Abstract
Artificial neural networks are computer software or hardware models inspired by the structure and behavior of neurons in the human nervous system. As a powerful learning tool, increasingly neural networks have been adopted by many large-scale information processing applications but there is no a set of well defined criteria for choosing a neural network. The user mostly treats a neural network as a black box and cannot explain how learning from input data was done nor how performance can be consistently ensured. We have experimented with several information visualization designs aiming to open the black box to possibly uncover underlying dependencies between the input data and the output data of a neural network. In this paper, we present our designs and show that the visualizations not only help us design more efficient neural networks, but also assist us in the process of using neural networks for problem solving such as performing a classification task.
| Original language | English (US) |
|---|---|
| Title of host publication | VIS 05 |
| Subtitle of host publication | IEEE Visualization 2005, Proceedings |
| Number of pages | 1 |
| DOIs | |
| State | Published - Dec 1 2005 |
| Event | VIS 05: IEEE Visualization 2005, Proceedings - Minneapolis, MN, United States Duration: Oct 23 2005 → Oct 28 2005 |
Other
| Other | VIS 05: IEEE Visualization 2005, Proceedings |
|---|---|
| Country | United States |
| City | Minneapolis, MN |
| Period | 10/23/05 → 10/28/05 |
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Keywords
- Artificial Neural Network
- Classification
- Information Visualization
- Machine Learning
- Visualization Application
ASJC Scopus subject areas
- Software
- Computer Science(all)
- Engineering(all)
- Computer Graphics and Computer-Aided Design
Cite this
Opening the black box -Data driven visualization of neural networks. / Tzeng, Fan Yin; Ma, Kwan-Liu.
VIS 05: IEEE Visualization 2005, Proceedings. 2005. 1566031.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Opening the black box -Data driven visualization of neural networks
AU - Tzeng, Fan Yin
AU - Ma, Kwan-Liu
PY - 2005/12/1
Y1 - 2005/12/1
N2 - Artificial neural networks are computer software or hardware models inspired by the structure and behavior of neurons in the human nervous system. As a powerful learning tool, increasingly neural networks have been adopted by many large-scale information processing applications but there is no a set of well defined criteria for choosing a neural network. The user mostly treats a neural network as a black box and cannot explain how learning from input data was done nor how performance can be consistently ensured. We have experimented with several information visualization designs aiming to open the black box to possibly uncover underlying dependencies between the input data and the output data of a neural network. In this paper, we present our designs and show that the visualizations not only help us design more efficient neural networks, but also assist us in the process of using neural networks for problem solving such as performing a classification task.
AB - Artificial neural networks are computer software or hardware models inspired by the structure and behavior of neurons in the human nervous system. As a powerful learning tool, increasingly neural networks have been adopted by many large-scale information processing applications but there is no a set of well defined criteria for choosing a neural network. The user mostly treats a neural network as a black box and cannot explain how learning from input data was done nor how performance can be consistently ensured. We have experimented with several information visualization designs aiming to open the black box to possibly uncover underlying dependencies between the input data and the output data of a neural network. In this paper, we present our designs and show that the visualizations not only help us design more efficient neural networks, but also assist us in the process of using neural networks for problem solving such as performing a classification task.
KW - Artificial Neural Network
KW - Classification
KW - Information Visualization
KW - Machine Learning
KW - Visualization Application
UR - http://www.scopus.com/inward/record.url?scp=33749429989&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33749429989&partnerID=8YFLogxK
U2 - 10.1109/VIS.2005.73
DO - 10.1109/VIS.2005.73
M3 - Conference contribution
SN - 0780394623
SN - 9780780394629
BT - VIS 05
ER -