Architecture Descriptions, Model Checkpoints, and Training Histories for A4NN Workflow on Protein Diffraction Data
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:'This artifact contains scripts, input, and output datasets that reinforce the reproducibility of our results in our ICPP 2023 paper “Composable Workflow for Accelerating Neural Architecture Search Using In Situ Analytics for Protein Classification” (see README.txt). The input data comprises simulated protein diffraction patterns from X-ray Free Electron Laser (XFEL) experiments at low, medium, and high beam intensities. The datasets for each beam intensity contain 63,508 images for training and 15,876 images for testing (80/20 train-test split). The output dataset contains neural network (NN) models and metadata generated with the Analytics for Neural Networks (A4NN) workflow for several laser beam intensities on different GPU distributions. Each experiment contains 100 NN models that train for 25 epochs (max) each. There are approximat' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["deep learning","early termination","Neural Architecture Search","neural networks","predictive modeling","protein diffraction"]} <|eot_id|>
 Here is the Description of a dataset. Description:'This artifact contains scripts, input, and output datasets that reinforce the reproducibility of our results in our ICPP 2023 paper “Composable Workflow for Accelerating Neural Architecture Search Using In Situ Analytics for Protein Classification” (see README.txt). The input data comprises simulated protein diffraction patterns from X-ray Free Electron Laser (XFEL) experiments at low, medium, and high beam intensities. The datasets for each beam intensity contain 63,508 images for training and 15,876 images for testing (80/20 train-test split). The output dataset contains neural network (NN) models and metadata generated with the Analytics for Neural Networks (A4NN) workflow for several laser beam intensities on different GPU distributions. Each experiment contains 100 NN models that train for 25 epochs (max) each. There are approximat' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
This artifact contains scripts, input, and output datasets that reinforce the reproducibility of our results in our ICPP 2023 paper “Composable Workflow for Accelerating Neural Architecture Search Using In Situ Analytics for Protein Classification” (see README.txt). The input data comprises simulated protein diffraction patterns from X-ray Free Electron Laser (XFEL) experiments at low, medium, and high beam intensities. The datasets for each beam intensity contain 63,508 images for training and 15,876 images for testing (80/20 train-test split). The output dataset contains neural network (NN) models and metadata generated with the Analytics for Neural Networks (A4NN) workflow for several laser beam intensities on different GPU distributions. Each experiment contains 100 NN models that train for 25 epochs (max) each. There are approximat
deep learning; early termination; Neural Architecture Search; neural networks; predictive modeling; protein diffraction