图 书 馆开课单位--普林斯顿大学
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21Theory and Applications of Boosting[升压原理及其应用]
Robert Schapire(普林斯顿大学) Boosting is a general method for producing a very accurate classification rule by combining rough and moderately inaccurate "rules of thumb"...
热度：28
Robert Schapire(普林斯顿大学) Boosting is a general method for producing a very accurate classification rule by combining rough and moderately inaccurate "rules of thumb"...
热度：28
2223Sliding Shapes for 3D Object Detection in Depth Images[用于深度图像中3D对象检测的滑动形状]
Song Shuran(普林斯顿大学) The depth information of RGB-D sensors has greatly simplified some common challenges in computer vision and enabled breakthroughs for several tasks. I...
热度：37
Song Shuran(普林斯顿大学) The depth information of RGB-D sensors has greatly simplified some common challenges in computer vision and enabled breakthroughs for several tasks. I...
热度：37
24The Neuroscience of Reinforcement Learning[强化学习的神经科学]
Yael Niv(普林斯顿大学) Overview and goals: One of the most influential contributions of machine learning to understanding the human brain is the (fairly recent) formulation ...
热度：135
Yael Niv(普林斯顿大学) Overview and goals: One of the most influential contributions of machine learning to understanding the human brain is the (fairly recent) formulation ...
热度：135
25Have We All Been Right? Looking Backwards at Linguistic Theory, Statistics, and Language Acquisition[我们都是正确的吗？回顾语言学、统计学和说话方式]
Jean-Roger Vergnaud, Norbert Hornstein, William Gregory Sakas, Anna Maria Di Sciullo, Charles Yang, Robert Freidin(普林斯顿大学) It was uncertain by the end of this panel if linguists and computational scientists could find meaningful common ground. As conference organizer Micha...
热度：65
Jean-Roger Vergnaud, Norbert Hornstein, William Gregory Sakas, Anna Maria Di Sciullo, Charles Yang, Robert Freidin(普林斯顿大学) It was uncertain by the end of this panel if linguists and computational scientists could find meaningful common ground. As conference organizer Micha...
热度：65
26Global Poverty: How Demanding Are Our Obligations?[全球贫困：我们的义务有多高？]
Peter Singer(普林斯顿大学) Peter Singer walks listeners through one of his most provocative philosophical arguments -- that affluent individuals must acknowledge their moral obl...
热度：48
Peter Singer(普林斯顿大学) Peter Singer walks listeners through one of his most provocative philosophical arguments -- that affluent individuals must acknowledge their moral obl...
热度：48
27The Art of Structural Design: A Swiss Legacy[结构设计的艺术：瑞士的传统]
David P. Billington(普林斯顿大学) Bridges serve a utilitarian purpose, but they should also please the eye. David P. Billington celebrates an influential group of Swiss structural engi...
热度：73
David P. Billington(普林斯顿大学) Bridges serve a utilitarian purpose, but they should also please the eye. David P. Billington celebrates an influential group of Swiss structural engi...
热度：73
28Gradient Weights help Nonparametric Regressors[梯度权重帮助非参数回归系数]
Samory Kpotufe(普林斯顿大学) In regression problems over real d, the unknown function f often varies more in some coordinates than in others. We show that weighting each coordinat...
热度：36
Samory Kpotufe(普林斯顿大学) In regression problems over real d, the unknown function f often varies more in some coordinates than in others. We show that weighting each coordinat...
热度：36
29Kernel Hyperalignment[内核超对齐]
Ramadge Peter J(普林斯顿大学) We offer a regularized, kernel extension of the multi-set, orthogonal Procrustes problem, or hyperalignment. Our new method, called Kernel Hyperalignm...
热度：41
Ramadge Peter J(普林斯顿大学) We offer a regularized, kernel extension of the multi-set, orthogonal Procrustes problem, or hyperalignment. Our new method, called Kernel Hyperalignm...
热度：41
30Is Intractability a Barrier for Machine Learning?[机器学习是棘手的障碍？]
Sanjeev Arora(普林斯顿大学) One of the frustrations of machine learning theory is that many of the underlying algorithmic problems are provably intractable (e.g., NP-hard or wors...
热度：63
Sanjeev Arora(普林斯顿大学) One of the frustrations of machine learning theory is that many of the underlying algorithmic problems are provably intractable (e.g., NP-hard or wors...
热度：63