Institute for Architecture for Application Systems

Featured Dataverses

In order to use this feature you must have at least one published or linked dataverse.

Publish Dataverse

Are you sure you want to publish your dataverse? Once you do so it must remain published.

Publish Dataverse

This dataverse cannot be published because the dataverse it is in has not been published.

Delete Dataverse

Are you sure you want to delete your dataverse? You cannot undelete this dataverse.

41 to 50 of 129 Results

composition.py Nov 27, 2023 - Replication Data for: Uncovering LLMs for Service-Composition: Challenges and Opportunities Python Source Code - 2.0 KB - MD5: c3b9b8ba04941162ec565d5540c5c4b6
composition.py Nov 27, 2023 - Replication Data for: Uncovering LLMs for Service-Composition: Challenges and Opportunities Python Source Code - 1.3 KB - MD5: 8126f42405ccfd830a5430927f9e1283
composition.py Nov 27, 2023 - Replication Data for: Uncovering LLMs for Service-Composition: Challenges and Opportunities Python Source Code - 1.0 KB - MD5: 0bc8dad5abe549b564a4319d41f38890
Exemplars.tab 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.)
Exemplars.xlsx 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.)
Reproduction Code for: On Reducing the Amount of Samples Required for Training of QNNs 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...
avg_rank_exp.py 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.
classic_training.py 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.
config.py 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.
cost_modifying_functions.py 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.

composition.py

Nov 27, 2023 - Replication Data for: Uncovering LLMs for Service-Composition: Challenges and Opportunities

Python Source Code - 2.0 KB -

composition.py

Nov 27, 2023 - Replication Data for: Uncovering LLMs for Service-Composition: Challenges and Opportunities

Python Source Code - 1.3 KB -

composition.py

Nov 27, 2023 - Replication Data for: Uncovering LLMs for Service-Composition: Challenges and Opportunities

Python Source Code - 1.0 KB -

Exemplars.tab

Nov 27, 2023 - Replication Data for: Uncovering LLMs for Service-Composition: Challenges and Opportunities

Tabular Data - 10.2 KB - 9 Variables, 54 Observations -

List of scenarios found in the Google Scholar literature search. (Exported as text based tabular data.)

Exemplars.xlsx

Nov 27, 2023 - Replication Data for: Uncovering LLMs for Service-Composition: Challenges and Opportunities

MS Excel Spreadsheet - 18.8 KB -

List of scenarios found in the Google Scholar literature search. (The original file.)

Reproduction Code for: On Reducing the Amount of Samples Required for Training of QNNs

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...

avg_rank_exp.py

Sep 27, 2023 - Reproduction Code for: On Reducing the Amount of Samples Required for Training of QNNs

Python Source Code - 2.3 KB -

Experiments for training QNNs using training data of varying Schmidt rank.

classic_training.py

Sep 27, 2023 - Reproduction Code for: On Reducing the Amount of Samples Required for Training of QNNs

Python Source Code - 1.5 KB -

Cost function and training routines procedures for PyTorch QNN simulation.

config.py

Sep 27, 2023 - Reproduction Code for: On Reducing the Amount of Samples Required for Training of QNNs

Python Source Code - 3.8 KB -

Configuration structures for experiments.

cost_modifying_functions.py

Sep 27, 2023 - Reproduction Code for: On Reducing the Amount of Samples Required for Training of QNNs

Python Source Code - 273 B -

Various functions to modify the loss function after evaluation.

Add Data

Share Dataverse

Link Dataverse

Reset Modifications