Machine Learning functionalities per library CaffeCaffe2XGBoostTensorRTNCNNLibtorchTensorflowT-SNEDlib Serving Training (CPU) Y Y Y N/A N/A Y N Y N Training (GPU) Y Y Y N/A N/A Y N Y N Inference (CPU) Y Y Y N Y Y Y N/A Y Inference (GPU) Y Y Y Y N Y Y N/A Y Mo...
A specification of a property required to be upheld by a computerized machine learning system is obtained. A training data set corresponding to the property and inputs and outputs of the system is built. The system is trained on the training data set. Activity of the system is monitored befor...
usability testingemotion detectionBrain-Computer Interfacechannel selectionEEG signal processingdeep-learningrecurrent neural networkRECOGNITIONIt is becoming increasingly attractive to detect human emotions using electroencephalography (EEG) brain signals. EEG is a reliable and cost-effective technology used to ...
DIGITS (theDeep LearningGPUTrainingSystem) is a webapp for training deep learning models. Note:We are no longer adding features, fixing bugs, or supporting the NVIDIA Deep Learning GPU Training System (DIGITS) software. You may continue to use the software if it meets your needs. However: ...
The deep-learning model has better robustness insoftware reliabilityprediction. Abstract Different software reliability models, such as parameter and non-parameter models, have been developed in the past four decades to assess software reliability in the software testing process. Although these models can...
Deep Learning (DL) systems are key enablers for engineering intelligent applications due to their ability to solve complex tasks such as image recognition and machine translation. Nevertheless, using DL systems in safety- and security-critical applications requires to provide testing evidence for their ...
learning through Batch-RL and models the state-action value function with a Graph Neural Network. We apply DRIFT to testing the Windows 10 operating system and show that DRIFT can robustly trigger the desired software functionality in a fully automated manner. Our experiments test the abili...
Efficient software testing is essential for productive software development and reliable user experiences. As human testing is inefficient and expensive, automated software testing is needed. In this work, we propose a Reinforcement Learning (RL) framework for functional software testing named DRIFT. DRIF...
Deep learning is a subset of machine learning that uses multilayered neural networks, to simulate the complex decision-making power of the human brain.
Log data Anomaly detection Neural networks Deep learning 1. Introduction Log files provide a rich source of information when it comes to monitoring computer systems. Thereby, the majority of log events are usually generated as consequences of normal system operations, such as starting and stopping of...