41 to 50 of 118 Results
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 -
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. |
Sep 27, 2023 -
Reproduction Code for: On Reducing the Amount of Samples Required for Training of QNNs
Python Source Code - 18.5 KB -
MD5: ee5babfdaf1828206a38944c1f26c713
QNN ansatz implementations for classical simulation using PyTorch. |
Sep 27, 2023 -
Reproduction Code for: On Reducing the Amount of Samples Required for Training of QNNs
Python Source Code - 14.4 KB -
MD5: 05a60d0e08fe086be30c1982b88ee9a8
Data generation routines for the experiments. |
Sep 27, 2023 -
Reproduction Code for: On Reducing the Amount of Samples Required for Training of QNNs
Python Source Code - 13.2 KB -
MD5: 27ae2978ce34969769e840b21da5526c
Experiment entry point. Generates training data, calls simulation and training routines and saves results. Also responsible for distributing experiment workloads to multiple processors. |