Pith. sign in

Paper Citation Record · LEDGER

LSQ+: Improving low-bit quantization through learnable offsets and better initialization

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2004.09576.

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

pith.paper-citation-record.v1
2004.09576 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T22:26:32.469983Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-10T10:44:37.870409Z

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 365e6472-1de2-4944-9675-74414df8976c · inbound

Dedicated Inference Engine and Binary-Weight Neural Networks for Lightweight Instance Segmentation cites this paper.

Dedicated Inference Engine and Binary-Weight Neural Networks for Lightweight Instance Segmentation LSQ+: Improving low-bit quantization through learnable offsets and better initialization

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-10T22:26:32.469983Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T22:26:32.469983Z digest=sha256:6a3be33c522917f6771c1d2b5e21d2c677437032da8516d5e4ab56f75c8c2d7b

Observation c17871c1-e203-4480-9a1c-317d35c332cc · inbound

A Comparative Study of CNN Optimization Methods for Edge AI: Exploring the Role of Early Exits cites this paper.

A Comparative Study of CNN Optimization Methods for Edge AI: Exploring the Role of Early Exits LSQ+: Improving low-bit quantization through learnable offsets and better initialization

Reference 61

Resolution
verified exact
arxiv_id, observed 2026-05-10T10:44:37.873143Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-05-10T10:40:36.879366Z digest=sha256:6eb5f300c11acf0d436eefae6d6129d2e3f5ac6f6487fcada1b0389abcff1ad9