Paper Citation Record · LEDGER

Building Efficient Lightweight CNN Models

As of 18 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 0 inbound Pith citation observations for arXiv:2501.15547.

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

pith.paper-citation-record.v1
2501.15547 v1

Coverage vector

measured 27 of 27 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T14:15:23.971549Z

measured 27 of 27 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+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

27 of 27 outbound references displayed

verified exact3
verified fuzzy5
unresolved18
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c6dbc002-e3a2-4e75-9256-3509014bc558 · outbound

This paper cites TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems.

Building Efficient Lightweight CNN Models TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-10T14:15:23.849031Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 92d4011f-fa79-4759-89c4-c0567d84d892 · outbound

This paper cites Practical recommendations for gradient-based training of deep architectures.

Building Efficient Lightweight CNN Models Practical recommendations for gradient-based training of deep architectures

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-10T14:15:23.854332Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 80b11d5b-1f86-4518-8e85-e26e6b7f4188 · outbound

This paper cites Energy and Policy Considerations for Deep Learning in NLP.

Building Efficient Lightweight CNN Models Energy and Policy Considerations for Deep Learning in NLP

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-10T14:15:23.859159Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9d369a8d-2d64-43ed-a5b7-bd66fc9d0af3 · outbound

This paper cites Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluation.

Building Efficient Lightweight CNN Models Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluation

Reference 4

Resolution: verified exact; local_arxiv, observed 2026-08-10T14:15:24.597633Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e5aae9ce-d50f-4d46-9920-5de310a600b5 · outbound

This paper cites an unresolved cited work.

Building Efficient Lightweight CNN Models Unresolved cited work

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-10T14:15:23.869869Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c369e4b6-f9e9-4a7b-b1bf-570f848b1bd9 · outbound

This paper cites Reducing model complexity in neural networks by using pyramid training approaches.

Building Efficient Lightweight CNN Models Reducing model complexity in neural networks by using pyramid training approaches

Reference 6

Resolution: verified exact; doi, observed 2026-08-10T14:15:24.093977Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e701cb2c-cb43-4b93-9a1d-8c3e7c52c795 · outbound

This paper cites Learning both Weights and Connections for Efficient Neural Networks.

Building Efficient Lightweight CNN Models Learning both Weights and Connections for Efficient Neural Networks

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-10T14:15:23.880389Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2361b84a-49d3-42ca-a89d-6ffad699f564 · outbound

This paper cites Universal Language Model Fine-tuning for Text Classification.

Building Efficient Lightweight CNN Models Universal Language Model Fine-tuning for Text Classification

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-10T14:15:23.885109Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ce0fbe07-d78d-48c0-8a35-6394a097bf14 · outbound

This paper cites Densely Connected Convolutional Networks.

Building Efficient Lightweight CNN Models Densely Connected Convolutional Networks

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-10T14:15:23.890026Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9bc021f0-67c1-454a-a99a-a141d8ed1253 · outbound

This paper cites Quantization and training of neural networks for efficient integer-arithmetic-only inference.

Building Efficient Lightweight CNN Models Quantization and training of neural networks for efficient integer-arithmetic-only inference

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-10T14:15:23.894717Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 42088542-7f6b-4a37-9772-e0c9ad5824eb · outbound

This paper cites Efficient gpu usage tips.

Building Efficient Lightweight CNN Models Efficient gpu usage tips

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:15:24.725568Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 7be8de0a-586e-4ad0-b434-123f48082b21 · outbound

This paper cites A study of cross-validation and bootstrap for accuracy estimation and model selection.

Building Efficient Lightweight CNN Models A study of cross-validation and bootstrap for accuracy estimation and model selection

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:15:24.710137Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d021e134-c43c-4494-8609-790aeac2663c · outbound

This paper cites Imagenet classification with deep convolutional neural networks.

