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Paper Citation Record · LEDGER

EdgeCompress: Coupling Multidimensional Model Compression and Dynamic Inference for EdgeAI

As of 11 August 2026, this Paper Citation Record lists 59 of 59 outbound references and 0 inbound Pith citation observations for arXiv:2607.06982.

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

pith.paper-citation-record.v1
2607.06982 v1

Coverage vector

measured 59 of 59 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-09T22:22:15.878399Z

measured 59 of 59 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+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

59 of 59 outbound references displayed

  • verified exact4
  • verified fuzzy55
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8a046f65-d4dd-4024-969b-49a521ba9cb2 · outbound

This paper cites Imagenet: A large-scale hierarchical image database.

EdgeCompress: Coupling Multidimensional Model Compression and Dynamic Inference for EdgeAI Imagenet: A large-scale hierarchical image database

Reference 1

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 29d2a971-e160-42b2-9759-dfb75adcdb14 · outbound

This paper cites Microsoft coco: Common objects in context,.

EdgeCompress: Coupling Multidimensional Model Compression and Dynamic Inference for EdgeAI Microsoft coco: Common objects in context,

Reference 2

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verified fuzzy
raw_fallback, observed 2026-07-09T22:26:37.345212Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-07-09T22:22:15.878399Z digest=sha256:f86ef38abdc6c7d0ff5ab1589d7537f11d07493a3f5028e0fc6d253093dd3057

Observation 1183d727-9043-4f55-a48a-5b04eac7d2bd · outbound

This paper cites Efficientnet: Rethinking model scaling for convo- lutional neural networks,.

EdgeCompress: Coupling Multidimensional Model Compression and Dynamic Inference for EdgeAI Efficientnet: Rethinking model scaling for convo- lutional neural networks,

Reference 3

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verified fuzzy
raw_fallback, observed 2026-07-09T22:26:37.407100Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 692f006f-d91d-4516-8434-f74e4f53e86b · outbound

This paper cites Designing network design spaces,.

EdgeCompress: Coupling Multidimensional Model Compression and Dynamic Inference for EdgeAI Designing network design spaces,

Reference 4

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raw_fallback, observed 2026-07-09T22:26:37.405483Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-07-09T22:22:15.878399Z digest=sha256:c89c979f129039cd6d180d156a00d593effdf5519c7731398b5edabd913f1aae

Observation 47ee679b-bc04-44c9-be48-04c3ee2c23e1 · outbound

This paper cites A convnet for the 2020s,.

EdgeCompress: Coupling Multidimensional Model Compression and Dynamic Inference for EdgeAI A convnet for the 2020s,

Reference 5

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verified fuzzy
raw_fallback, observed 2026-07-09T22:26:37.351944Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-07-09T22:22:15.878399Z digest=sha256:3182173f59212b923ce1df5a2d962e9ae19f90bf7771012d088cc5e4fea199c7

Observation ce83e113-4041-42a9-b879-f512b0aa2770 · outbound

This paper cites Edge computing: Vision and challenges,.

EdgeCompress: Coupling Multidimensional Model Compression and Dynamic Inference for EdgeAI Edge computing: Vision and challenges,

Reference 6

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T22:26:37.398841Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation f2173493-1a87-481b-bd67-35f320f411ce · outbound

This paper cites Update compression for deep neural networks on the edge,.

EdgeCompress: Coupling Multidimensional Model Compression and Dynamic Inference for EdgeAI Update compression for deep neural networks on the edge,

Reference 7

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T22:26:37.402221Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-07-09T22:22:15.878399Z digest=sha256:53f3a3669a8bbc62bb4b44f185fafb8d476835a78da07ecc6154fdb947e32df9

Observation 9abb729c-4370-4d5a-8432-2fd8af121b62 · outbound

This paper cites Mobilenetv2: Inverted residuals and linear bottle- necks,.

