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Found 6 papers, 0 papers with code
AD-NEv++ : The multi-architecture neuroevolution-based multivariate anomaly detection framework
Anomaly detection tools and methods enable key analytical capabilities in modern cyberphysical and sensor-based systems.
Towards efficient deep autoencoders for multivariate time series anomaly detection
First, pruning reduces the number of weights, while preventing catastrophic drops in accuracy by means of a fast search process that identifies high sparsity levels.
Ada-QPacknet -- adaptive pruning with bit width reduction as an efficient continual learning method without forgetting
Most of them have many problems with learning in dynamic and complex environments.
Speedup deep learning models on GPU by taking advantage of efficient unstructured pruning and bit-width reduction
One of the most common techniques for improving the efficiency of CNN models is weight pruning and quantization.
Ensemble neuroevolution based approach for multivariate time series anomaly detection
To our knowledge, this is the first approach in which an ensemble deep learning anomaly detection model is built in a fully automatic way using a neuroevolution strategy.
Training with reduced precision of a support vector machine model for text classification
This paper presents the impact of using quantization on the efficiency of multi-class text classification in the training process of a support vector machine (SVM).
