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Model release pace

Monthly counts by original publication date. Completed months compare with their previous month; the current month compares equal completed days. A zero denominator has no percentage growth. This is not an exhaustive model census.

Model release pace

Model release pace2024-10-01: 11.00; 2024-11-01: 8.00; 2024-12-01: 13.00; 2025-01-01: 10.00; 2025-02-01: 4.00; 2025-03-01: 10.00; 2025-04-01: 6.00; 2025-05-01: 7.00; 2025-06-01: 4.00; 2025-07-01: 10.00; 2025-08-01: 11.00; 2025-09-01: 8.00; 2025-10-01: 6.00; 2025-11-01: 19.00; 2025-12-01: 12.00; 2026-01-01: 1.00; 2026-02-01: 9.00; 2026-03-01: 9.00; 2026-04-01: 17.00; 2026-05-01: 6.00; 2026-06-01: 9.00; 2026-07-01: 7.00; 2026-08-01: 8.00; 2026-09-01: 12.0009.5192024-10-01: 11.00 2024-11-01: 8.00 2024-12-01: 13.00 2025-01-01: 10.00 2025-02-01: 4.00 2025-03-01: 10.00 2025-04-01: 6.00 2025-05-01: 7.00 2025-06-01: 4.00 2025-07-01: 10.00 2025-08-01: 11.00 2025-09-01: 8.00 2025-10-01: 6.00 2025-11-01: 19.00 2025-12-01: 12.00 2026-01-01: 1.00 2026-02-01: 9.00 2026-03-01: 9.00 2026-04-01: 17.00 2026-05-01: 6.00 2026-06-01: 9.00 2026-07-01: 7.00 2026-08-01: 8.00 2026-09-01: 12.00 10/0109/01 UTC

Monthly release growth

Monthly release growth2024-10-01: -15.38%; 2024-11-01: -27.27%; 2024-12-01: 62.50%; 2025-01-01: -23.08%; 2025-02-01: -60.00%; 2025-03-01: 150.00%; 2025-04-01: -40.00%; 2025-05-01: 16.67%; 2025-06-01: -42.86%; 2025-07-01: 150.00%; 2025-08-01: 10.00%; 2025-09-01: -27.27%; 2025-10-01: -25.00%; 2025-11-01: 216.67%; 2025-12-01: -36.84%; 2026-01-01: -91.67%; 2026-02-01: 800.00%; 2026-03-01: 0.00%; 2026-04-01: 88.89%; 2026-05-01: -64.71%; 2026-06-01: 50.00%; 2026-07-01: -22.22%; 2026-08-01: 14.29%; 2026-09-01: 71.43%-91.7354.28002024-10-01: -15.38 %2024-11-01: -27.27 %2024-12-01: 62.50 %2025-01-01: -23.08 %2025-02-01: -60.00 %2025-03-01: 150.00 %2025-04-01: -40.00 %2025-05-01: 16.67 %2025-06-01: -42.86 %2025-07-01: 150.00 %2025-08-01: 10.00 %2025-09-01: -27.27 %2025-10-01: -25.00 %2025-11-01: 216.67 %2025-12-01: -36.84 %2026-01-01: -91.67 %2026-02-01: 800.00 %2026-03-01: 0.00 %2026-04-01: 88.89 %2026-05-01: -64.71 %2026-06-01: 50.00 %2026-07-01: -22.22 %2026-08-01: 14.29 %2026-09-01: 71.43 %10/0109/01 UTC

Total 1,072 · Page 38/43 · Observed 9/27/2026, 3:30:50 AM

NameGroupReported valueDateNotesSource
NLP from scratchNEC Laboratories,Princeton University—2011-11-08—Source ↗
CNN committee (traffic sign)IDSIA—2011-10-03UnreleasedSource ↗
Adaptive SubgradTechnion - Israel Institute of Technology,Google,University of California (UC) Berkeley—2011-10-03—Source ↗
CNN Committee (NIST)IDSIA—2011-09-18UnreleasedSource ↗
CNN Committee (MNIST)IDSIA—2011-09-18UnreleasedSource ↗
High Performance CNN (NORB)IDSIA,SUPSI—2011-07-16UnreleasedSource ↗
Recursive Neural NetworkStanford University—2011-06-28—Source ↗
Vector Space ModelStanford University—2011-06-19—Source ↗
Cross-Lingual POS TaggerCarnegie Mellon University (CMU),Google Research—2011-06-19—Source ↗
Deep AutoencodersUniversity of Toronto—2011-04-29—Source ↗
Deep rectifier networksUniversity of Montreal / Université de Montréal—2011-04-13—Source ↗
YouTube Video Recommendation SystemGoogle—2010-09-26—Source ↗
RNN LMJohns Hopkins University—2010-09-26—Source ↗
Pooling CNN (NORB)University of Bonn—2010-09-15UnreleasedSource ↗
Pooling CNN (Caltech 101)University of Bonn—2010-09-15UnreleasedSource ↗
ReLU (NORB)University of Toronto—2010-06-15—Source ↗
ReLU (LFW)University of Toronto—2010-06-15—Source ↗
iCCCPMassachusetts Institute of Technology (MIT)—2010-06-13UnreleasedSource ↗
Feedforward NNUniversity of Montreal / Université de Montréal—2010-05-13—Source ↗
Stacked Denoising AutoencodersUniversity of Montreal / Université de Montréal,University of Toronto—2010-01-03—Source ↗
Super-vector codingUniversity of Illinois Urbana-Champaign (UIUC),NEC Laboratories,Rutgers University—2010-01-01—Source ↗
LCNP NORB——2009-11-22—Source ↗
LCNP MNIST——2009-11-22—Source ↗
LCNP LabelMeUniversity of Bonn—2009-11-22—Source ↗
BellKor 2007AT&T—2009-09-21—Source ↗

Data: Epoch AI · CC BY · View source · Automatically synced; not an exhaustive global sample