Automatic Machine Learning (AutoML) is a powerful mechanism to design and tune models.
Technology ecosystems often undergo significant transformations as they mature.
Distributed, Parallel, and Cluster Computing
Modern distributed applications increasingly rely on cloud-native platforms to abstract the complexity of deployment and scalability.
Software Engineering Distributed, Parallel, and Cluster Computing
As more and more companies are migrating (or planning to migrate) from on-premise to Cloud, their focus is to find anomalies and deficits as early as possible in the development life cycle.
Distributed, Parallel, and Cluster Computing
System operators responsible for protecting software applications remain hesitant to implement cyber deception technology, including methods that place traps to catch attackers, despite its proven benefits.
Cryptography and Security
Deep learning driven by large neural network models is overtaking traditional machine learning methods for understanding unstructured and perceptual data domains such as speech, text, and vision.
Distributed, Parallel, and Cluster Computing
A very simple way to improve the performance of almost any machine learning algorithm is to train many different models on the same data and then to average their predictions.
Ranked #20 on
Knowledge Distillation
on CIFAR-100
Although more layers and more parameters generally improve the accuracy of the models, such big models generally have high computational complexity and require big memory, which exceed the capacity of small devices for inference and incurs long training time.
Modern cyber-physical systems (CPS) such as Cooperative Intelligent Transport Systems (C-ITS) are increasingly defined by the software which operates these systems.
Robotics Distributed, Parallel, and Cluster Computing Software Engineering
This study examines 971 GitHub repositories that incorporate 10 popular chaos engineering tools to identify patterns and trends in their use.
Software Engineering