41 to 50 of 129 Results
Nov 27, 2023 -
Replication Data for: Uncovering LLMs for Service-Composition: Challenges and Opportunities
Python Source Code - 2.0 KB -
MD5: c3b9b8ba04941162ec565d5540c5c4b6
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Nov 27, 2023 -
Replication Data for: Uncovering LLMs for Service-Composition: Challenges and Opportunities
Python Source Code - 1.3 KB -
MD5: 8126f42405ccfd830a5430927f9e1283
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Nov 27, 2023 -
Replication Data for: Uncovering LLMs for Service-Composition: Challenges and Opportunities
Python Source Code - 1.0 KB -
MD5: 0bc8dad5abe549b564a4319d41f38890
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Nov 27, 2023 -
Replication Data for: Uncovering LLMs for Service-Composition: Challenges and Opportunities
Tabular Data - 10.2 KB - 9 Variables, 54 Observations - UNF:6:GV+GzTPs7xXW9ITeS6uC7Q==
List of scenarios found in the Google Scholar literature search. (Exported as text based tabular data.) |
Nov 27, 2023 -
Replication Data for: Uncovering LLMs for Service-Composition: Challenges and Opportunities
MS Excel Spreadsheet - 18.8 KB -
MD5: e9ae404b4c15cf4db615debfb318510d
List of scenarios found in the Google Scholar literature search. (The original file.) |
Sep 27, 2023 - Quantum Computing @IAAS
Mandl, Alexander; Barzen, Johanna; Leymann, Frank; Mangold, Victoria; Riegel, Benedikt; Vietz, Daniel; Winterhalter, Felix, 2023, "Reproduction Code for: On Reducing the Amount of Samples Required for Training of QNNs", https://doi.org/10.18419/darus-3445, DaRUS, V1
Replication code for training Quantum Neural Networks using entangled datasets. This is the version of the code that was used to generate the experiment results in the related publication. For future developments and discussion see the Github repository. Experiments: avg_rank_exp... |
Sep 27, 2023 -
Reproduction Code for: On Reducing the Amount of Samples Required for Training of QNNs
Python Source Code - 2.3 KB -
MD5: 3092220f87583ca17e7cfd73fdacafef
Experiments for training QNNs using training data of varying Schmidt rank. |
Sep 27, 2023 -
Reproduction Code for: On Reducing the Amount of Samples Required for Training of QNNs
Python Source Code - 1.5 KB -
MD5: ff77e11ba2a65fbf30afe3680e7903e6
Cost function and training routines procedures for PyTorch QNN simulation. |
Sep 27, 2023 -
Reproduction Code for: On Reducing the Amount of Samples Required for Training of QNNs
Python Source Code - 3.8 KB -
MD5: b0b7ff540bb8e1b631cff61a652b06f7
Configuration structures for experiments. |
Sep 27, 2023 -
Reproduction Code for: On Reducing the Amount of Samples Required for Training of QNNs
Python Source Code - 273 B -
MD5: b1a0c53c5525b9980bb82bf3784e3c7a
Various functions to modify the loss function after evaluation. |