Randomization-based machine learning in renewable energy prediction problems: Critical literature review, new results and perspectives
In the last few years, methods falling within the family of randomization-based machine
learning models have grasped a great interest in the Artificial Intelligence community, mainly …
learning models have grasped a great interest in the Artificial Intelligence community, mainly …
[HTML][HTML] A new approach based on association rules to add explainability to time series forecasting models
Abstract Machine learning and deep learning have become the most useful and powerful
tools in the last years to mine information from large datasets. Despite the successful …
tools in the last years to mine information from large datasets. Despite the successful …
Black-box error diagnosis in Deep Neural Networks for computer vision: a survey of tools
Abstract The application of Deep Neural Networks (DNNs) to a broad variety of tasks
demands methods for co** with the complex and opaque nature of these architectures …
demands methods for co** with the complex and opaque nature of these architectures …
[HTML][HTML] ForecastExplainer: Explainable household energy demand forecasting by approximating shapley values using DeepLIFT
The rapid progress in sensor technology has empowered smart home systems to efficiently
monitor and control household appliances. AI-enabled smart home systems can forecast …
monitor and control household appliances. AI-enabled smart home systems can forecast …
Using echo state networks to inform physical models for fire front propagation
Wildfires can be devastating, causing significant damage to property, ecosystem disruption,
and loss of life. Forecasting the evolution of wildfire boundaries is essential to real-time …
and loss of life. Forecasting the evolution of wildfire boundaries is essential to real-time …
Exploring deep echo state networks for image classification: A multi-reservoir approach
Echo state networks (ESNs) belong to the class of recurrent neural networks and have
demonstrated robust performance in time series prediction tasks. In this study, we …
demonstrated robust performance in time series prediction tasks. In this study, we …
Characterizing climate pathways using feature importance on echo state networks
K Goode, D Ries, K McClernon - Statistical Analysis and Data …, 2024 - Wiley Online Library
Abstract The 2022 National Defense Strategy of the United States listed climate change as a
serious threat to national security. Climate intervention methods, such as stratospheric …
serious threat to national security. Climate intervention methods, such as stratospheric …
Energy-Efficient Edge and Cloud Image Classification with Multi-Reservoir Echo State Network and Data Processing Units
In an era dominated by Internet of Things (IoT) devices, software-as-a-service (SaaS)
platforms, and rapid advances in cloud and edge computing, the demand for efficient and …
platforms, and rapid advances in cloud and edge computing, the demand for efficient and …
Characterizing climate pathways using feature importance on echo state networks
K Goode, D Ries, K McClernon - arxiv preprint arxiv:2310.08495, 2023 - arxiv.org
The 2022 National Defense Strategy of the United States listed climate change as a serious
threat to national security. Climate intervention methods, such as stratospheric aerosol …
threat to national security. Climate intervention methods, such as stratospheric aerosol …
Assembly of echo state networks driven by segregated low dimensional signals
An echo state network (ESN), consisting of an input layer, reservoir, and output layer,
provides a higher learning-efficient approach than other recurrent neural networks (RNNs) …
provides a higher learning-efficient approach than other recurrent neural networks (RNNs) …