Paper Citation Record · LEDGER

Leveraging Stochastic Depth Training for Adaptive Inference

As of 9 August 2026, this Paper Citation Record lists 22 of 22 outbound references and 0 inbound Pith citation observations for arXiv:2505.17626.

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

pith.paper-citation-record.v1
2505.17626 v1

Coverage vector

measured 22 of 22 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:48:02.766394Z

measured 22 of 22 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+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

22 of 22 outbound references displayed

verified exact0
verified fuzzy19
unresolved3
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c869b7ba-f798-45ed-8820-89d259e7a886 · outbound

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

Leveraging Stochastic Depth Training for Adaptive Inference Skipnet: Learning dynamic routing in convolutional networks,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:48:06.208972Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b61cc628-8ac0-4dae-8bb1-029bef44ee00 · outbound

This paper cites HAPI: hardware-aware progressive inference,.

Leveraging Stochastic Depth Training for Adaptive Inference HAPI: hardware-aware progressive inference,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:48:05.978948Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation addad570-9bca-400f-95cc-a6257040c414 · outbound

This paper cites FlexDNN: Input-Adaptive On-Device Deep Learning for Efficient Mobile Vision,.

Leveraging Stochastic Depth Training for Adaptive Inference FlexDNN: Input-Adaptive On-Device Deep Learning for Efficient Mobile Vision,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:48:05.830007Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 67201071-052e-433f-9d1b-a723b551aa2c · outbound

This paper cites Convolutional networks with adaptive inference graphs,.

Leveraging Stochastic Depth Training for Adaptive Inference Convolutional networks with adaptive inference graphs,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:48:05.660858Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation d89bbd40-1636-4e78-97ad-7ae40a5b2f0a · outbound

This paper cites Learning layer-skippable inference network,.

Leveraging Stochastic Depth Training for Adaptive Inference Learning layer-skippable inference network,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:48:05.568660Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 712ed0f2-89e1-45df-995e-eeea0d94f3cc · outbound

This paper cites Dual dynamic inference: Enabling more efficient, adaptive, and controllable deep inference,.

Leveraging Stochastic Depth Training for Adaptive Inference Dual dynamic inference: Enabling more efficient, adaptive, and controllable deep inference,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:48:05.405790Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 69150313-4a4f-430b-b5cb-ac8539048ed7 · outbound

This paper cites Learning long-term dependencies with gradient descent is difficult,.

Leveraging Stochastic Depth Training for Adaptive Inference Learning long-term dependencies with gradient descent is difficult,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T14:48:01.558446Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9412955e-05fb-4ab0-97d3-79231ce73278 · outbound

This paper cites Neural architecture search: A survey,.

Leveraging Stochastic Depth Training for Adaptive Inference Neural architecture search: A survey,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:48:05.221426Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 4db578d8-a001-4aba-9788-afbc19527a76 · outbound

This paper cites Deep networks with stochastic depth,.

Leveraging Stochastic Depth Training for Adaptive Inference Deep networks with stochastic depth,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:48:05.035089Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 6645751d-ed67-47d2-b1ba-41912d951260 · outbound

This paper cites Deep residual learning for image recognition,.

Leveraging Stochastic Depth Training for Adaptive Inference Deep residual learning for image recognition,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T14:48:01.760462Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e254ea00-6e8d-4504-95cd-f9a116609bdc · outbound

This paper cites A compre- hensive survey on model compression and acceleration,.

Leveraging Stochastic Depth Training for Adaptive Inference A compre- hensive survey on model compression and acceleration,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:48:04.837666Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 9ceb5080-c677-46e3-8ece-6dad716f3d16 · outbound

This paper cites Adaptive inference through early-exit networks: Design, challenges and directions,.

Leveraging Stochastic Depth Training for Adaptive Inference Adaptive inference through early-exit networks: Design, challenges and directions,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:48:04.670910Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 90ddc149-1ff4-4b8f-8eee-6376e0bb8c68 · outbound

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

Leveraging Stochastic Depth Training for Adaptive Inference Branchynet: Fast inference via early exiting from deep neural networks,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:48:04.429709Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 024d640f-d104-488f-8b96-e8ec692fbebf · outbound

This paper cites Classynet: Class-aware early- exit neural networks for edge devices,.

Leveraging Stochastic Depth Training for Adaptive Inference Classynet: Class-aware early- exit neural networks for edge devices,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:48:04.273518Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 32d61b4e-1db6-4001-b118-74a9acc0d7f0 · outbound

This paper cites Pruning filters for efficient convnets,.

Leveraging Stochastic Depth Training for Adaptive Inference Pruning filters for efficient convnets,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:48:04.100869Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 65109521-ad22-4ee9-9643-971dc58d939b · outbound

This paper cites The Unreasonable Ineffectiveness of the Deeper Layers.

Leveraging Stochastic Depth Training for Adaptive Inference The Unreasonable Ineffectiveness of the Deeper Layers

Reference 16

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Source-reported events for the cited work

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Observation ea670737-c1e1-4647-b3a1-1f4192682a9d · outbound

This paper cites Interpretable task-inspired adaptive filter pruning for neural networks under multiple constraints,.

Leveraging Stochastic Depth Training for Adaptive Inference Interpretable task-inspired adaptive filter pruning for neural networks under multiple constraints,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:48:03.890112Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 94fc5821-cbea-47fe-9b88-9a2b6bf8b442 · outbound

This paper cites Pruning and early-exit co-optimization for cnn acceleration on fpgas,.

Leveraging Stochastic Depth Training for Adaptive Inference Pruning and early-exit co-optimization for cnn acceleration on fpgas,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:48:03.652095Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b9de48ec-9b9f-42c3-8f1d-fe61414dba6a · outbound

This paper cites Conditional deep learning for energy- efficient and enhanced pattern recognition,.

Leveraging Stochastic Depth Training for Adaptive Inference Conditional deep learning for energy- efficient and enhanced pattern recognition,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:48:03.514876Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 5fc7612f-70f9-446a-89a3-1424aa903a9e · outbound

This paper cites An mlir-based compiler and runtime for ml models from multiple frameworks,.

Leveraging Stochastic Depth Training for Adaptive Inference An mlir-based compiler and runtime for ml models from multiple frameworks,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:48:03.321271Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b4cc05b0-abb0-453c-9e75-30e637b1638b · outbound

This paper cites Mlperf inference benchmark,.

Leveraging Stochastic Depth Training for Adaptive Inference Mlperf inference benchmark,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:48:03.165087Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 1787fbe3-a409-47a2-8a8c-b3a134dfc7d2 · outbound

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

Leveraging Stochastic Depth Training for Adaptive Inference Learning multiple layers of features from tiny images,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:48:02.927882Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Pith citing papers

No inbound Pith citation observations are available.