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 2026-08-21T06:32:19.484+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

40 of 40 outbound references displayed

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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

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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

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

This paper cites Model compression.

Improved Techniques for Training Adaptive Deep Networks Model compression

Reference 3

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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

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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

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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

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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

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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

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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

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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

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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

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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

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Source-reported events for the cited work

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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

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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

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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

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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

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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

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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

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Source-reported events for the cited work

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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This paper cites Collaborative learning for deep neural networks.

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

Reference 31

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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