Paper Citation Record · LEDGER

Improved Techniques for Training Adaptive Deep Networks

As of 22 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 0 inbound Pith citation observations for arXiv:1908.06294.

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

pith.paper-citation-record.v1
1908.06294 v1

Coverage vector

measured 40 of 40 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T12:53:41.647801Z

measured 40 of 40 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+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

40 of 40 outbound references displayed

verified exact3
verified fuzzy30
unresolved7
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 3ebaf5b4-66b1-48a3-a918-c4b577cf4189 · outbound

This paper cites Do deep nets really need to be deep? In NIPS, 2014.

Improved Techniques for Training Adaptive Deep Networks Do deep nets really need to be deep? In NIPS, 2014

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:53:43.398553Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 93a7c40d-40dc-4da5-85ad-8ee7fa1accb3 · outbound

This paper cites Adaptive neural networks for fast test-time pre- diction.

Improved Techniques for Training Adaptive Deep Networks Adaptive neural networks for fast test-time pre- diction

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:53:43.368592Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation f8220771-07b0-4f9a-ab75-3b148884050a · outbound

This paper cites Model compression.

Improved Techniques for Training Adaptive Deep Networks Model compression

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:53:43.330982Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 3b040894-cf4a-4c0d-a8e3-a3799a95d248 · outbound

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

Improved Techniques for Training Adaptive Deep Networks Compressing convolutional neural networks in the frequency domain

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:53:43.285776Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 5bac5870-0a83-4938-a102-20ca171717ef · outbound

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

Improved Techniques for Training Adaptive Deep Networks Imagenet: A large-scale hierarchical image database

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-14T12:53:41.004020Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a5555e5a-b270-4f8d-909a-a01e21274f2a · outbound

This paper cites Spatially Adaptive Computation Time for Residual Networks.

Improved Techniques for Training Adaptive Deep Networks Spatially Adaptive Computation Time for Residual Networks

Reference 6

Resolution: verified exact; local_arxiv, observed 2026-08-14T12:53:41.943354Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 9f90ead9-828d-4abb-96c4-bfcc7d988073 · outbound

This paper cites Adaptive Computation Time for Recurrent Neural Networks.

Improved Techniques for Training Adaptive Deep Networks Adaptive Computation Time for Recurrent Neural Networks

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-14T12:53:41.054373Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 03fe346f-f790-48d0-afab-b88aaaaf63de · outbound

This paper cites Deep com- pression: Compressing deep neural networks with pruning, trained quantization and huffman coding.

Improved Techniques for Training Adaptive Deep Networks Deep com- pression: Compressing deep neural networks with pruning, trained quantization and huffman coding

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:53:43.204936Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 353d0884-d216-4d25-9338-e95686283062 · outbound

This paper cites Mask r-cnn.

Improved Techniques for Training Adaptive Deep Networks Mask r-cnn

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-14T12:53:41.108528Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 903599e9-4fe9-4216-bd1f-3dad6b36e4e8 · outbound

This paper cites Deep residual learning for image recognition.

Improved Techniques for Training Adaptive Deep Networks Deep residual learning for image recognition

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-14T12:53:41.114480Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3c081ad8-234c-43be-a81a-b2f817ef2aab · outbound

This paper cites Amc: Automl for model compression and accel- eration on mobile devices.

Improved Techniques for Training Adaptive Deep Networks Amc: Automl for model compression and accel- eration on mobile devices

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:53:43.112808Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation c009b934-9af4-4a99-9034-10078b91b8ba · outbound

This paper cites Distilling the knowledge in a neural network.

Improved Techniques for Training Adaptive Deep Networks Distilling the knowledge in a neural network

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:53:43.086200Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b489a409-044e-449e-8dcd-2a4c5f12da17 · outbound

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

Improved Techniques for Training Adaptive Deep Networks MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-14T12:53:41.137650Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 08164da0-81c5-4e7d-a119-04d72faed422 · outbound

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

Improved Techniques for Training Adaptive Deep Networks Multi-scale dense networks for resource efﬁcient image classiﬁcation

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:53:43.059765Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 38a7dd92-c9e3-41d8-ba4e-da812d02a490 · outbound

This paper cites Condensenet: An efﬁcient densenet using learned group convolutions.

Improved Techniques for Training Adaptive Deep Networks Condensenet: An efﬁcient densenet using learned group convolutions

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:53:42.983021Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation da864756-af89-4c69-a2c7-8916161bb287 · outbound

This paper cites Densely connected convolutional networks.

Improved Techniques for Training Adaptive Deep Networks Densely connected convolutional networks

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:53:42.914323Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 0fce080a-f4cf-4084-835e-fc42150635a1 · outbound

This paper cites Binarized neural networks.

Improved Techniques for Training Adaptive Deep Networks Binarized neural networks

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:53:42.885610Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 6c111a02-f664-43dc-81c0-c26e314cd23f · outbound

This paper cites Incorporating side information by adaptive convolution.

Improved Techniques for Training Adaptive Deep Networks Incorporating side information by adaptive convolution

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:53:42.837038Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 0ae44b59-a9b6-4b7f-9b5a-ffd7710c5852 · outbound

This paper cites Pixel-wise Attentional Gating for Parsimonious Pixel Labeling.

Improved Techniques for Training Adaptive Deep Networks Pixel-wise Attentional Gating for Parsimonious Pixel Labeling

Reference 19

Resolution: verified exact; local_arxiv, observed 2026-08-14T12:53:41.811260Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation fe08199f-1c47-4258-9fb2-a898e16da2af · outbound

This paper cites Learning multiple layers of features from tiny images.

Improved Techniques for Training Adaptive Deep Networks Learning multiple layers of features from tiny images

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:53:42.773485Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 1c814ee9-61f1-4480-bf41-f4d5b8e46367 · outbound

This paper cites Imagenet classiﬁcation with deep convolutional neural net- works.