EdgeCompress: Coupling Multidimensional Model Compression and Dynamic Inference for EdgeAI Mobilenetv2: Inverted residuals and linear bottle- necks,

Reference 8

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T22:26:37.377498Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-07-09T22:22:15.878399Z digest=sha256:7bce140040f332fa44bc2622692c2ebcf0996a7558f56d9d1b7c69b7359c5af3

Observation 3b4243f8-4917-4d41-a0ea-fed1d1db4bb4 · outbound

This paper cites Mnasnet: Platform-aware neural architecture search for mobile,.

EdgeCompress: Coupling Multidimensional Model Compression and Dynamic Inference for EdgeAI Mnasnet: Platform-aware neural architecture search for mobile,

Reference 9

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verified fuzzy
raw_fallback, observed 2026-07-09T22:26:37.374182Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-07-09T22:22:15.878399Z digest=sha256:4d75cbbe4459293bee5a032aafd5c5070fdfc95d336d8e7cc358e493c5f703b6

Observation 0d52e1bd-fea8-4cb8-ba5c-9ac11ebdeb1b · outbound

This paper cites Dynamic resolution network,.

EdgeCompress: Coupling Multidimensional Model Compression and Dynamic Inference for EdgeAI Dynamic resolution network,

Reference 10

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verified fuzzy
raw_fallback, observed 2026-07-09T22:26:37.331064Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-07-09T22:22:15.878399Z digest=sha256:862cfb000614d3ae877003f40e8589829ebc95d7759cff62075081517db1daee

Observation 1233fa3f-15e5-42c6-bfac-ff46311d15cb · outbound

This paper cites Hrank: Filter pruning using high-rank feature map,.

EdgeCompress: Coupling Multidimensional Model Compression and Dynamic Inference for EdgeAI Hrank: Filter pruning using high-rank feature map,

Reference 11

Resolution
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raw_fallback, observed 2026-07-09T22:26:37.412228Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-07-09T22:22:15.878399Z digest=sha256:019915b83e19de21594288b38120eed32128cadb0d98dd655828cc0a882c4f37

Observation 4c5d7915-a8f8-47c7-ba39-c58137f14d16 · outbound

This paper cites Convolutional neural network pruning with structural redundancy reduction,.

EdgeCompress: Coupling Multidimensional Model Compression and Dynamic Inference for EdgeAI Convolutional neural network pruning with structural redundancy reduction,

Reference 12

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raw_fallback, observed 2026-07-09T22:26:37.369289Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-07-09T22:22:15.878399Z digest=sha256:f37017df18d43ccb3f8301b3f3980d27d7b6b79e0eba2d840a4af003abf74fec

Observation 56168db5-e2de-4f33-a435-9d7f128d77d5 · outbound

This paper cites Slimmable neural networks,.

EdgeCompress: Coupling Multidimensional Model Compression and Dynamic Inference for EdgeAI Slimmable neural networks,

Reference 13

Resolution
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raw_fallback, observed 2026-07-09T22:26:37.358515Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-07-09T22:22:15.878399Z digest=sha256:a9bb4175d74d54d27c00ba4647dba0c51bfaad07edd711323ff3cabe04f3e7bd

Observation 71abaab2-8159-427b-be1d-b45a5754ee4d · outbound

This paper cites Importance estimation for neural network pruning,.

EdgeCompress: Coupling Multidimensional Model Compression and Dynamic Inference for EdgeAI Importance estimation for neural network pruning,

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T22:26:37.372597Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-07-09T22:22:15.878399Z digest=sha256:eedae9c6030ef9c9cda6c97df565609f3d239fcc77f4b8a036559f3f3ddfcd73

Observation 4b0b0839-d8e7-42bb-8d5c-62a62663ea65 · outbound

This paper cites DBP: Discrimination Based Block-Level Pruning for Deep Model Acceleration.

