Artificial Neural Networks, as the name itself suggests, are biologically inspired algorithms designed to simulate the way in which the human brain processes information. Like neurons, which consist of a cell nucleus that receives input from other neurons through a web of input terminals, an Artificial Neural Network includes hundreds of single units, artificial neurons or processing elements, connected with coefficients (weights), and are organized in layers. The power of neural computations comes from connecting neurons in a network: in fact, in an Artificial Neural Network it is possible to manage a different number of information at the same time. What is not fully understood is which is the most efficient way to train an Artificial Neural Network, and in particular what is the best mini-batch size for maximize accuracy while minimizing training time. The idea that will be developed in this study has its roots in the biological world, that inspired the creation of Artificial Neural Network in the first place. Humans have altered the face of the world through extraordinary adaptive and technological advances: those changes were made possible by our cognitive structure, particularly the ability to reasoning and build causal models of external events. This dynamism is made possible by a high degree of curiosity. In the biological world, and especially in human beings, curiosity arises from the constant search of knowledge and information: behaviours that support the information sampling mechanism range from the very small (initial mini-batch size) to the very elaborate sustained (increasing mini-batch size). The goal of this project is to train an Artificial Neural Network by increasing dynamically, in an adaptive manner (with validation set), the mini-batch size; our hypothesis is that this training method will be more efficient (in terms of time and costs) compared to the ones implemented so far.

Artificial Neural Networks: The Missing Link Between Curiosity and Accuracy / Franchini, G.; Burgio, P.; Zanni, L.. - 941:(2020), pp. 1025-1034. ((Intervento presentato al convegno Joint Conferences on 18th International Conference on Intelligent Systems Design and Applications, ISDA 2018 and 10th World Congress on Nature and Biologically Inspired Computing , NaBIC 2018 tenutosi a ind nel 2018 [10.1007/978-3-030-16660-1_100].

Artificial Neural Networks: The Missing Link Between Curiosity and Accuracy

Franchini G.;Burgio P.;Zanni L.
2020

Abstract

Artificial Neural Networks, as the name itself suggests, are biologically inspired algorithms designed to simulate the way in which the human brain processes information. Like neurons, which consist of a cell nucleus that receives input from other neurons through a web of input terminals, an Artificial Neural Network includes hundreds of single units, artificial neurons or processing elements, connected with coefficients (weights), and are organized in layers. The power of neural computations comes from connecting neurons in a network: in fact, in an Artificial Neural Network it is possible to manage a different number of information at the same time. What is not fully understood is which is the most efficient way to train an Artificial Neural Network, and in particular what is the best mini-batch size for maximize accuracy while minimizing training time. The idea that will be developed in this study has its roots in the biological world, that inspired the creation of Artificial Neural Network in the first place. Humans have altered the face of the world through extraordinary adaptive and technological advances: those changes were made possible by our cognitive structure, particularly the ability to reasoning and build causal models of external events. This dynamism is made possible by a high degree of curiosity. In the biological world, and especially in human beings, curiosity arises from the constant search of knowledge and information: behaviours that support the information sampling mechanism range from the very small (initial mini-batch size) to the very elaborate sustained (increasing mini-batch size). The goal of this project is to train an Artificial Neural Network by increasing dynamically, in an adaptive manner (with validation set), the mini-batch size; our hypothesis is that this training method will be more efficient (in terms of time and costs) compared to the ones implemented so far.
14-apr-2019
Joint Conferences on 18th International Conference on Intelligent Systems Design and Applications, ISDA 2018 and 10th World Congress on Nature and Biologically Inspired Computing , NaBIC 2018
ind
2018
941
1025
1034
Franchini, G.; Burgio, P.; Zanni, L.
Artificial Neural Networks: The Missing Link Between Curiosity and Accuracy / Franchini, G.; Burgio, P.; Zanni, L.. - 941:(2020), pp. 1025-1034. ((Intervento presentato al convegno Joint Conferences on 18th International Conference on Intelligent Systems Design and Applications, ISDA 2018 and 10th World Congress on Nature and Biologically Inspired Computing , NaBIC 2018 tenutosi a ind nel 2018 [10.1007/978-3-030-16660-1_100].
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