Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-10T16:28:52.219106Z
Paper Citation Record · LEDGER
Learning in Log-Domain: Subthreshold Analog AI Accelerator Based on Stochastic Gradient Descent
As of 16 August 2026, this Paper Citation Record lists 24 of 24 outbound references and 0 inbound Pith citation observations for arXiv:2501.13181.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
24 of 24 outbound references displayed
- verified exact0
- verified fuzzy19
- unresolved5
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 86d6d0ce-3447-43ed-9850-042540f12ac7 · outbound
This paper cites Privacy-preserving heterogeneous federated transfer learning.
Learning in Log-Domain: Subthreshold Analog AI Accelerator Based on Stochastic Gradient Descent Privacy-preserving heterogeneous federated transfer learning
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T16:28:52.625174Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 5e1cd179-1158-4ff7-8009-37e72ba9292f · outbound
This paper cites Attention is all you need.
Learning in Log-Domain: Subthreshold Analog AI Accelerator Based on Stochastic Gradient Descent Attention is all you need
Reference 2
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T16:28:52.112959Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7131b67a-07ed-4048-9ce4-554c16994aa3 · outbound
This paper cites Wu, Andrew Y.
Learning in Log-Domain: Subthreshold Analog AI Accelerator Based on Stochastic Gradient Descent Wu, Andrew Y
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T16:28:52.598569Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation d39be8a1-37ac-4f76-9df3-7f2da7c55323 · outbound
This paper cites Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models.
Learning in Log-Domain: Subthreshold Analog AI Accelerator Based on Stochastic Gradient Descent Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models
Reference 4
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T16:28:52.123369Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 96203fcb-f6af-4816-8e0a-45747765a3c9 · outbound
This paper cites Yodann: An ultra-low power convolutional neural network accelerator based on binary weights.
Learning in Log-Domain: Subthreshold Analog AI Accelerator Based on Stochastic Gradient Descent Yodann: An ultra-low power convolutional neural network accelerator based on binary weights
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T16:28:52.583203Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation b8b274a6-8629-4245-a00a-220c0e3b4374 · outbound
This paper cites Energy and policy considerations for deep learning in NLP.
Learning in Log-Domain: Subthreshold Analog AI Accelerator Based on Stochastic Gradient Descent Energy and policy considerations for deep learning in NLP
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T16:28:52.568910Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 6619d426-bc4d-4833-a788-55787bc8c651 · outbound
This paper cites Patrick Xiao, Christopher H.
Learning in Log-Domain: Subthreshold Analog AI Accelerator Based on Stochastic Gradient Descent Patrick Xiao, Christopher H
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T16:28:52.554045Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 3a4b0a50-3577-4fa1-b5ca-2d450d023126 · outbound
This paper cites an unresolved cited work.
Learning in Log-Domain: Subthreshold Analog AI Accelerator Based on Stochastic Gradient Descent Unresolved cited work
Reference 8
- Resolution
- unresolved
- raw_fallback, observed 2026-08-10T16:28:52.538095Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation cd454893-dfc8-4948-85e6-e58083123c69 · outbound
This paper cites Survey of Machine Learning Accelerators.
Learning in Log-Domain: Subthreshold Analog AI Accelerator Based on Stochastic Gradient Descent Survey of Machine Learning Accelerators
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T16:28:52.522333Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation a8c0ce76-b0b4-45a8-b2f6-7f49fa13fa3f · outbound
This paper cites an unresolved cited work.
Learning in Log-Domain: Subthreshold Analog AI Accelerator Based on Stochastic Gradient Descent Unresolved cited work
Reference 10
- Resolution
- unresolved
- raw_fallback, observed 2026-08-10T16:28:52.505782Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation ef7b0b0d-7c8f-4198-8b0e-cac720c67a21 · outbound
This paper cites an unresolved cited work.
Learning in Log-Domain: Subthreshold Analog AI Accelerator Based on Stochastic Gradient Descent Unresolved cited work
Reference 11
- Resolution
- unresolved
- raw_fallback, observed 2026-08-10T16:28:52.489972Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 0af3d663-c1ee-4189-9813-e4ff1935ec07 · outbound
This paper cites Unpu: A 50.6tops/w unified deep neural network accelerator with 1b-to-16b fully-variable weight bit-precision.
