Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Training deep neural networks with low precision multiplications
As of 5 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:1412.7024.
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 2026-08-03T07:36:26.002981Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-07-04T19:30:07.164029Z
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 90f1da44-f20d-460a-b1dc-7c44fc04e113 · inbound
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications cites this paper.
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications Training deep neural networks with low precision multiplications
Reference 4
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T02:50:40.391966Z
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 fe2c70f1-59a3-4e63-9637-45b6495a3fbf · inbound
Template-Based Posit Multiplication for Training and Inferring in Neural Networks cites this paper.
Template-Based Posit Multiplication for Training and Inferring in Neural Networks Training deep neural networks with low precision multiplications
Reference 33
- Resolution
- verified exact
- local_arxiv, observed 2026-05-25T00:35:08.432407Z
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 161c94c5-b913-4c7e-8802-9baf92fb8da5 · inbound
LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale cites this paper.
LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale Training deep neural networks with low precision multiplications
Reference 127
- Resolution
- verified exact
- arxiv_id, observed 2026-05-13T13:35:36.060829Z
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 ecc20d5b-922d-4613-89c9-b6c06c84579e · inbound
Computing k-means in mixed precision cites this paper.
Computing k-means in mixed precision Training deep neural networks with low precision multiplications
Reference 30
- Resolution
- verified exact
- local_arxiv, observed 2026-05-23T22:33:32.398749Z
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 53fe2433-8aab-45e2-b69a-813608494fec · inbound
How Much Progress Has There Been in NVIDIA Datacenter GPUs? cites this paper.
How Much Progress Has There Been in NVIDIA Datacenter GPUs? Training deep neural networks with low precision multiplications
Reference 49
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T07:36:26.002981Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1699a15e-2566-45cd-96b1-a371a412b761 · inbound
Llamas on the Web: Memory-Efficient, Performance-Portable, and Multi-Precision LLM Inference with WebGPU cites this paper.
Llamas on the Web: Memory-Efficient, Performance-Portable, and Multi-Precision LLM Inference with WebGPU Training deep neural networks with low precision multiplications
Reference 7
- Resolution
- verified exact
- local_arxiv, observed 2026-05-21T02:53:55.322692Z
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 3bfe0d9c-8a46-4225-840e-63de58286632 · inbound
Ego-METAS: Egocentric online Multimodal Energy-efficient Temporal Action Segmentation benchmark cites this paper.
Ego-METAS: Egocentric online Multimodal Energy-efficient Temporal Action Segmentation benchmark Training deep neural networks with low precision multiplications
Reference 8
- Resolution
- verified exact
- local_arxiv, observed 2026-06-28T22:42:46.941196Z
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 4475252a-0ea6-4637-966c-dcb035cfd4f9 · inbound
Efficient Remote Sensing Instance Segmentation with Linear-Time State Space Distilled Visual Foundation Models cites this paper.
Efficient Remote Sensing Instance Segmentation with Linear-Time State Space Distilled Visual Foundation Models Training deep neural networks with low precision multiplications
Reference 69
- Resolution
- verified exact
- local_arxiv, observed 2026-07-04T19:30:07.165230Z
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.