Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-05-09T14:30:15.740453Z
Paper Citation Record · LEDGER
Certified vs. Empirical Adversarial Robust-ness via Hybrid Convolutions with Attention Stochasticity
As of 2 August 2026, this Paper Citation Record lists 4 of 4 outbound references and 1 inbound Pith citation observation for arXiv:2605.01519.
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 2026-08-02T06:30:47.504484+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-01T13:33:12.582478Z
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
4 of 4 outbound references displayed
- verified exact1
- verified fuzzy2
- unresolved0
- parse uncertain0
- malformed identifier1
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation dc4f3e8e-e1ae-4d1a-97cd-33708bf0983b · outbound
This paper cites Francesco Croce, Sven Gowal, Thomas Brunner, Evan Shelhamer, Matthias Hein, and Taylan Cemgil.
Certified vs. Empirical Adversarial Robust-ness via Hybrid Convolutions with Attention Stochasticity Francesco Croce, Sven Gowal, Thomas Brunner, Evan Shelhamer, Matthias Hein, and Taylan Cemgil
Reference 1
- Resolution
- verified exact
- doi, observed 2026-05-09T22:24:01.268573Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.
Observation 2a3ee3c0-556c-4ae9-bc6c-bc72f3b8c56a · outbound
This paper cites We certify the smoothed classifier gσ(x) ≜ arg max Pε,Ω[fθ(x + ε; Ω) = c].
Certified vs. Empirical Adversarial Robust-ness via Hybrid Convolutions with Attention Stochasticity We certify the smoothed classifier gσ(x) ≜ arg max Pε,Ω[fθ(x + ε; Ω) = c]
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T02:01:30.476235Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.
Observation 3391e493-fa32-4d58-bf5e-61a6f0275bd2 · outbound
This paper cites Independently of input noise, we certify the backbone + internal randomness by averaging logits only over Ω: Z(x) ≜ EΩ [ s(x; Ω) ], with Lip(Z) ≤ 2.
Certified vs. Empirical Adversarial Robust-ness via Hybrid Convolutions with Attention Stochasticity Independently of input noise, we certify the backbone + internal randomness by averaging logits only over Ω: Z(x) ≜ EΩ [ s(x; Ω) ], with Lip(Z) ≤ 2
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T02:01:30.480173Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.
Observation a3bb0710-a564-493b-8794-90de618a8bf5 · outbound
This paper cites an unresolved cited work.
Certified vs. Empirical Adversarial Robust-ness via Hybrid Convolutions with Attention Stochasticity Unresolved cited work
Reference 4
- Resolution
- malformed identifier
- raw_fallback, observed 2026-05-26T02:01:30.483704Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.
Pith citing papers
Observation e8a6bdf3-e427-40bc-a0b6-e3da76ebfde4 · inbound
Advancing Multimodal Fusion on Heterogeneous Medical Data with Hybrid Geometry Attention cites this paper.
Advancing Multimodal Fusion on Heterogeneous Medical Data with Hybrid Geometry Attention Certified vs. Empirical Adversarial Robust-ness via Hybrid Convolutions with Attention Stochasticity
Reference 17
- Resolution
- unresolved
- no resolver link, observed 2026-08-01T13:33:12.582478Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.