EdgeCompress: Coupling Multidimensional Model Compression and Dynamic Inference for EdgeAI DBP: Discrimination Based Block-Level Pruning for Deep Model Acceleration

Reference 15

Resolution
verified exact
local_arxiv, observed 2026-07-09T22:26:36.954380Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-07-09T22:22:15.878399Z digest=sha256:772959231a243928b856573a4f6e02bdb194d12bd1be690ac8cb1d2a42f85703

Observation 798f0d20-511c-44ad-a914-d9a430f6b84c · outbound

This paper cites Decore: Deep compression with reinforcement learning,.

EdgeCompress: Coupling Multidimensional Model Compression and Dynamic Inference for EdgeAI Decore: Deep compression with reinforcement learning,

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T22:26:37.382361Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-07-09T22:22:15.878399Z digest=sha256:105bd65caa6547606c3e7866103b33487811dc6742fc5078ada76b472e26a3ad

Observation 318c22ea-5ba7-4c52-b463-04587e75b8e1 · outbound

This paper cites Glance and focus: A dynamic approach to reducing spatial redundancy in image classification,.

EdgeCompress: Coupling Multidimensional Model Compression and Dynamic Inference for EdgeAI Glance and focus: A dynamic approach to reducing spatial redundancy in image classification,

Reference 17

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raw_fallback, observed 2026-07-09T22:26:37.385716Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-07-09T22:22:15.878399Z digest=sha256:22cf924cf61691f98eb945c301a8b5ce9f622c4a029db66ff493a3c6832441ed

Observation ef71f83a-ab42-424a-ab76-592b81d53abe · outbound

This paper cites YOLOv4: Optimal Speed and Accuracy of Object Detection.

EdgeCompress: Coupling Multidimensional Model Compression and Dynamic Inference for EdgeAI YOLOv4: Optimal Speed and Accuracy of Object Detection

Reference 18

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verified exact
local_arxiv, observed 2026-07-09T22:26:36.952108Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-07-09T22:22:15.878399Z digest=sha256:3166bd71a31c606eed9257485cf151b97b60ccc65646dbd4503cb9c59f01abee

Observation 35aa6036-07e6-47bd-96c0-81a3d7ddb9f4 · outbound

This paper cites Ssd: Single shot multibox detector,.

EdgeCompress: Coupling Multidimensional Model Compression and Dynamic Inference for EdgeAI Ssd: Single shot multibox detector,

Reference 19

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raw_fallback, observed 2026-07-09T22:26:37.388923Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-07-09T22:22:15.878399Z digest=sha256:25385da5861f48283eec99156674c1a8f83b456bb72e6e06131cb13e286b4716

Observation dfe6d824-74c3-43d1-a966-8bde2cb3baa4 · outbound

This paper cites Faster r-cnn: Towards real-time object detection with region proposal networks,.

EdgeCompress: Coupling Multidimensional Model Compression and Dynamic Inference for EdgeAI Faster r-cnn: Towards real-time object detection with region proposal networks,

Reference 20

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raw_fallback, observed 2026-07-09T22:26:37.332677Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-07-09T22:22:15.878399Z digest=sha256:9e4d7ec895fd3eb65f2e849e633d59052248390f9fbb45d46861a5bfe81498da

Observation af843880-c3f7-418e-8d2d-e2bb21afbea5 · outbound

This paper cites Mask r-cnn,.

EdgeCompress: Coupling Multidimensional Model Compression and Dynamic Inference for EdgeAI Mask r-cnn,

Reference 21

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raw_fallback, observed 2026-07-09T22:26:37.417303Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-07-09T22:22:15.878399Z digest=sha256:a2cdd560a79b0c5532b24e7d8d42a6b6d536690f9fc9d0f31f099d00c18be15c

Observation f5d8b722-87fe-4bfb-98fe-ba238a437869 · outbound

This paper cites The pascal visual object classes (voc) challenge,.

