Search Results for author:
Found 16 papers, 3 papers with code
AiGAS-dEVL: An Adaptive Incremental Neural Gas Model for Drifting Data Streams under Extreme Verification Latency
The ever-growing speed at which data are generated nowadays, together with the substantial cost of labeling processes cause Machine Learning models to face scenarios in which data are partially labeled.
Multiobjective Optimization Analysis for Finding Infrastructure-as-Code Deployment Configurations
Despite the fact that a prototypical version of the IOP has been introduced in the literature before, a deeper analysis focused on the resolution of the problem is needed, in order to determine which is the most appropriate multiobjective method for embedding in the IOP.
Managing the unknown: a survey on Open Set Recognition and tangential areas
In real-world scenarios classification models are often required to perform robustly when predicting samples belonging to classes that have not appeared during its training stage.
Optimizing IaC Configurations: a Case Study Using Nature-inspired Computing
Also, we contextualize the IOP within the complete platform in which it is embedded, describing how a user can benefit from its use.
On the Connection between Concept Drift and Uncertainty in Industrial Artificial Intelligence
AI-based digital twins are at the leading edge of the Industry 4. 0 revolution, which are technologically empowered by the Internet of Things and real-time data analysis.
A Novel Explainable Out-of-Distribution Detection Approach for Spiking Neural Networks
Specifically, this work presents a novel OoD detector that can identify whether test examples input to a Spiking Neural Network belong to the distribution of the data over which it was trained.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
AT-MFCGA: An Adaptive Transfer-guided Multifactorial Cellular Genetic Algorithm for Evolutionary Multitasking
Transfer Optimization is an incipient research area dedicated to solving multiple optimization tasks simultaneously.
CURIE: A Cellular Automaton for Concept Drift Detection
Specifically, in CU RIE the distribution of the data stream is represented in the grid of a cellular automata, whose neighborhood rule can then be utilized to detect possible distribution changes over the stream.
On the Transferability of Knowledge among Vehicle Routing Problems by using Cellular Evolutionary Multitasking
On the other hand, equally interesting is the second contribution, which is focused on the quantitative analysis of the positive genetic transferability among the problem instances.
Deep Echo State Networks for Short-Term Traffic Forecasting: Performance Comparison and Statistical Assessment
Results from this comparison benchmark and the analysis of the statistical significance of the reported performance gaps are decisive: Deep Echo State Networks achieve more accurate traffic forecasts than the rest of considered modeling counterparts.
New Perspectives on the Use of Online Learning for Congestion Level Prediction over Traffic Data
In this manuscript we elaborate on the suitability of online learning methods to predict the road congestion level based on traffic speed time series data.
Multifactorial Cellular Genetic Algorithm (MFCGA): Algorithmic Design, Performance Comparison and Genetic Transferability Analysis
Furthermore, the equally recent concept of Evolutionary Multitasking (EM) refers to multitasking environments adopting concepts from Evolutionary Computation as their inspiration for the simultaneous solving of the problems under consideration.
LUNAR: Cellular Automata for Drifting Data Streams
A lack of efficient and scalable solutions is particularly noted in real-time scenarios where computing resources are severely constrained, as it occurs in networks of small, numerous, interconnected processing units (such as the so-called Smart Dust, Utility Fog, or Swarm Robotics paradigms).
Unsupervised Anomaly Detection in Stream Data with Online Evolving Spiking Neural Networks
The proposed OeSNN-UAD detector was experimentally compared with state-of-the-art unsupervised and semi-supervised detectors of anomalies in stream data from the Numenta Anomaly Benchmark and Yahoo Anomaly Datasets repositories.
Spiking Neural Networks and Online Learning: An Overview and Perspectives
Specially in these non-stationary scenarios, there is a pressing need for new algorithms that adapt to these changes as fast as possible, while maintaining good performance scores.
Exploiting a Stimuli Encoding Scheme of Spiking Neural Networks for Stream Learning
Stream data processing has gained progressive momentum with the arriving of new stream applications and big data scenarios.
