devutilsFrançais · article non traduitGitHub A particular research result
Krizhevsky, Sutskever and Hinton's 2012 paper reports image classification using a deep convolutional network and an efficient GPU implementation. It documents a combination of data, training, architecture and evaluation rather than the invention of every neural network or the beginning of artificial intelligence.
Keep the benchmark attached to its task
An image-classification result concerns the stated data and evaluation procedure. It cannot establish how a system will perform on every new population, prove general intelligence or supply a complete explanation of later AI systems.
Context and limits
The modern-computing chapter places this publication beside the much earlier Dartmouth proposal, GPU programming, data evaluation and physical resource limits. Its classification-accuracy exercises are invented and must not be confused with this paper's measured results. This event uses publication-year precision. No model training, benchmark reproduction or independent replication was performed for the site's assessment.
Verification record
- Sources assessed by Codex (assistant)
- No executable example check recorded
- Independent human review pending
Selected claims have been assessed against the passages below. This record does not certify every sentence or guarantee correctness.
Inspect claims, evidence and limitations
Source assessment: . 1 of 1 selected claims marked supported by the assessor. This is not a coverage percentage for the article.
The 2012 paper by Krizhevsky, Sutskever and Hinton documents a specific image-classification system and its evaluation.
Assessor finding: supported · historical · Article passage
- NeurIPS 2012, ImageNet Classification with Deep Convolutional Neural Networks — Title, author list, NIPS 2012 proceedings and abstract
Passage read: Named passage inspected during the related chapter assessment in this session.
Selected documented milestone; no unqualified first-ever priority claim.
- NeurIPS 2012, ImageNet Classification with Deep Convolutional Neural Networks — Title, author list, NIPS 2012 proceedings and abstract
Reproducible checks
These historical or editorial claims have no executable truth test. Source assessment and human review remain separate.
Limits and open work
- Publication, experiment and deployment are distinguished; no historical hardware or software was executed for this event.
- Independent human review remains pending.
Independent human review
Pending. No independent human reviewer has signed off this version.
Content fingerprint: fbd3bf57adcff0174d80275a631e3d13362953e975db52a159dd3aab736bfce1. An article change invalidates this assessment.
Sources
Published by devutils · Updated · Original explanatory text; sources retain their own terms.