{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,29]],"date-time":"2024-10-29T13:07:19Z","timestamp":1730207239772,"version":"3.28.0"},"reference-count":23,"publisher":"IEEE","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,8]]},"DOI":"10.1109\/cig.2018.8490455","type":"proceedings-article","created":{"date-parts":[[2018,10,16]],"date-time":"2018-10-16T03:37:41Z","timestamp":1539661061000},"page":"1-8","source":"Crossref","is-referenced-by-count":3,"title":["Building Evaluation Functions for Chess and Shogi with Uniformity Regularization Networks"],"prefix":"10.1109","author":[{"given":"Shanchuan","family":"Wan","sequence":"first","affiliation":[]},{"given":"Tomoyuki","family":"Kaneko","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1613\/jair.4217"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-44781-0_11"},{"key":"ref12","doi-asserted-by":"crossref","first-page":"484","DOI":"10.1038\/nature16961","article-title":"Mastering the game of go with deep neural networks and tree search","volume":"529","author":"silver","year":"2016","journal-title":"Nature"},{"article-title":"One model to learn them all","year":"2017","author":"kaiser","key":"ref13"},{"key":"ref14","first-page":"94","article-title":"Facial landmark detection by deep multi-task learning","author":"zhang","year":"2014","journal-title":"European Conference on Computer Vision"},{"key":"ref15","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from over-fitting","volume":"15","author":"srivastava","year":"2014","journal-title":"The Journal of Machine Learning Research"},{"article-title":"Style transfer generative adversarial networks: Learning to play chess differently","year":"2017","author":"chidambaram","key":"ref16"},{"journal-title":"Deep learning for chess","year":"2014","author":"bernhardsson","key":"ref17"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TAAI.2017.17"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.304"},{"key":"ref4","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1038\/nature24270","article-title":"Mastering the game of go without human knowledge","volume":"550","author":"silver","year":"2017","journal-title":"Nature"},{"key":"ref3","first-page":"950","article-title":"A simple weight decay can improve generalization","author":"krogh","year":"1992","journal-title":"Advances in neural information processing systems"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1023\/A:1007634325138"},{"key":"ref5","first-page":"126","article-title":"From simple features to sophisticated evaluation functions","author":"buro","year":"1998","journal-title":"Int Conf Comput Games"},{"key":"ref8","first-page":"99","article-title":"Connectionist learning of expert preferences by comparison training","author":"tesauro","year":"1989","journal-title":"Advances in neural information processing systems"},{"article-title":"Giraffe: Using deep reinforcement learning to play chess","year":"2015","author":"lai","key":"ref7"},{"article-title":"Mastering chess and shogi by self-play with a general reinforcement learning algorithm","year":"2017","author":"silver","key":"ref2"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/S0004-3702(01)00129-1"},{"key":"ref9","first-page":"117","article-title":"Comparison training of chess evaluation functions","author":"tesauro","year":"2001","journal-title":"Machines that Learn to Play Games"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"journal-title":"Giraffe An experimental chess engine based on deep learning","year":"2016","author":"matthew","key":"ref22"},{"key":"ref21","first-page":"315","article-title":"Deep sparse rectifier neural networks","author":"glorot","year":"2011","journal-title":"Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics"},{"journal-title":"Stockfish Strong open source chess engine","year":"2018","author":"romstad","key":"ref23"}],"event":{"name":"2018 IEEE Conference on Computational Intelligence and Games (CIG)","start":{"date-parts":[[2018,8,14]]},"location":"Maastricht","end":{"date-parts":[[2018,8,17]]}},"container-title":["2018 IEEE Conference on Computational Intelligence and Games (CIG)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/8473398\/8490359\/08490455.pdf?arnumber=8490455","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,26]],"date-time":"2022-01-26T19:23:32Z","timestamp":1643225012000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8490455\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,8]]},"references-count":23,"URL":"https:\/\/doi.org\/10.1109\/cig.2018.8490455","relation":{},"subject":[],"published":{"date-parts":[[2018,8]]}}}