{"schema_version":"onlylabs.public_signal.v1","title":"NVIDIA Repo: NVIDIA/SymNMF-factors","description":"NVIDIA repo signal with public source context, captured evidence pages, related signals, and data-business radar classification.","url":"https://onlylabs.fyi/signals/efdf09d5-30ce-48fb-ba4e-72a6d28c2358","json_url":"https://onlylabs.fyi/signals/efdf09d5-30ce-48fb-ba4e-72a6d28c2358/signal.json","generated_at":"2026-08-29T18:08:34.961Z","evidence_latest_fetched_at":"2026-08-21T07:05:09.232936+00:00","signal_first_seen_at":"2026-08-21T07:03:28.920983+00:00","org":{"slug":"nvidia","name":"NVIDIA","category":"frontier-lab","category_label":"Frontier lab","dossier_url":"https://onlylabs.fyi/labs/nvidia","dossier_json_url":"https://onlylabs.fyi/labs/nvidia/dossier.json"},"related_urls":{"signal":"https://onlylabs.fyi/signals/efdf09d5-30ce-48fb-ba4e-72a6d28c2358","signal_json":"https://onlylabs.fyi/signals/efdf09d5-30ce-48fb-ba4e-72a6d28c2358/signal.json","source":"https://github.com/NVIDIA/SymNMF-factors","lab_dossier":"https://onlylabs.fyi/labs/nvidia","lab_dossier_json":"https://onlylabs.fyi/labs/nvidia/dossier.json","analysis":"https://onlylabs.fyi/analysis/nvidia","analysis_json":"https://onlylabs.fyi/analysis/nvidia/analysis.json","analysis_evidence_json":"https://onlylabs.fyi/analysis/nvidia/evidence.json","category":"https://onlylabs.fyi/frontier","category_json":"https://onlylabs.fyi/frontier.json","category_feed":"https://onlylabs.fyi/frontier/feed.xml","category_signals_json":"https://onlylabs.fyi/signals.json","topic":null,"topic_signals_json":null,"topic_feed":null,"data_business":{"radar":"https://onlylabs.fyi/data-radar","radar_json":"https://onlylabs.fyi/data-radar.json","opportunities":"https://onlylabs.fyi/opportunities","opportunities_json":"https://onlylabs.fyi/opportunities.json","lanes":[{"key":"infrastructure","label":"Infrastructure","url":"https://onlylabs.fyi/data-radar/infrastructure","json_url":"https://onlylabs.fyi/data-radar/infrastructure/signals.json"}]}},"answer_pack":{"answer":"NVIDIA published NVIDIA/SymNMF-factors (Jupyter Notebook). 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imply eval, data, infrastructure, agent, or deployment work?","Is the repo new evidence for a lab direction that is not yet in writing or releases?","Which related signals should an analyst inspect next?"],"signal_questions":["What does this new repository reveal before a formal announcement exists?","What technical area does this repository expose?","Does the repo imply eval, data, infrastructure, agent, or deployment work?","Which data-business lane explains this signal: Infrastructure?","Do the 6 related repo signals show a repeated pattern?"],"output_fields":["org","repo","technical_theme","data_business_lane","evidence_url"],"data_business_relevance":"New repositories can expose organization build priorities early, especially around internal tooling, eval infrastructure, data systems, deployment, and agent 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Language: Jupyter Notebook License: Apache-2.0 Stars: 1 Forks: 0 Open issues: 0 Created: 2026-07-24T17:43:16Z Pushed: 2026-08-20T22:56:01Z Default branch: main Fork: no Archived: no README: SymNMF-factors GPU-accelerated symmetric non-negative matrix factorization for discovering latent factors in large similarity and dependence matrices. The AdaptGrow solver and the synthetic correlation / TPDM generators follow [Ghita et al. (2026)](https://arxiv.org/abs/2607.24518). Overview SymNMF-factors provides three primary components: 1. A standalone AdaptGrow solver for single-GPU execution. AdaptGrow combines projected AdaGrad with block-sampled stochastic variance-reduced gradient (Block-SVRG), uses a spectral probe to select the initial optimization path, and can transition toward full-batch AdaGrad. 2. PyTorch/NCCL support for running AdaptGrow across multiple GPUs and nodes on prebuilt, row-sharded matrices. 3. 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