...twork learning techniques influenced by [[wikipedia:Hebbian theory|Hebbian learning]], attempting to determine whether collaborative volunteer computing infras
...GPU clusters, Axiom Distributed AI experimented with decentralized machine learning architectures inspired by biological neural adaptation.
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9 KB (1,145 words) - 11:46, 2 June 2026
| size = Variable virtual machine workloads
...ources to assist with computational nanotechnology simulations and machine learning research.<ref name="purdue2019">{{cite web |url=https://it.purdue.edu/newsr
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13 KB (1,564 words) - 13:34, 4 July 2026
| sponsor = Cognition, Robotics, and Learning (CORAL) Lab, University of Maryland, Baltimore County
...ture for Network Computing|BOINC]] project focused specifically on machine learning research.<ref name="mlds_paper">{{Cite arxiv |author=Clemens, John |year=20
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15 KB (2,130 words) - 12:17, 11 June 2026
...ons of chess positions, building a labeled dataset intended for training a machine-learned classifier of winning positions.
| category = Computer science, Machine learning
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7 KB (908 words) - 09:46, 20 July 2026
...]] on problems drawn from fields ranging from materials science to machine learning.<ref>{{Cite web |url=https://en.wikipedia.org/wiki/AQUA@home |title=AQUA@ho
...on binary classification problems for [[wikipedia:machine learning|machine learning]], developed in part with researchers from [[wikipedia:Google|Google]].<ref
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11 KB (1,351 words) - 15:26, 27 June 2026
At the same time, the field of [[machine learning]] was producing models capable of predicting molecular properties at a frac
...On Windows and macOS, the calculations ran inside a [[VirtualBox]] virtual machine to ensure consistent results across heterogeneous hardware.<ref name="bcwik
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21 KB (2,861 words) - 12:48, 9 June 2026
...h sample is sent to a volunteer's computer as a work unit. The volunteer's machine runs the [[CAMB]] code to compute the CMB [[power spectrum]] for that param
=== PICO: machine learning from volunteer results ===
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17 KB (2,368 words) - 20:20, 6 June 2026
|* [[Chess@Home]] build a labeled dataset intended for training a machine-learned classifier of winning position
|* [[FreeHAL@home]] generate and convert semantic networks for the self-learning chatbot FreeHAL
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19 KB (2,028 words) - 14:53, 30 August 2026
| name = Ramanujan Machine on BOINC
[https://rnma.xyz/boinc/ '''''Ramanujan Machine'''''] is a [[wikipedia:Berkeley Open Infrastructure for Network Computing|B
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20 KB (2,655 words) - 15:42, 18 June 2026
...BOINC project that generated and converted semantic networks for the self-learning chatbot FreeHAL, developed by Tobias Schulz.
...p databases ([[semantic network]]s) for '''FreeHAL''', an open-source self-learning [[chatbot]].<ref name="wayback2011">{{cite web |title=FreeHAL@home |url=htt
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18 KB (2,436 words) - 13:47, 24 August 2026
...mputing, evolutionary search techniques, optimization methods, and machine learning approaches to search for compact programs that reproduce OEIS sequences cor
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10 KB (1,270 words) - 00:31, 25 May 2026
...tionary Algorithms to optimize the parameters of different kind of machine learning algorithms.
...ons of chess positions, building a labeled dataset intended for training a machine-learned classifier of winning positions. [[Chess960@home|See more...]]
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94 KB (12,433 words) - 14:22, 30 August 2026
# Create an educational tool for learning about plant life: while indexing keywords, the participant's computer would
...web/20071023063008/http://www.scilinc.org/ SciLINC website] at the Wayback Machine
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22 KB (2,954 words) - 18:31, 14 August 2026
...mulations. Modern Rosetta methods also incorporate statistical and machine-learning-assisted scoring functions to improve prediction accuracy.
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17 KB (2,106 words) - 13:37, 29 May 2026
...rg/html/2502.16455v1 Asteroid shape inversion with light curves using deep learning (2025), referencing DAMIT's model count.]</ref>
...ch interval is independent and can be processed by any available volunteer machine.
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22 KB (2,950 words) - 15:07, 26 May 2026
...00}\,\text{GigaFLOP-day}</math>). The WCG servers compare claims from each machine that processed the same work unit, discard outliers, and award the averaged
...ve computational biology''' — combining large-scale data analysis, machine learning, and network biology to understand complex diseases.
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86 KB (11,993 words) - 18:40, 8 July 2026