Paper Citation Record · LEDGER

Torch.manual_seed(3407) is all you need: On the influence of random seeds in deep learning architectures for computer vision

As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 20 inbound Pith citation observations for arXiv:2109.08203.

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

pith.paper-citation-record.v1
2109.08203 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 20 of 20 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 20 of 20 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:05:06.968328Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
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malformed identifier0
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External citation measurements

6
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation c81b0dac-c21c-40c9-8030-8eef4ebc1217 · inbound

Differentiable SVD based on Moore-Penrose Pseudoinverse for Inverse Imaging Problems cites this paper.

Differentiable SVD based on Moore-Penrose Pseudoinverse for Inverse Imaging Problems Torch.manual_seed(3407) is all you need: On the influence of random seeds in deep learning architectures for computer vision

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-12T15:34:53.341357Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation deef6ad2-3be2-4ed1-ab1c-818200ffda6f · inbound

Systemizing Multiplicity: The Curious Case of Arbitrariness in Machine Learning cites this paper.

Systemizing Multiplicity: The Curious Case of Arbitrariness in Machine Learning Torch.manual_seed(3407) is all you need: On the influence of random seeds in deep learning architectures for computer vision

Reference 818

Resolution: unresolved; no resolver link, observed 2026-08-10T14:49:40.274067Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 635c86b2-e590-4535-bda3-b33e1a8d7e96 · inbound

Beyond Anonymization: Object Scrubbing for Privacy-Preserving 2D and 3D Vision Tasks cites this paper.

Beyond Anonymization: Object Scrubbing for Privacy-Preserving 2D and 3D Vision Tasks Torch.manual_seed(3407) is all you need: On the influence of random seeds in deep learning architectures for computer vision

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-16T11:05:06.968328Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 52f97d96-a53d-48e0-ba85-e8e64c1acd64 · inbound

Beginning with You: Perceptual-Initialization Improves Vision-Language Representation and Alignment cites this paper.

Beginning with You: Perceptual-Initialization Improves Vision-Language Representation and Alignment Torch.manual_seed(3407) is all you need: On the influence of random seeds in deep learning architectures for computer vision

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T15:42:28.215006Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7fe46835-5bfa-4855-b2c4-ec64bded2c43 · inbound

Towards more transferable adversarial attack in black-box manner cites this paper.

Towards more transferable adversarial attack in black-box manner Torch.manual_seed(3407) is all you need: On the influence of random seeds in deep learning architectures for computer vision

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-07T14:41:11.564844Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 85f0f758-44c1-48f6-9379-c7dbe8376f25 · inbound

Multi-Loco: Unifying Multi-Embodiment Legged Locomotion via Reinforcement Learning Augmented Diffusion cites this paper.

Multi-Loco: Unifying Multi-Embodiment Legged Locomotion via Reinforcement Learning Augmented Diffusion Torch.manual_seed(3407) is all you need: On the influence of random seeds in deep learning architectures for computer vision

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-07T04:10:46.432493Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 54bfea88-6ae9-486d-80d8-fe4d093f67bc · inbound

Language-Unlocked ViT (LUViT): Empowering Self-Supervised Vision Transformers with LLMs cites this paper.

Language-Unlocked ViT (LUViT): Empowering Self-Supervised Vision Transformers with LLMs Torch.manual_seed(3407) is all you need: On the influence of random seeds in deep learning architectures for computer vision

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-06T21:17:09.069919Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 69e1190d-a107-4c89-9dff-1b409f9c727f · inbound

Algorithmic Tradeoffs, Applied NLP, and the State-of-the-Art Fallacy cites this paper.

Algorithmic Tradeoffs, Applied NLP, and the State-of-the-Art Fallacy Torch.manual_seed(3407) is all you need: On the influence of random seeds in deep learning architectures for computer vision

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-04T21:10:32.670160Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 250bb636-a444-4107-ab08-901ba2dbd34c · inbound

On the Extreme Variance of Certified Local Robustness Across Model Seeds cites this paper.

On the Extreme Variance of Certified Local Robustness Across Model Seeds Torch.manual_seed(3407) is all you need: On the influence of random seeds in deep learning architectures for computer vision

Reference 26

Resolution: metadata mismatch; arxiv_id, observed 2026-05-16T12:57:54.064399Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 3d415058-f3aa-446d-9cf5-49322a33117f · inbound

If It's Good Enough for You, It's Good Enough for Me: Transferability of Audio Sufficiencies across Models cites this paper.

