Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
BinaryConnect: Training Deep Neural Networks with binary weights during propagations
As of 5 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:1511.00363.
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 2026-08-05T06:32:48.257954+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-02T01:35:42.874126Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-05-25T16:56:04.232498Z
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 ae9ff84e-c3fd-4d53-b7c4-371eab37a584 · inbound
New pointwise convolution in Deep Neural Networks through Extremely Fast and Non Parametric Transforms cites this paper.
New pointwise convolution in Deep Neural Networks through Extremely Fast and Non Parametric Transforms BinaryConnect: Training Deep Neural Networks with binary weights during propagations
Reference 4
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-05-25T16:56:04.235797Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation c66cd5cc-ad5a-41cf-9f2b-33493be549c9 · inbound
Single-bit-per-weight deep convolutional neural networks without batch-normalization layers for embedded systems cites this paper.
Single-bit-per-weight deep convolutional neural networks without batch-normalization layers for embedded systems BinaryConnect: Training Deep Neural Networks with binary weights during propagations
Reference 8
- Resolution
- verified exact
- local_arxiv, observed 2026-05-24T21:06:22.777887Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation f8ff096c-9014-417d-8162-38e9123e68fa · inbound
Learning Instance-wise Sparsity for Accelerating Deep Models cites this paper.
Learning Instance-wise Sparsity for Accelerating Deep Models BinaryConnect: Training Deep Neural Networks with binary weights during propagations
Reference 3
- Resolution
- verified exact
- local_arxiv, observed 2026-05-24T15:16:14.179308Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 8b83fe6a-78bb-46c8-8610-6ddb01c86d1c · inbound
VeloxQ: A Fast and Efficient QUBO Solver cites this paper.
VeloxQ: A Fast and Efficient QUBO Solver BinaryConnect: Training Deep Neural Networks with binary weights during propagations
Reference 71
- Resolution
- verified exact
- local_arxiv, observed 2026-05-23T04:42:34.304659Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 2e097ed9-5bb6-4d59-8110-1dbd3dd59eae · inbound
A Geometric Analysis of Sign-Magnitude Asymmetry in a ReLU + RMSNorm Block under Ternary Quantization cites this paper.
A Geometric Analysis of Sign-Magnitude Asymmetry in a ReLU + RMSNorm Block under Ternary Quantization BinaryConnect: Training Deep Neural Networks with binary weights during propagations
Reference 3
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-05-20T13:08:17.811117Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation e4eb882a-a147-4cac-9d50-7907714d7f01 · inbound
Cross-Layer Error Compensation and Finite-Sample Feature-Statistics Matching for Extreme Low-Bit Quantization of Large Language Models cites this paper.
Cross-Layer Error Compensation and Finite-Sample Feature-Statistics Matching for Extreme Low-Bit Quantization of Large Language Models BinaryConnect: Training Deep Neural Networks with binary weights during propagations
Reference 8
- Resolution
- unresolved
- no resolver link, observed 2026-08-02T01:35:42.874126Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.