EdgeCompress: Coupling Multidimensional Model Compression and Dynamic Inference for EdgeAI The pascal visual object classes (voc) challenge,

Reference 22

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T22:26:37.387387Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-07-09T22:22:15.878399Z digest=sha256:5672d6c9691e94ceeab88718c83f9a53983176be7b839253c11bad567d8f02c4

Observation 0c04ca1b-fd2e-442b-b762-a71112ba65d7 · outbound

This paper cites Unsupervised object discovery and co-localization by deep descriptor transformation,.

EdgeCompress: Coupling Multidimensional Model Compression and Dynamic Inference for EdgeAI Unsupervised object discovery and co-localization by deep descriptor transformation,

Reference 23

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T22:26:37.343624Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-07-09T22:22:15.878399Z digest=sha256:61202a4505eac2f3f2c4824fb07c0827623de17d8a49d0855ea1a3256cbb7f56

Observation e0455b16-4b54-4635-a04e-65342614b804 · outbound

This paper cites Rethinking the route towards weakly supervised object localization,.

EdgeCompress: Coupling Multidimensional Model Compression and Dynamic Inference for EdgeAI Rethinking the route towards weakly supervised object localization,

Reference 24

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T22:26:37.375841Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-07-09T22:22:15.878399Z digest=sha256:2df09bba9b9bda9d35fad73c78994e2eb0ce23a267466fedb2c73b518b31f598

Observation 19e691af-08cd-4975-b2ba-29b16935f9ed · outbound

This paper cites Learning deep features for discriminative localization,.

EdgeCompress: Coupling Multidimensional Model Compression and Dynamic Inference for EdgeAI Learning deep features for discriminative localization,

Reference 25

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raw_fallback, observed 2026-07-09T22:26:37.422277Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-07-09T22:22:15.878399Z digest=sha256:50b7ed67b9f442c53df373a200eb2df09a7c220c8ea2de0a2403eae571254967

Observation 319f8e9b-6353-42af-81f6-726703fa64f8 · outbound

This paper cites Grad-cam: Visual explanations from deep networks via gradient-based localization,.

EdgeCompress: Coupling Multidimensional Model Compression and Dynamic Inference for EdgeAI Grad-cam: Visual explanations from deep networks via gradient-based localization,

Reference 26

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verified fuzzy
raw_fallback, observed 2026-07-09T22:26:37.395619Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-07-09T22:22:15.878399Z digest=sha256:399c48fe7cf0283cd00a7d1aa944f7f865ffa9286631828da571c3337e5c7a41

Observation dae90626-c7b6-4e35-9f92-af58fe67c203 · outbound

This paper cites Adversarial complementary learning for weakly supervised object localization,.

EdgeCompress: Coupling Multidimensional Model Compression and Dynamic Inference for EdgeAI Adversarial complementary learning for weakly supervised object localization,

Reference 27

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T22:26:37.367620Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 310c3b26-f629-4d75-93d0-14838a02f07e · outbound

This paper cites Bringing ai to edge: From deep learning’s perspective,.

EdgeCompress: Coupling Multidimensional Model Compression and Dynamic Inference for EdgeAI Bringing ai to edge: From deep learning’s perspective,

Reference 28

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verified fuzzy
raw_fallback, observed 2026-07-09T22:26:37.413895Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-07-09T22:22:15.878399Z digest=sha256:4c950b9f0800f23fbdf088a4e1deb1a381c5f718bfaff72f9d76b7567bcadd1e

Observation a2c19f64-cb0d-4a60-857b-3cbbefda73d5 · outbound

This paper cites Layer pruning for obtaining shallower resnets,.

EdgeCompress: Coupling Multidimensional Model Compression and Dynamic Inference for EdgeAI Layer pruning for obtaining shallower resnets,

Reference 29

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raw_fallback, observed 2026-07-09T22:26:37.348620Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-07-09T22:22:15.878399Z digest=sha256:5239d79316d2d5c920a59cd4770c77a3a825e5ebc6c22d9612fbd53302a46cfd

Observation 30b7b61f-3258-4632-97f8-311239f42ea6 · outbound

This paper cites Branchynet: Fast inference via early exiting from deep neural networks,.

