[HTML][HTML] Deep neural networks in the cloud: Review, applications, challenges and research directions
Deep neural networks (DNNs) are currently being deployed as machine learning technology
in a wide range of important real-world applications. DNNs consist of a huge number of …
in a wide range of important real-world applications. DNNs consist of a huge number of …
Evolutionary multitask optimization: a methodological overview, challenges, and future research directions
In this work, we consider multitasking in the context of solving multiple optimization problems
simultaneously by conducting a single search process. The principal goal when dealing with …
simultaneously by conducting a single search process. The principal goal when dealing with …
Comprehensive taxonomies of nature-and bio-inspired optimization: Inspiration versus algorithmic behavior, critical analysis recommendations
In recent algorithmic family simulates different biological processes observed in Nature in
order to efficiently address complex optimization problems. In the last years the number of …
order to efficiently address complex optimization problems. In the last years the number of …
General Purpose Artificial Intelligence Systems (GPAIS): Properties, definition, taxonomy, societal implications and responsible governance
Abstract Most applications of Artificial Intelligence (AI) are designed for a confined and
specific task. However, there are many scenarios that call for a more general AI, capable of …
specific task. However, there are many scenarios that call for a more general AI, capable of …
Hybrid approaches to optimization and machine learning methods: a systematic literature review
Notably, real problems are increasingly complex and require sophisticated models and
algorithms capable of quickly dealing with large data sets and finding optimal solutions …
algorithms capable of quickly dealing with large data sets and finding optimal solutions …
Adaptive multifactorial evolutionary optimization for multitask reinforcement learning
Evolutionary computation has largely exhibited its potential to complement conventional
learning algorithms in a variety of machine learning tasks, especially those related to …
learning algorithms in a variety of machine learning tasks, especially those related to …
EvoPruneDeepTL: An evolutionary pruning model for transfer learning based deep neural networks
Abstract In recent years, Deep Learning models have shown a great performance in
complex optimization problems. They generally require large training datasets, which is a …
complex optimization problems. They generally require large training datasets, which is a …
Vessel-GAN: Angiographic reconstructions from myocardial CT perfusion with explainable generative adversarial networks
Dynamic CT angiography derived from CT perfusion data can obviate a separate coronary
CT angiography and the use of ionizing radiation and contrast agent, thereby enhancing …
CT angiography and the use of ionizing radiation and contrast agent, thereby enhancing …
Genetic programming-based evolutionary deep learning for data-efficient image classification
Data-efficient image classification is a challenging task that aims to solve image
classification using small training data. Neural network-based deep learning methods are …
classification using small training data. Neural network-based deep learning methods are …
General Purpose Artificial Intelligence Systems (GPAIS): Properties, Definition, Taxonomy, Open Challenges and Implications
Most applications of Artificial Intelligence (AI) are designed for a confined and specific task.
However, there are many scenarios that call for a more general AI, capable of solving a wide …
However, there are many scenarios that call for a more general AI, capable of solving a wide …