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Paper Citation Record · LEDGER

Towards energy-efficient Deep Learning: An overview of energy-efficient approaches along the Deep Learning Lifecycle

As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2303.01980.

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

pith.paper-citation-record.v1
2303.01980 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T12:07:51.759907Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-04T10:09:45.443803Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation e8e8cc9c-e635-41ef-878f-c9491d315be4 · inbound

Connectivity for AI enabled cities -- A field survey based study of emerging economies cites this paper.

Connectivity for AI enabled cities -- A field survey based study of emerging economies Towards energy-efficient Deep Learning: An overview of energy-efficient approaches along the Deep Learning Lifecycle

Reference 35

Resolution
unresolved
no resolver link, observed 2026-08-10T20:02:49.794137Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T20:02:49.794137Z digest=sha256:34e7cdcf826558b0a3f2fb9445ec0e85b02a5994d274793f74f565efee68ad97

Observation fac197d2-c542-4f7f-91c7-e3e05f693f3f · inbound

Word Embedding Techniques for Classification of Star Ratings cites this paper.

Word Embedding Techniques for Classification of Star Ratings Towards energy-efficient Deep Learning: An overview of energy-efficient approaches along the Deep Learning Lifecycle

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-16T12:07:51.759907Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T12:07:51.759907Z digest=sha256:9d0c16c837b4b50174ac3b6aac6cc74cbf72d7f818a674b1fc8815efc0b79da1

Observation ad7c6112-3499-418f-986b-5da523c5078c · inbound

Energy Consumption in Parallel Neural Network Training cites this paper.

Energy Consumption in Parallel Neural Network Training Towards energy-efficient Deep Learning: An overview of energy-efficient approaches along the Deep Learning Lifecycle

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-05T21:59:28.450074Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T21:59:28.450074Z digest=sha256:1b423a389f1355e42439a905567e6e11598c605df696b2dac386137278c721eb

Observation d292e714-5d8d-4a05-bc4e-1114cfa0a617 · inbound

AI Application Benchmarking: Power-Aware Performance Analysis for Vision and Language Models cites this paper.

AI Application Benchmarking: Power-Aware Performance Analysis for Vision and Language Models Towards energy-efficient Deep Learning: An overview of energy-efficient approaches along the Deep Learning Lifecycle

Reference 9

Resolution
unresolved
no resolver link, observed 2026-07-13T23:54:54.872017Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-13T23:54:54.872017Z digest=sha256:53a4c9998ea16c24075b42746538c9ec87d4a3e94105048624c0348a2f6da236

Observation b0d9fd75-5a0a-4e46-b1e2-d4d3d582cb45 · inbound

The Energy Consumption of Transformer Fine-Tuning: A Roofline-Inspired Scaling Model cites this paper.

The Energy Consumption of Transformer Fine-Tuning: A Roofline-Inspired Scaling Model Towards energy-efficient Deep Learning: An overview of energy-efficient approaches along the Deep Learning Lifecycle

Reference 38

Resolution
verified exact
arxiv_id, observed 2026-07-04T10:09:45.445219Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-06-26T09:04:05.257526Z digest=sha256:88773bed80e3302bedeb41ccc7f3b8ea25d48eaed1a82863c5947946be6a77a2