Paper Citation Record · LEDGER

Machine Learning in Python: Main developments and technology trends in data science, machine learning, and artificial intelligence

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2002.04803.

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

pith.paper-citation-record.v1
2002.04803 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 9 of 9 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T14:32:35.447097Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T19:40:07.397197Z

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 238b94ef-7d9e-4926-81a5-fdfcd7788419 · inbound

Occam's model: Selecting simpler representations for better transferability estimation cites this paper.

Occam's model: Selecting simpler representations for better transferability estimation Machine Learning in Python: Main developments and technology trends in data science, machine learning, and artificial intelligence

Reference 2017

Resolution: malformed identifier; no resolver link, observed 2026-08-08T14:32:35.447097Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 98595e19-a561-4b77-9822-7b4bf7573daa · inbound

Self-Supervised Dynamical System Representations for Physiological Time-Series cites this paper.

Self-Supervised Dynamical System Representations for Physiological Time-Series Machine Learning in Python: Main developments and technology trends in data science, machine learning, and artificial intelligence

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-03T19:34:05.177212Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a45693f0-91e4-480d-8e5e-dda475e75a7e · inbound

Obliviator Reveals the Cost of Nonlinear Guardedness in Concept Erasure cites this paper.

Obliviator Reveals the Cost of Nonlinear Guardedness in Concept Erasure Machine Learning in Python: Main developments and technology trends in data science, machine learning, and artificial intelligence

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-05-15T14:56:08.283470Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation fc4b6695-d043-419c-a121-27c420d4623a · inbound

Novel GPU Boruta algorithms for feature selection from high-dimensional data cites this paper.

Novel GPU Boruta algorithms for feature selection from high-dimensional data Machine Learning in Python: Main developments and technology trends in data science, machine learning, and artificial intelligence

Reference 33

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T06:51:27.161224Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 7c28044a-af7c-4dc4-9d49-042a768148f3 · inbound

ASH: Agents that Self-Hone via Embodied Learning cites this paper.

ASH: Agents that Self-Hone via Embodied Learning Machine Learning in Python: Main developments and technology trends in data science, machine learning, and artificial intelligence

Reference 48

Resolution: verified exact; arxiv_id, observed 2026-05-15T02:53:33.511434Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 45661661-ce07-4b2f-8110-4c8fb41daf64 · inbound

ASH: Agents that Self-Hone via Embodied Learning cites this paper.

ASH: Agents that Self-Hone via Embodied Learning Machine Learning in Python: Main developments and technology trends in data science, machine learning, and artificial intelligence

Reference 48

Resolution: verified exact; arxiv_id, observed 2026-06-30T21:15:04.094965Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 757dc16c-cf96-4f45-bdfe-734564e218c6 · inbound

Secrets Best Not Shared: DNS Privacy Enhancements for the Constrained IoT cites this paper.

Secrets Best Not Shared: DNS Privacy Enhancements for the Constrained IoT Machine Learning in Python: Main developments and technology trends in data science, machine learning, and artificial intelligence

Reference 81

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T02:07:33.914851Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation fbd8a514-8ebc-4123-8415-9e3ef58e0c23 · inbound

Minimalist Genetic Programming cites this paper.

Minimalist Genetic Programming Machine Learning in Python: Main developments and technology trends in data science, machine learning, and artificial intelligence

Reference 67

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T02:07:34.153067Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 2e362fe2-d8a2-4460-babc-66d1ce46671a · inbound

Probing in the Wild: A Case Study of Self-Supervised Speech Representations on Mandarin Sub-dialects with Unsupervised Articulatory Analysis cites this paper.

Probing in the Wild: A Case Study of Self-Supervised Speech Representations on Mandarin Sub-dialects with Unsupervised Articulatory Analysis Machine Learning in Python: Main developments and technology trends in data science, machine learning, and artificial intelligence

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-07-04T19:40:07.399522Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.