EdgeCompress: Coupling Multidimensional Model Compression and Dynamic Inference for EdgeAI Branchynet: Fast inference via early exiting from deep neural networks,

Reference 30

Resolution
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raw_fallback, observed 2026-07-09T22:26:37.390566Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-07-09T22:22:15.878399Z digest=sha256:12f0e0f648f538aa70e74bd9c14476f63cc8913d6896f34c2e23b1009dd43b97

Observation 1f0eb744-6d6d-46c0-ac7b-8a562f331be4 · outbound

This paper cites Multi-scale dense networks for resource efficient image classification,.

EdgeCompress: Coupling Multidimensional Model Compression and Dynamic Inference for EdgeAI Multi-scale dense networks for resource efficient image classification,

Reference 31

Resolution
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raw_fallback, observed 2026-07-09T22:26:37.347019Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-07-09T22:22:15.878399Z digest=sha256:8c5caaa05d9e7633eaa057be5bcb925791e9bb822962e74d990f2e3c1a981db8

Observation 1f2658c8-f774-4e34-8518-8b7ef896aaf6 · outbound

This paper cites Adaptive neural networks for efficient inference,.

EdgeCompress: Coupling Multidimensional Model Compression and Dynamic Inference for EdgeAI Adaptive neural networks for efficient inference,

Reference 32

Resolution
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raw_fallback, observed 2026-07-09T22:26:37.365987Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-07-09T22:22:15.878399Z digest=sha256:da19ab867a3a038ce4909384f225eecaa5edcd8ee3793908ef3fe6f8914e13e4

Observation b9595270-271d-4af2-8430-c3ac92e44cca · outbound

This paper cites Adaptive Computation Time for Recurrent Neural Networks.

EdgeCompress: Coupling Multidimensional Model Compression and Dynamic Inference for EdgeAI Adaptive Computation Time for Recurrent Neural Networks

Reference 33

Resolution
verified exact
local_arxiv, observed 2026-07-09T22:26:36.958778Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-07-09T22:22:15.878399Z digest=sha256:9e5004e354ba14577acf89740d91a9f69fff6ea8ebdcdebd1326ae92b157c129

Observation 188c0187-e5b0-4a48-9a64-6415f9c4ff3b · outbound

This paper cites Skipnet: Learning dynamic routing in convolutional networks,.

EdgeCompress: Coupling Multidimensional Model Compression and Dynamic Inference for EdgeAI Skipnet: Learning dynamic routing in convolutional networks,

Reference 34

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T22:26:37.342040Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-07-09T22:22:15.878399Z digest=sha256:60c48b90104b90d4dfcc8e608428070930490b48cf7c66b964e0bed9efe4f993

Observation 1247d3a0-6732-49fc-b36d-17a5d6795f7a · outbound

This paper cites Convolutional networks with adaptive infer- ence graphs,.

EdgeCompress: Coupling Multidimensional Model Compression and Dynamic Inference for EdgeAI Convolutional networks with adaptive infer- ence graphs,

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T22:26:37.400504Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-07-09T22:22:15.878399Z digest=sha256:5b61a0006d6e68fa7c440b06bc21856061a49b196ea90e06988fd21811326bdd

Observation 7c717072-76eb-44f3-a6c0-2e8d869aee30 · outbound

This paper cites Channel gating neural networks,.

EdgeCompress: Coupling Multidimensional Model Compression and Dynamic Inference for EdgeAI Channel gating neural networks,

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T22:26:37.420671Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-07-09T22:22:15.878399Z digest=sha256:10f24acebd474f6864a2a0b532eb141cca1ce9df3ae78a2734533de9a43532b4

Observation 8223500e-7c47-4112-b225-d5437640cee0 · outbound

This paper cites Runtime neural pruning,.

