Designing large deep learning neural networks by hand requires tuning large sets of method parameters, requiring trial and error testing and domain specific knowledge. Neuroevolution methods such as CoDeepNeat (CDN), based on Neuroevolution of Augmenting Topologies (NEAT), apply evolutionary algorithms to automate deep neural network parameter optimisation. This paper presents and demonstrates various novel beneficial extensions to the CDN method, including new genotypic speciation mechanisms, special mappings in deep neural network encodings, as well as evolving Data Augmentation schemes. Results indicate that these CDN method variants yield significant task-performance benefits over the benchmark CDN method when evaluated on a po...
This work focuses on automatization of neural network design via the so-called neuroevolution, which...
In recent years, deep learning has revolutionized computer vision and has been applied to a range of...
Convolutional neural networks (CNNs) have exhibited significant performance gains over conventional ...
A variety of methods have been applied to the architectural configuration and learning or training o...
Deep neural networks (DNNs) have produced state-of-the-art results in many benchmarks and problem do...
Deep artificial neural networks require a large corpus of training data in order to effectively lear...
Image classification problems often face the issues of high dimensionality and large variance within...
Mención Internacional en el título de doctorFor three decades, neuroevolution has applied evolutiona...
[EN] Deep Neural Networks (DNN) have become a powerful, and extremely popular mechanism, which has b...
Neural networks have achieved widespread adoption due to both their applicability to a wide range of...
Santos, F. J. J. B., Gonçalves, I., & Castelli, M. (2023). Neuroevolution with box mutation: An adap...
Neuro-Evolution is a field of study that has recently gained significantly increased traction in the...
Deep learning is a new research direction in the field of machine learning. It is a subclass of mach...
Deep Learning is based on deep neural networks trained over huge sets of examples. It enabled comp...
In the past few years, deep learning has become a very important research field that has attracted a...
This work focuses on automatization of neural network design via the so-called neuroevolution, which...
In recent years, deep learning has revolutionized computer vision and has been applied to a range of...
Convolutional neural networks (CNNs) have exhibited significant performance gains over conventional ...
A variety of methods have been applied to the architectural configuration and learning or training o...
Deep neural networks (DNNs) have produced state-of-the-art results in many benchmarks and problem do...
Deep artificial neural networks require a large corpus of training data in order to effectively lear...
Image classification problems often face the issues of high dimensionality and large variance within...
Mención Internacional en el título de doctorFor three decades, neuroevolution has applied evolutiona...
[EN] Deep Neural Networks (DNN) have become a powerful, and extremely popular mechanism, which has b...
Neural networks have achieved widespread adoption due to both their applicability to a wide range of...
Santos, F. J. J. B., Gonçalves, I., & Castelli, M. (2023). Neuroevolution with box mutation: An adap...
Neuro-Evolution is a field of study that has recently gained significantly increased traction in the...
Deep learning is a new research direction in the field of machine learning. It is a subclass of mach...
Deep Learning is based on deep neural networks trained over huge sets of examples. It enabled comp...
In the past few years, deep learning has become a very important research field that has attracted a...
This work focuses on automatization of neural network design via the so-called neuroevolution, which...
In recent years, deep learning has revolutionized computer vision and has been applied to a range of...
Convolutional neural networks (CNNs) have exhibited significant performance gains over conventional ...