Paper Citation Record · LEDGER

FMCE-Net++: Feature Map Convergence Evaluation and Training

As of 15 August 2026, this Paper Citation Record lists 20 of 20 outbound references and 0 inbound Pith citation observations for arXiv:2508.06109.

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

pith.paper-citation-record.v1
2508.06109 v2

Coverage vector

measured 20 of 20 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T23:04:15.585923Z

measured 20 of 20 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+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

20 of 20 outbound references displayed

verified exact3
verified fuzzy17
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 038be50d-3efb-4efd-aae8-4d91b00168ad · outbound

This paper cites Uncertainty in machine learning: A safety perspective on autonomous driving,.

FMCE-Net++: Feature Map Convergence Evaluation and Training Uncertainty in machine learning: A safety perspective on autonomous driving,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:04:15.828788Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 7eb27a60-2b3a-4b28-995a-5d2bb9fbaf0e · outbound

This paper cites Deep learning for safe autonomous driving: Current challenges and future directions,.

FMCE-Net++: Feature Map Convergence Evaluation and Training Deep learning for safe autonomous driving: Current challenges and future directions,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:04:15.818803Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 897bd65f-9064-44a4-84e6-89d91b8289c2 · outbound

This paper cites Explainability of deep vision-based autonomous driving systems: Review and chal- lenges,.

FMCE-Net++: Feature Map Convergence Evaluation and Training Explainability of deep vision-based autonomous driving systems: Review and chal- lenges,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:04:15.808723Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 9677aa3e-643a-497b-bd8e-feea81774f4e · outbound

This paper cites Explainable arti- ficial intelligence for autonomous driving: A comprehensive overview and field guide for future research directions,.

FMCE-Net++: Feature Map Convergence Evaluation and Training Explainable arti- ficial intelligence for autonomous driving: A comprehensive overview and field guide for future research directions,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:04:15.798219Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 35c07198-3490-4b25-8c61-2ddbee2b60a0 · outbound

This paper cites Feature Map Convergence Evaluation for Functional Module.

FMCE-Net++: Feature Map Convergence Evaluation and Training Feature Map Convergence Evaluation for Functional Module

Reference 5

Resolution: verified exact; local_arxiv, observed 2026-08-05T23:04:15.651856Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation d8e16d9b-2bd9-44c8-9cee-9dc6a214bb0a · outbound

This paper cites To what extent do dnn-based image classification models make unreliable inferences?.

FMCE-Net++: Feature Map Convergence Evaluation and Training To what extent do dnn-based image classification models make unreliable inferences?

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:04:15.786836Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 12ed1ccb-b12d-4461-bb3d-640cd1029c74 · outbound

This paper cites Image classification using dnn with an improved optimizer,.

FMCE-Net++: Feature Map Convergence Evaluation and Training Image classification using dnn with an improved optimizer,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:04:15.776497Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 55e9a9c2-8d9e-4ee3-908c-607ce6144d56 · outbound

This paper cites isec: An optimized deep learning model for image classification on edge computing,.

FMCE-Net++: Feature Map Convergence Evaluation and Training isec: An optimized deep learning model for image classification on edge computing,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:04:15.766778Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 8fda686a-919a-484a-b776-319899e0d36d · outbound

This paper cites Optimizing convolutional neural networks archi- tecture using a modified particle swarm optimization for image classi- fication,.

FMCE-Net++: Feature Map Convergence Evaluation and Training Optimizing convolutional neural networks archi- tecture using a modified particle swarm optimization for image classi- fication,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:04:15.756717Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 360cbb24-3b83-497c-b36a-82c28ac9c79d · outbound

This paper cites Jointly Optimizing Preprocessing and Inference for DNN-based Visual Analytics.

FMCE-Net++: Feature Map Convergence Evaluation and Training Jointly Optimizing Preprocessing and Inference for DNN-based Visual Analytics

Reference 10

Resolution: verified exact; local_arxiv, observed 2026-08-05T23:04:15.636300Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation bc33a50a-d461-4b88-b1d1-86dad22ea42f · outbound

This paper cites A testing and evaluation framework for the quality of dnn models,.

FMCE-Net++: Feature Map Convergence Evaluation and Training A testing and evaluation framework for the quality of dnn models,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:04:15.746722Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation b781d346-8f85-4d0c-9b3d-8dbeaf5e585c · outbound

This paper cites Deepxplore: Automated whitebox testing of deep learning systems,.

FMCE-Net++: Feature Map Convergence Evaluation and Training Deepxplore: Automated whitebox testing of deep learning systems,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:04:15.736700Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation ac1b6c1e-7e26-4517-b9de-ba57fb9c4256 · outbound

This paper cites Efficient online testing for dnn- enabled systems using surrogate-assisted and many-objective opti- mization,.

FMCE-Net++: Feature Map Convergence Evaluation and Training Efficient online testing for dnn- enabled systems using surrogate-assisted and many-objective opti- mization,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:04:15.726870Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation e5e1eb25-efd2-49c5-a6f4-3fb5e9f4c544 · outbound

This paper cites A scenario- based functional testing approach to improving dnn performance,.

FMCE-Net++: Feature Map Convergence Evaluation and Training A scenario- based functional testing approach to improving dnn performance,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:04:15.716690Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation c37cd7a9-9088-4a99-a95c-4ead8803edea · outbound

This paper cites Selection of test samples to improve dnn test efficiency based on neuron clusters,.

FMCE-Net++: Feature Map Convergence Evaluation and Training Selection of test samples to improve dnn test efficiency based on neuron clusters,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:04:15.706232Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 541abb9c-f028-4866-887d-eb377c80eaeb · outbound

This paper cites Quantitative Performance Assessment of CNN Units via Topological Entropy Calculation.

FMCE-Net++: Feature Map Convergence Evaluation and Training Quantitative Performance Assessment of CNN Units via Topological Entropy Calculation

Reference 16

Resolution: verified exact; local_arxiv, observed 2026-08-05T23:04:15.620588Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 936b6c0b-1235-4776-8779-8c0d16b973f6 · outbound

This paper cites Explaining the black- box model: A survey of local interpretation methods for deep neural networks,.

FMCE-Net++: Feature Map Convergence Evaluation and Training Explaining the black- box model: A survey of local interpretation methods for deep neural networks,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:04:15.696479Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 2a7f9583-db4e-4547-98af-f437d4254361 · outbound

This paper cites Accelerating image classification using feature map similarity in convolutional neural networks,.

FMCE-Net++: Feature Map Convergence Evaluation and Training Accelerating image classification using feature map similarity in convolutional neural networks,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:04:15.685377Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation b0c8bac9-428d-4c11-898f-43994a631046 · outbound

This paper cites End-to-end self-driving using deep neural networks with multi-auxiliary tasks,.

FMCE-Net++: Feature Map Convergence Evaluation and Training End-to-end self-driving using deep neural networks with multi-auxiliary tasks,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:04:15.674177Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation bf9fdf26-b276-4ef9-b121-d79d30b8e1d8 · outbound

This paper cites Bevformer v2: Adapting modern image backbones to bird’s-eye-view recognition via perspective supervision,.

FMCE-Net++: Feature Map Convergence Evaluation and Training Bevformer v2: Adapting modern image backbones to bird’s-eye-view recognition via perspective supervision,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:04:15.663191Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Pith citing papers

No inbound Pith citation observations are available.