Improved Techniques for Training Adaptive Deep Networks Imagenet classiﬁcation with deep convolutional neural net- works

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-14T12:53:41.319327Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 84bf2383-f8be-484c-91f5-9751de1dd2d4 · outbound

This paper cites Knowledge Distillation by On-the-Fly Native Ensemble.

Improved Techniques for Training Adaptive Deep Networks Knowledge Distillation by On-the-Fly Native Ensemble

Reference 22

Resolution: verified exact; local_arxiv, observed 2026-08-14T12:53:41.777049Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 342717c4-9d10-408d-9158-c8c9afadf2cd · outbound

This paper cites Pruning ﬁlters for efﬁcient convnets.

Improved Techniques for Training Adaptive Deep Networks Pruning ﬁlters for efﬁcient convnets

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:53:42.680622Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 9ad95a24-718a-48ff-acf4-2dc33470eb8f · outbound

This paper cites Dynamic computational time for visual attention.

Improved Techniques for Training Adaptive Deep Networks Dynamic computational time for visual attention

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:53:42.645401Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 04b33ced-ec33-4a47-b3ee-51802dc20396 · outbound

This paper cites Runtime neural pruning.

Improved Techniques for Training Adaptive Deep Networks Runtime neural pruning

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:53:42.618726Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 7caacf33-2205-4dcc-9165-f321ec88a742 · outbound

This paper cites Learning efﬁcient convolutional networks through network slimming.

Improved Techniques for Training Adaptive Deep Networks Learning efﬁcient convolutional networks through network slimming

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:53:42.594515Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 53f2dd69-7ee9-4091-ab12-f069032f9d9b · outbound

This paper cites Fully convolutional networks for semantic segmentation.

Improved Techniques for Training Adaptive Deep Networks Fully convolutional networks for semantic segmentation

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-14T12:53:41.393305Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation af60b9dd-3c99-4f25-9186-847aca52ae76 · outbound

This paper cites Shufﬂenet v2: Practical guidelines for efﬁcient cnn architec- ture design.

Improved Techniques for Training Adaptive Deep Networks Shufﬂenet v2: Practical guidelines for efﬁcient cnn architec- ture design

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:53:42.561352Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 19267e72-d2e4-44ca-960c-0a5871b06132 · outbound

This paper cites Recurrent segmentation for variable com- putational budgets.

Improved Techniques for Training Adaptive Deep Networks Recurrent segmentation for variable com- putational budgets

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:53:42.542294Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 67de7665-4520-4c2c-bd75-df248b874159 · outbound

This paper cites Mobilenetv2: Inverted residuals and linear bottlenecks.

Improved Techniques for Training Adaptive Deep Networks Mobilenetv2: Inverted residuals and linear bottlenecks

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:53:42.522236Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 73f85639-6560-4adf-833e-490506752ad2 · outbound

This paper cites Collaborative learning for deep neural networks.

Improved Techniques for Training Adaptive Deep Networks Collaborative learning for deep neural networks

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:53:42.491964Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 145272ec-f072-4383-a8f8-1ce81b682f77 · outbound

This paper cites Going deeper with convolutions.

Improved Techniques for Training Adaptive Deep Networks Going deeper with convolutions

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:53:42.431047Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 4d83a3ab-9844-4e06-8f8e-9f2b2d663481 · outbound

This paper cites Branchynet: Fast inference via early exiting from deep neu- ral networks.

Improved Techniques for Training Adaptive Deep Networks Branchynet: Fast inference via early exiting from deep neu- ral networks

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:53:42.370506Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation e6499a1e-1315-4dca-b188-cb8b56b37b8d · outbound

This paper cites Hydranets: Specialized dynamic archi- tectures for efﬁcient inference.

Improved Techniques for Training Adaptive Deep Networks Hydranets: Specialized dynamic archi- tectures for efﬁcient inference

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:53:42.297858Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 70c9c47a-3361-4a55-88c7-0c8ddcafa915 · outbound

This paper cites Convolutional networks with adaptive inference graphs.

Improved Techniques for Training Adaptive Deep Networks Convolutional networks with adaptive inference graphs

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:53:42.274437Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 6eed9890-6961-4429-8f0d-861374c7774b · outbound

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

Improved Techniques for Training Adaptive Deep Networks Skipnet: Learning dynamic routing in convolutional networks

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:53:42.249702Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 3d0877cf-1bb9-4e68-82ec-f401e8c250b9 · outbound

This paper cites Blockdrop: Dynamic inference paths in residual networks.

Improved Techniques for Training Adaptive Deep Networks Blockdrop: Dynamic inference paths in residual networks

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:53:42.172484Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation f88a89a8-f157-4021-a00d-ee48fd5650dd · outbound

This paper cites Depth-adaptive com- putational policies for efﬁcient visual tracking.

Improved Techniques for Training Adaptive Deep Networks Depth-adaptive com- putational policies for efﬁcient visual tracking

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:53:42.064396Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 0b441bd1-7fe2-4d7a-b1b2-ca242b9e63a8 · outbound

This paper cites Shufﬂenet: An extremely efﬁcient convolutional neural net- work for mobile devices.

Improved Techniques for Training Adaptive Deep Networks Shufﬂenet: An extremely efﬁcient convolutional neural net- work for mobile devices

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:53:42.031007Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 826f3fae-d06a-4573-a22e-2254b0bf6094 · outbound

This paper cites Learning transferable architectures for scalable image recognition.

Improved Techniques for Training Adaptive Deep Networks Learning transferable architectures for scalable image recognition

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:53:42.000988Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Pith citing papers

No inbound Pith citation observations are available.