A unified Markov random field/marked point process image model and its application to computational materials
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: 2014DOI: 10.1109/ICRA.2014.6907425
Keyword(s): biology computing,n gradient methods,n learning (artificial intelligence),n neural nets,n biologically plausible actor-critic algorithm,n connectionist actor-critic algorithm,n dopaminergic signaling patterns,n intrinsic reward system,n model-free reinforcement learning,n neural actor-critic,n polecart problem,n policy gradients,n Backpropagation,n Biological system modeling,n Learning (artificial intelligence),n Neurons,n Supervised learning
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A unified Markov random field/marked point process image model and its application to computational materials
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| contributor author | Zhao, Huixi | |
| contributor author | Comer, Mary L. | |
| contributor author | De Graef, Marc | |
| date accessioned | 2020-03-12T22:51:11Z | |
| date available | 2020-03-12T22:51:11Z | |
| date issued | 2014 | |
| identifier other | 7026231.pdf | |
| identifier uri | http://libsearch.um.ac.ir:80/fum/handle/fum/1096819 | |
| format | general | |
| language | English | |
| publisher | IEEE | |
| title | A unified Markov random field/marked point process image model and its application to computational materials | |
| type | Conference Paper | |
| contenttype | Metadata Only | |
| identifier padid | 8237438 | |
| subject keywords | biology computing | |
| subject keywords | n gradient methods | |
| subject keywords | n learning (artificial intelligence) | |
| subject keywords | n neural nets | |
| subject keywords | n biologically plausible actor-critic algorithm | |
| subject keywords | n connectionist actor-critic algorithm | |
| subject keywords | n dopaminergic signaling patterns | |
| subject keywords | n intrinsic reward system | |
| subject keywords | n model-free reinforcement learning | |
| subject keywords | n neural actor-critic | |
| subject keywords | n polecart problem | |
| subject keywords | n policy gradients | |
| subject keywords | n Backpropagation | |
| subject keywords | n Biological system modeling | |
| subject keywords | n Learning (artificial intelligence) | |
| subject keywords | n Neurons | |
| subject keywords | n Supervised learning | |
| identifier doi | 10.1109/ICRA.2014.6907425 | |
| journal title | mage Processing (ICIP), 2014 IEEE International Conference on | |
| filesize | 164198 | |
| citations | 0 |