Building Efficient Lightweight CNN Models Imagenet classification with deep convolutional neural networks

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-10T14:15:23.908905Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ea5be402-f2a6-4437-ab0e-e99b9a17df48 · outbound

This paper cites How to Fine-Tune Vision Models with SGD.

Building Efficient Lightweight CNN Models How to Fine-Tune Vision Models with SGD

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-10T14:15:23.913540Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 965d9bf5-ae1e-4133-a44d-1e3d46716196 · outbound

This paper cites Gradient-based learning applied to document recognition.

Building Efficient Lightweight CNN Models Gradient-based learning applied to document recognition

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-10T14:15:23.918177Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7efd19bb-96d0-4565-9222-9bed8cef6dca · outbound

This paper cites Retraining-Based Iterative Weight Quantization for Deep Neural Networks.

Building Efficient Lightweight CNN Models Retraining-Based Iterative Weight Quantization for Deep Neural Networks

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-10T14:15:23.922782Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 86e2b82c-6632-4926-83b6-552ca5cc5832 · outbound

This paper cites Lightweight Deep Learning for Resource-Constrained Environments: A Survey.

Building Efficient Lightweight CNN Models Lightweight Deep Learning for Resource-Constrained Environments: A Survey

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-10T14:15:23.927846Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f5f0a724-0f2c-462a-9925-47c4a8e3673d · outbound

This paper cites Fully Convolutional Networks for Semantic Segmentation.

Building Efficient Lightweight CNN Models Fully Convolutional Networks for Semantic Segmentation

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-10T14:15:23.932692Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 529c1293-cb51-4a37-91ff-550a46f589e4 · outbound

This paper cites Maji and R.

Building Efficient Lightweight CNN Models Maji and R

Reference 19

Resolution: verified exact; doi, observed 2026-08-10T14:15:24.054793Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation eec0e5e3-549e-455c-acc5-4f3ad6c16b04 · outbound

This paper cites A survey on transfer learning.

Building Efficient Lightweight CNN Models A survey on transfer learning

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-10T14:15:23.942516Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9bc2d08a-e64a-447e-97c6-510ac25455d3 · outbound

This paper cites Khoshgoftaar.

Building Efficient Lightweight CNN Models Khoshgoftaar

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-10T14:15:23.947161Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a67eb6fe-c4f9-4cd0-b2b2-0c0adc759f43 · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

Building Efficient Lightweight CNN Models Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-10T14:15:23.951302Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d1d87ba6-f4f7-409b-9584-891a9f403832 · outbound

This paper cites Dropout: A simple way to prevent neural networks from overfitting.

Building Efficient Lightweight CNN Models Dropout: A simple way to prevent neural networks from overfitting

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:15:24.693936Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 56f071d1-46b8-4e46-b019-d224206c6492 · outbound

This paper cites Rethinking the inception architecture for computer vision.

Building Efficient Lightweight CNN Models Rethinking the inception architecture for computer vision

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-10T14:15:23.959762Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 13757034-3949-47a5-94cc-d12c35c240ca · outbound

This paper cites Gpu kernel-tesla p100 gpu.

Building Efficient Lightweight CNN Models Gpu kernel-tesla p100 gpu

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:15:24.677923Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 0fbcc01e-278c-455b-81e2-f0e76520bfbc · outbound

This paper cites Lcrm: Layer-wise complexity reduction method for cnn model optimization on end devices.

Building Efficient Lightweight CNN Models Lcrm: Layer-wise complexity reduction method for cnn model optimization on end devices

Reference 26

Resolution: metadata mismatch; raw_fallback, observed 2026-08-10T14:15:24.208119Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 80c924a0-a359-48e5-80ac-44bdd34a9b4b · outbound

This paper cites The correct way to measure inference time of deep neural networks.

Building Efficient Lightweight CNN Models The correct way to measure inference time of deep neural networks

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:15:24.662780Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Pith citing papers

No inbound Pith citation observations are available.