If It's Good Enough for You, It's Good Enough for Me: Transferability of Audio Sufficiencies across Models Torch.manual_seed(3407) is all you need: On the influence of random seeds in deep learning architectures for computer vision

Reference 51

Resolution: metadata mismatch; arxiv_id, observed 2026-05-13T18:58:08.988413Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 33896ef6-5d60-4420-8405-d498f5bb145a · inbound

Making Uncertainty Visible: Multiverse Analysis for Robust Computational Social Science cites this paper.

Making Uncertainty Visible: Multiverse Analysis for Robust Computational Social Science Torch.manual_seed(3407) is all you need: On the influence of random seeds in deep learning architectures for computer vision

Reference 53

Resolution: verified exact; arxiv_id, observed 2026-05-20T01:37:55.577637Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation bb570fdb-f830-4140-aede-307d0cc8d023 · inbound

Crossing the Validation Crisis: Cross-Validation Reduces Benchmarking Variance Surprisingly Well cites this paper.

Crossing the Validation Crisis: Cross-Validation Reduces Benchmarking Variance Surprisingly Well Torch.manual_seed(3407) is all you need: On the influence of random seeds in deep learning architectures for computer vision

Reference 9

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T10:17:57.696635Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 70291cff-827e-4024-9395-13d45284bb5e · inbound

The FID Lottery: Quantifying Hidden Randomness in Generative-Model Evaluation cites this paper.

The FID Lottery: Quantifying Hidden Randomness in Generative-Model Evaluation Torch.manual_seed(3407) is all you need: On the influence of random seeds in deep learning architectures for computer vision

Reference 76

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T03:49:29.492324Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 318a9d01-9fd2-4292-81e0-cfe984ed951d · inbound

GRAIN: Group Aggregation via Min-Norm Objective cites this paper.

GRAIN: Group Aggregation via Min-Norm Objective Torch.manual_seed(3407) is all you need: On the influence of random seeds in deep learning architectures for computer vision

Reference 74

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T09:59:45.282380Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 1e0afd5a-d0d3-4fed-a2a7-17e955aeb821 · inbound

Kolmogorov Arnold networks (KAN) for aerodynamic prediction: a comparison with MLPs and GNNs cites this paper.

Kolmogorov Arnold networks (KAN) for aerodynamic prediction: a comparison with MLPs and GNNs Torch.manual_seed(3407) is all you need: On the influence of random seeds in deep learning architectures for computer vision

Reference 32

Resolution: verified exact; arxiv_id, observed 2026-07-04T13:29:51.571597Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 8a36a644-f658-4fb9-9ae6-a78a6a180fd9 · inbound

Grokking Is Conditional and Fragile: A Fully-Tractable, Multi-Seed Study at 12K Parameters cites this paper.

Grokking Is Conditional and Fragile: A Fully-Tractable, Multi-Seed Study at 12K Parameters Torch.manual_seed(3407) is all you need: On the influence of random seeds in deep learning architectures for computer vision

Reference 10

Resolution: unresolved; no resolver link, observed 2026-07-11T08:46:29.099447Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5751f1c2-e360-4468-8265-cd588822b662 · inbound

GLID: Gated Local Intrinsic Dimension Repairs the Blind Spots of Face-Forgery Detectors cites this paper.

GLID: Gated Local Intrinsic Dimension Repairs the Blind Spots of Face-Forgery Detectors Torch.manual_seed(3407) is all you need: On the influence of random seeds in deep learning architectures for computer vision

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-01T14:29:55.587246Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 45c8e570-0195-4450-bc90-a484d934f59d · inbound

Pretrain on Small Synthetic Data, Scale Large for Free: Symmetry-Aware Foundation Model for Logic Rule Induction cites this paper.

Pretrain on Small Synthetic Data, Scale Large for Free: Symmetry-Aware Foundation Model for Logic Rule Induction Torch.manual_seed(3407) is all you need: On the influence of random seeds in deep learning architectures for computer vision

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-05T04:18:38.138012Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3729fe4c-43b0-49b3-bb3f-79672664f8a0 · inbound

Can Training Logs Make Model Comparisons More Precise? cites this paper.

Can Training Logs Make Model Comparisons More Precise? Torch.manual_seed(3407) is all you need: On the influence of random seeds in deep learning architectures for computer vision

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-15T15:08:17.548073Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 31290ca3-40b9-4948-9863-e48c8f6f1b2a · inbound

Seeds Before Objectives: Rethinking Evaluation for Low-Resource Garhwali ASR cites this paper.

Seeds Before Objectives: Rethinking Evaluation for Low-Resource Garhwali ASR Torch.manual_seed(3407) is all you need: On the influence of random seeds in deep learning architectures for computer vision

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-12T19:47:23.672117Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.