Institute for Architecture for Application Systems

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41 to 50 of 115 Results

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.)
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.
cuda_qnn.py 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.
data.py 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.
generate_experiments.py 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.

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.)

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.

cuda_qnn.py

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

Python Source Code - 18.5 KB -

QNN ansatz implementations for classical simulation using PyTorch.

data.py

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

Python Source Code - 14.4 KB -

Data generation routines for the experiments.

generate_experiments.py

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

Python Source Code - 13.2 KB -

Experiment entry point. Generates training data, calls simulation and training routines and saves results. Also responsible for distributing experiment workloads to multiple processors.

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