当前位置: X-MOL 学术Proc. Natl. Acad. Sci. U.S.A. › 论文详情
Another step toward demystifying deep neural networks [Commentaries]
Proceedings of the National Academy of Sciences of the United States of America ( IF 9.412 ) Pub Date : 2020-10-15 , DOI: 10.1073/pnas.2018957117
Michael Elad, Dror Simon, Aviad Aberdam

The field of deep learning has positioned itself in the past decade as a prominent and extremely fruitful engineering discipline. This comeback of neural networks in the early 2000s swept the machine learning community, and soon after found itself immersed in practically every scientific, social, and technological front. A growing series of contributions established this field as leading to state-of-the-art results in nearly every task, recognizing image content, understanding written documents, exposing obscure connections in massive datasets, facilitating efficient search in large repositories, translating languages, enabling a revolution in transportation, revealing new scientific laws in physics and chemistry, and so much more. Deep neural networks not only solve known problems but offer, in addition, unprecedented results in deploying learning to problems that until recently were considered as hopeless or only weakly successful. These include automatically synthesizing text–media, creating musical art pieces, synthesizing realistic images and video, enabling competitive game-playing, and this list goes on and on.
更新日期:2020-10-17

 

全部期刊列表>>
spring&清华大学出版社
城市可持续发展前沿研究专辑
Springer 纳米技术权威期刊征稿
全球视野覆盖
施普林格·自然新
chemistry
3分钟学术视频演讲大赛
物理学研究前沿热点精选期刊推荐
自然职位线上招聘会
欢迎报名注册2020量子在线大会
化学领域亟待解决的问题
材料学研究精选新
GIANT
ACS ES&T Engineering
ACS ES&T Water
屿渡论文,编辑服务
ACS Publications填问卷
阿拉丁试剂right
麻省大学
西北大学
湖南大学
华东师范大学
陆海华
化学所
隐藏1h前已浏览文章
课题组网站
新版X-MOL期刊搜索和高级搜索功能介绍
ACS材料视界
天合科研
x-mol收录
胡眆昊
杨财广
廖矿标
试剂库存
down
wechat
bug