EdgeCompress: Coupling Multidimensional Model Compression and Dynamic Inference for EdgeAI Runtime neural pruning,

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T22:26:37.380784Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-07-09T22:22:15.878399Z digest=sha256:f7e21690a04eb0d1b61fb4678575d841add7eef15c3c92604bd7a0bc9330829d

Observation 7502524c-33a3-4a91-b677-5c19e4416849 · outbound

This paper cites Dynamic channel pruning: Feature boosting and sup- pression,.

EdgeCompress: Coupling Multidimensional Model Compression and Dynamic Inference for EdgeAI Dynamic channel pruning: Feature boosting and sup- pression,

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T22:26:37.397254Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-07-09T22:22:15.878399Z digest=sha256:8055e571d8c695a8dcdb2f90d057a856dae7ddfd90bb852249b79e2728eea159

Observation c8c2439d-200e-4b02-a908-eb7648c19426 · outbound

This paper cites Channel selection using gumbel softmax,.

EdgeCompress: Coupling Multidimensional Model Compression and Dynamic Inference for EdgeAI Channel selection using gumbel softmax,

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T22:26:37.336097Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-07-09T22:22:15.878399Z digest=sha256:8334ab83ab1414bcd2476c511e1d2b0a4d6ee28a1e0ed09b124ed9b980342d30

Observation e51cf6ce-9a47-42b1-bbe2-4bb52d800751 · outbound

This paper cites Resolution adaptive networks for efficient inference,.

EdgeCompress: Coupling Multidimensional Model Compression and Dynamic Inference for EdgeAI Resolution adaptive networks for efficient inference,

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T22:26:37.362602Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-07-09T22:22:15.878399Z digest=sha256:998d02c474f713aa4607beee4a9aafc9ee2e1b506e6dd21733b8dd5fb7c7bc94

Observation 4c079a55-1887-490d-b78c-9caf522cf651 · outbound

This paper cites Deep residual learning for image recognition,.

EdgeCompress: Coupling Multidimensional Model Compression and Dynamic Inference for EdgeAI Deep residual learning for image recognition,

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T22:26:37.353675Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-07-09T22:22:15.878399Z digest=sha256:c3d51c2fb28622cafa4eb854bb662e39735bd0adfae9d2708f252fc6989ca579

Observation 78f813fd-aaa8-4bcb-a360-c8edf594e20f · outbound

This paper cites Tiny-dsod: Lightweight object detection for resource- restricted usages,.

EdgeCompress: Coupling Multidimensional Model Compression and Dynamic Inference for EdgeAI Tiny-dsod: Lightweight object detection for resource- restricted usages,

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T22:26:37.355378Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-07-09T22:22:15.878399Z digest=sha256:42e53bf6ba2f1cc3c68efde7569b2bbe15e7ff51c948822c90e46e8340a9cb84

Observation 0ba90781-a8de-43ec-ae18-e35477d3fe78 · outbound

This paper cites Adam: A method for stochastic optimization.

EdgeCompress: Coupling Multidimensional Model Compression and Dynamic Inference for EdgeAI Adam: A method for stochastic optimization

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T22:26:37.350157Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-07-09T22:22:15.878399Z digest=sha256:3347d1613f60698eed13a9ab2190a78d5759b84b94be64c1f6e046e398bd3e6d

Observation 32f9b8e6-4a72-4914-9928-1b3b2b8ddc71 · outbound

This paper cites An exponential learning rate schedule for deep learning,.

EdgeCompress: Coupling Multidimensional Model Compression and Dynamic Inference for EdgeAI An exponential learning rate schedule for deep learning,

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T22:26:37.393980Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-07-09T22:22:15.878399Z digest=sha256:e2985f673bfbca2a18f520ba30438593dcdfd9ae108878f95d55774aef45f533

Observation b76a3f3c-56d5-4544-a8e4-75ba963501c9 · outbound

This paper cites Hapi: Hardware-aware progressive inference,.