Learning in Log-Domain: Subthreshold Analog AI Accelerator Based on Stochastic Gradient Descent Unpu: A 50.6tops/w unified deep neural network accelerator with 1b-to-16b fully-variable weight bit-precision
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T16:28:52.475214Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 194beae2-9fb9-45ec-be31-b808af32a6ac · outbound
This paper cites Brein memory: A single-chip binary/ternary reconfigurable in-memory deep neural network accelerator achieving 1.4 tops at 0.6 w.
Learning in Log-Domain: Subthreshold Analog AI Accelerator Based on Stochastic Gradient Descent Brein memory: A single-chip binary/ternary reconfigurable in-memory deep neural network accelerator achieving 1.4 tops at 0.6 w
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T16:28:52.459847Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation e2fc2084-6592-419f-a30a-36f9d75abd42 · outbound
This paper cites The carbon footprint of machine learning.
Learning in Log-Domain: Subthreshold Analog AI Accelerator Based on Stochastic Gradient Descent The carbon footprint of machine learning
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T16:28:52.445302Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 1c084ae8-865c-4453-9961-7236c5ed6c26 · outbound
This paper cites Stanley Williams, Paolo Faraboschi, Wen-mei W Hwu, John Paul Strachan, Kaushik Roy, and Dejan S.
Learning in Log-Domain: Subthreshold Analog AI Accelerator Based on Stochastic Gradient Descent Stanley Williams, Paolo Faraboschi, Wen-mei W Hwu, John Paul Strachan, Kaushik Roy, and Dejan S
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T16:28:52.429788Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 088bee8b-209c-4c58-9d0d-65434225c72e · outbound
This paper cites log-domain state-space.
Learning in Log-Domain: Subthreshold Analog AI Accelerator Based on Stochastic Gradient Descent log-domain state-space
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T16:28:52.413796Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 5a063830-64db-460d-8bd0-209b8eeffd65 · outbound
This paper cites Lyon, and Emmanuel.
Learning in Log-Domain: Subthreshold Analog AI Accelerator Based on Stochastic Gradient Descent Lyon, and Emmanuel
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T16:28:52.398152Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation e3acf30a-8989-4e1c-9b57-c9741fec9fec · outbound
This paper cites Hedonic housing prices and the demand for clean air.
Learning in Log-Domain: Subthreshold Analog AI Accelerator Based on Stochastic Gradient Descent Hedonic housing prices and the demand for clean air
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T16:28:52.381421Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 3f77a263-c8f2-4b03-b184-271d0b3cc26a · outbound
This paper cites The scikit-learn boston housing dataset documentation.
Learning in Log-Domain: Subthreshold Analog AI Accelerator Based on Stochastic Gradient Descent The scikit-learn boston housing dataset documentation
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T16:28:52.365923Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 1e91d87b-dadb-4bf5-a369-eb7ef9a74e42 · outbound
This paper cites The boston housing dataset and fairness concerns.
Learning in Log-Domain: Subthreshold Analog AI Accelerator Based on Stochastic Gradient Descent The boston housing dataset and fairness concerns
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T16:28:52.348544Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 113f2e32-f298-4ced-b316-662af359245f · outbound
This paper cites A comparative study of different curve fitting algorithms in artificial neural network using housing dataset.
Learning in Log-Domain: Subthreshold Analog AI Accelerator Based on Stochastic Gradient Descent A comparative study of different curve fitting algorithms in artificial neural network using housing dataset
Reference 21
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T16:28:52.329297Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation ea42760d-f20e-47c0-8194-35425a3e867d · outbound
This paper cites Gerosa, A.
Learning in Log-Domain: Subthreshold Analog AI Accelerator Based on Stochastic Gradient Descent Gerosa, A
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T16:28:52.310665Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation d252aef8-0d58-4501-a4b9-dadbedc9b81e · outbound
This paper cites Seevinck.
Learning in Log-Domain: Subthreshold Analog AI Accelerator Based on Stochastic Gradient Descent Seevinck
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T16:28:52.294221Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 8e59c9cd-1d8e-4e17-b49b-524559c474b6 · outbound
This paper cites Moro-Frias, M.
Learning in Log-Domain: Subthreshold Analog AI Accelerator Based on Stochastic Gradient Descent Moro-Frias, M
Reference 24
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T16:28:52.278213Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Pith citing papers
No inbound Pith citation observations are available.