Search Results for author:
Found 17 papers, 1 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.
Computer-Aided Cytology Diagnosis in Animals: CNN-Based Image Quality Assessment for Accurate Disease Classification
The research sheds light on the significance of CNN-based IQA in computer-aided cytology diagnosis for animals, enhancing the accuracy of disease classification.
Using simulation to calibrate real data acquisition in veterinary medicine
This paper explores the innovative use of simulation environments to enhance data acquisition and diagnostics in veterinary medicine, focusing specifically on gait analysis in dogs.
Using super-resolution for enhancing visual perception and segmentation performance in veterinary cytology
The primary objective of this research was to enhance the quality of semantic segmentation in cytology images by incorporating super-resolution (SR) architectures.
Estimation of River Water Surface Elevation Using UAV Photogrammetry and Machine Learning
The data set was supplemented with data collected by other researchers that compared the state-of-the-art methods for determining WSE using an UAV.
Segmentation of the veterinary cytological images for fast neoplastic tumors diagnosis
This paper shows the machine learning system which performs instance segmentation of cytological images in veterinary medicine.
Chosen methods of improving small object recognition with weak recognizable features
In this work the GAN-based method with augmentation is presented to improve small object detection on VOC Pascal dataset.
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.
Neuroevolution deep learning architecture search for estimation of river surface elevation from photogrammetric Digital Surface Models
For our knowledge this is the first approach in which dataset was created for this purpose and deep learning models were used for this task.
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).
Convolutional neural network compression for natural language processing
The method responsible for mapping compressed network to FPGA and results of this implementation are presented.
Improving text classification with vectors of reduced precision
This paper presents the analysis of the impact of a floating-point number precision reduction on the quality of text classification.
Using Spatial Pooler of Hierarchical Temporal Memory to classify noisy videos with predefined complexity
The authors conducted a series of experiments for various macro parameters of HTM SP, as well as for different levels of video reduction ratios.
OpenCL-accelerated object classification in video streams using Spatial Pooler of Hierarchical Temporal Memory
The classification accuracy of the system was examined through a series of experiments and the performance was given in terms of F1 score as a function of the number of columns, synapses, $min\_overlap$ and $winners\_set\_size$.
