DNNPerf can be easily adapted to predict a variety of runtime performance metrics for both model training and inference, such as inference time, GPU utilization, and GPU power consumption. To achieve this, users need to collect new training data with the relevant metrics as labels. Note that ...
loop nest cost = --metrics #intrinsic cache-usage Task scheduler Use same timeslice for all subgraph in the firstiteration, and get the gradient of improvement dy/dt, the biggest value means this subgraph is worth to tune and will give it more timeslice in the next iteration. Key contribut...
This section describes the results of our experiments and quantify the performance of our proposed models for predicting surface pressure fields and associated scalar values using error metrics discussed in Section 6.2. Conclusion In this paper, a geometric deep learning-based approach is presented to ...
It is now well established that more than one performance metrics are necessary for evaluating a multi-objective evolutionary algorithm (MOEA). Although there exist a number of performance metrics in the MOEA literature, most of them are applied to the final non-dominated set obtained by an MOEA...
2870 Accesses 6 Citations 2 Altmetric Metrics details Abstract Modern machine learning (ML) and deep learning (DL) techniques using high-dimensional data representations have helped accelerate the materials discovery process by efficiently detecting hidden patterns in existing datasets and linking input ...
Generalized additive models (GAM) are shown to be effective in modelling DL performance metrics based on the number of training images per class, tuning scheme and dataset. Key-words: Camera Traps, Deep Learning, Ecological Informatics, Generalised Additive Models, Learning Curves, Predictive ...
for train, test in kfold.split(X, Y): model = Sequential() model.add(Dense(n, input_dim=dim, init=’uniform’, activation=’sigmoid’)) model.add(Dense(1, init=’uniform’, activation=’sigmoid’)) model.compile(loss=’mse’, optimizer=’adam’, metrics=[‘accuracy’]) asd = mode...
On quantizing a FP32 model using the Intel Low Precision Optimization Tool, users can either leverage the pre-defined accuracy metrics supported by the tool, or customize evaluating function, evaluation dataset and accuracy metrics for calibration. It’s expected using pre-def...
3740 Accesses 117 Citations 37 Altmetric Metrics details A preprint version of the article is available at arXiv.Abstract Recent research has demonstrated that feature attribution methods for deep networks can themselves be incorporated into training; these attribution priors optimize for a model whose ...
5814 Accesses Metrics details Abstract We evaluated the diagnostic performance and generalizability of traditional machine learning and deep learning models for distinguishing glioblastoma from single brain metastasis using radiomics. The training and external validation cohorts comprised 166 (109 glioblastomas ...