EdgeCompress: Coupling Multidimensional Model Compression and Dynamic Inference for EdgeAI Hapi: Hardware-aware progressive inference,

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T22:26:37.356982Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-07-09T22:22:15.878399Z digest=sha256:655b5b6f6aa71ad2e02d1bcbbeeb9262dd2e308582430393409a717ae6c63949

Observation 6f685cff-09e0-4739-9b04-fe14622ee67c · outbound

This paper cites Shallow-deep networks: Understanding and mitigating network overthinking,.

EdgeCompress: Coupling Multidimensional Model Compression and Dynamic Inference for EdgeAI Shallow-deep networks: Understanding and mitigating network overthinking,

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T22:26:37.364192Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-07-09T22:22:15.878399Z digest=sha256:f2745cade33c5e854c0d5eb4cfb1209deb5fc6784309659d9c3074bb4f6fc56e

Observation 4db65c55-610a-4716-ad46-3e9ab0e6d179 · outbound

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

EdgeCompress: Coupling Multidimensional Model Compression and Dynamic Inference for EdgeAI Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 47

Resolution
verified exact
local_arxiv, observed 2026-07-09T22:26:36.956582Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-07-09T22:22:15.878399Z digest=sha256:90f32868e5be721f7beec7acece6ba306b6a86ebdf55f5014d16173b2f6b1c76

Observation 325d1e26-664b-4eb9-a53c-53acc245c5ca · outbound

This paper cites Rethinking the inception architecture for computer vision,.

EdgeCompress: Coupling Multidimensional Model Compression and Dynamic Inference for EdgeAI Rethinking the inception architecture for computer vision,

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T22:26:37.384019Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-07-09T22:22:15.878399Z digest=sha256:74e97ff7bf81d159a41e333ce619c8f36f0e59cf49a1b1e0712107a60dc781e7

Observation b2c225a4-6d46-477f-8111-c5bf6ca3fd35 · outbound

This paper cites Wide residual networks,.

EdgeCompress: Coupling Multidimensional Model Compression and Dynamic Inference for EdgeAI Wide residual networks,

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T22:26:37.338715Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-07-09T22:22:15.878399Z digest=sha256:8303146c461c5e8e5bfc77b327c4ebcdf6dfb598a4769999cef104e6bbe68442

Observation 1fb16f3a-3435-472b-bfc5-56c996fad770 · outbound

This paper cites Data-driven sparse structure selection for deep neural networks,.

EdgeCompress: Coupling Multidimensional Model Compression and Dynamic Inference for EdgeAI Data-driven sparse structure selection for deep neural networks,

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T22:26:37.410563Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-07-09T22:22:15.878399Z digest=sha256:3b0197093acc8388c6299a87ffe32a73756886c8c2e8ffc15914f5561533bbb7

Observation e5938fca-b86e-4f1a-9a1c-150671381171 · outbound

This paper cites Learning versatile filters for efficient convolutional neural networks,.

EdgeCompress: Coupling Multidimensional Model Compression and Dynamic Inference for EdgeAI Learning versatile filters for efficient convolutional neural networks,

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T22:26:37.403937Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-07-09T22:22:15.878399Z digest=sha256:40c6258af42118aacbb033897ed1b3e6bc2d3a929f13b0421bd7fda90101eb89

Observation f319b51e-bdff-4081-9907-c11a41baeff7 · outbound

This paper cites Provable filter pruning for efficient neural networks,.

EdgeCompress: Coupling Multidimensional Model Compression and Dynamic Inference for EdgeAI Provable filter pruning for efficient neural networks,

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T22:26:37.415720Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-07-09T22:22:15.878399Z digest=sha256:7c09e379562153dd5cb13f7440db9084699a7163b385268520dc51b70e63a57e

Observation a6e6bdb7-f4dd-4f07-a819-d5d36223bddf · outbound

This paper cites Centripetal sgd for pruning very deep convolutional networks with complicated structure,.

