Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
LitE-SNN: Designing Lightweight and Efficient Spiking Neural Network through Spatial-Temporal Compressive Network Search and Joint Optimization
As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2401.14652.
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 2026-08-09T06:31:02.800959+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-08T17:34:36.893724Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-18T10:46:17.256775Z
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 a81271fe-bdcf-4d25-b283-4f7e93a3ae6f · inbound
QP-SNN: Quantized and Pruned Spiking Neural Networks cites this paper.
QP-SNN: Quantized and Pruned Spiking Neural Networks LitE-SNN: Designing Lightweight and Efficient Spiking Neural Network through Spatial-Temporal Compressive Network Search and Joint Optimization
Reference 13
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T17:34:36.893724Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 42264e34-0f32-406e-ae4e-5eb083ba124b · inbound
SPEAR: Structured Pruning for Spiking Neural Networks via Synaptic Operation Estimation and Reinforcement Learning cites this paper.
SPEAR: Structured Pruning for Spiking Neural Networks via Synaptic Operation Estimation and Reinforcement Learning LitE-SNN: Designing Lightweight and Efficient Spiking Neural Network through Spatial-Temporal Compressive Network Search and Joint Optimization
Reference 38
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T21:59:44.011784Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 43245387-ca3d-4bfd-a209-bc64183f3b73 · inbound
SAFA-SNN: Sparsity-Aware On-Device Few-Shot Class-Incremental Learning with Fast-Adaptive Structure of Spiking Neural Network cites this paper.
SAFA-SNN: Sparsity-Aware On-Device Few-Shot Class-Incremental Learning with Fast-Adaptive Structure of Spiking Neural Network LitE-SNN: Designing Lightweight and Efficient Spiking Neural Network through Spatial-Temporal Compressive Network Search and Joint Optimization
Reference 1
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T10:46:17.259595Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 78db46b2-29d9-4dee-acee-978b4307a4ec · inbound
Spiking Neural Network Architecture Search: A Survey cites this paper.
Spiking Neural Network Architecture Search: A Survey LitE-SNN: Designing Lightweight and Efficient Spiking Neural Network through Spatial-Temporal Compressive Network Search and Joint Optimization
Reference 25
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-18T07:01:01.376829Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.