13:01.815597Z

Source-reported events for the cited work

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Observation f0059712-068d-4831-9b1f-971e7d65ec13 · outbound

This paper cites Low precision arithmetic for deep learning.

Secure and Storage-Efficient Deep Learning Models for Edge AI Using Automatic Weight Generation Low precision arithmetic for deep learning

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:13:02.000381Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Pith citing papers

No inbound Pith citation observations are available.