EdgeCompress: Coupling Multidimensional Model Compression and Dynamic Inference for EdgeAI Centripetal sgd for pruning very deep convolutional networks with complicated structure,

Reference 53

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T22:26:37.340406Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-07-09T22:22:15.878399Z digest=sha256:9fcbf7cf8b3e5711058b7808bdc1791ee071349d0537803f014578d41ce99716

Observation a0ae7f46-2b48-4a42-a994-9bce28e9aaa9 · outbound

This paper cites Towards optimal structured cnn pruning via generative adversarial learning,.

EdgeCompress: Coupling Multidimensional Model Compression and Dynamic Inference for EdgeAI Towards optimal structured cnn pruning via generative adversarial learning,

Reference 54

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T22:26:37.408883Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-07-09T22:22:15.878399Z digest=sha256:3c8eb1f8f1cb2a7172600491dea7ec0d1768e6d1f8f04cd14694e2da5c3fe1d2

Observation f48327a2-9f46-4b07-badf-43983714f94c · outbound

This paper cites Autopruner: An end-to-end trainable filter pruning method for efficient deep model inference,.

EdgeCompress: Coupling Multidimensional Model Compression and Dynamic Inference for EdgeAI Autopruner: An end-to-end trainable filter pruning method for efficient deep model inference,

Reference 55

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T22:26:37.334362Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-07-09T22:22:15.878399Z digest=sha256:d4441d3be79eae89c3158b2874855d088ecf5daed1d017107e2ef2ee796c7267

Observation 0ec14429-5fef-4c89-ac3e-5e75e5f5c3a7 · outbound

This paper cites Densely connected convolutional networks,.

EdgeCompress: Coupling Multidimensional Model Compression and Dynamic Inference for EdgeAI Densely connected convolutional networks,

Reference 56

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T22:26:37.360855Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-07-09T22:22:15.878399Z digest=sha256:0902fee16f0f4a09380d8af514d1b48183235dcf6995d29c9ac5fd0a7c57e559

Observation bece9939-3897-4794-9b62-0ae34bfac5b7 · outbound

This paper cites Batch normalization: Accelerating deep network training by reducing internal covariate shift,.

EdgeCompress: Coupling Multidimensional Model Compression and Dynamic Inference for EdgeAI Batch normalization: Accelerating deep network training by reducing internal covariate shift,

Reference 57

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T22:26:37.370960Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-07-09T22:22:15.878399Z digest=sha256:0ca8c73b7f0cf10bb815fc602d29e62a35298ded30210b3e28bd2461f0ec7ca7

Observation bae9f694-88d1-4512-861e-010fa9b61297 · outbound

This paper cites Variational convolutional neural network pruning,.

EdgeCompress: Coupling Multidimensional Model Compression and Dynamic Inference for EdgeAI Variational convolutional neural network pruning,

Reference 58

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T22:26:37.419074Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-07-09T22:22:15.878399Z digest=sha256:8d95e95f26e456a668bb22c04d57de8166f601c94e62ff48d1956a6b81926136

Observation 46bfbdbd-ba8c-400e-a76b-e884ea79264d · outbound

This paper cites His current research interests include edge intelli- gence, hardware-aware neural architecture search, and model compression.

EdgeCompress: Coupling Multidimensional Model Compression and Dynamic Inference for EdgeAI His current research interests include edge intelli- gence, hardware-aware neural architecture search, and model compression

Reference 59

Resolution
verified fuzzy
raw_fallback, observed 2026-07-09T22:26:37.379251Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-07-09T22:22:15.878399Z digest=sha256:2de30797d2cc567a40dac863b3ddfd34be62b4978368c3f7ba7da829de6f987f

Pith citing papers

No inbound Pith citation observations are available.