图 书 馆开课单位--卡内基梅隆大学
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101Adaptive Representations for Efficient Inference for Distributions on Permutations[排列分布有效推理的自适应表示]
Carlos Guestrin(卡内基梅隆大学) Permutations are ubiquitous in many real world problems, such as voting, rankings and data association. Representing uncertainty over permutations is ...
热度：35
Carlos Guestrin(卡内基梅隆大学) Permutations are ubiquitous in many real world problems, such as voting, rankings and data association. Representing uncertainty over permutations is ...
热度：35
102A theory of similarity functions for learning and clustering[学习和聚类的相似函数理论]
Avrim Blum(卡内基梅隆大学) Kernel methods have proven to be very powerful tools in machine learning. In addition, there is a well-developed theory of sufficient conditions for a...
热度：47
Avrim Blum(卡内基梅隆大学) Kernel methods have proven to be very powerful tools in machine learning. In addition, there is a well-developed theory of sufficient conditions for a...
热度：47
103Undercover: Authentication Usable in Front of Prying Eyes[卧底：在窥探前可用的认证]
Eiji Hayashi(卡内基梅隆大学) We propose the first authentication scheme to rely on the humanability to combine different sensory inputs. We demonstrate thesystem is usable and res...
热度：26
Eiji Hayashi(卡内基梅隆大学) We propose the first authentication scheme to rely on the humanability to combine different sensory inputs. We demonstrate thesystem is usable and res...
热度：26
104105A Unified View of Matrix Factorization Models[矩阵分解模型的统一视图]
Ajit Singh;Geoffrey J. Gordon(卡内基梅隆大学) We present a unified view of matrix factorization that frames the differences among popular methods, such as NMF, Weighted SVD, E-PCA, MMMF, pLSI, pLS...
热度：33
Ajit Singh;Geoffrey J. Gordon(卡内基梅隆大学) We present a unified view of matrix factorization that frames the differences among popular methods, such as NMF, Weighted SVD, E-PCA, MMMF, pLSI, pLS...
热度：33
106Designing Online Communities from Theory[用理论设计网络社区]
Bob Kraut(卡内基梅隆大学) Online communities are the fastest-growing portion of the Internet and provide members with information, social support, and entertainment. While a mi...
热度：35
Bob Kraut(卡内基梅隆大学) Online communities are the fastest-growing portion of the Internet and provide members with information, social support, and entertainment. While a mi...
热度：35
107Rare Category Detection for Spatial Data[空间数据的罕见类别检测]
Jingrui He(卡内基梅隆大学) Given an unlabeled unbalanced data set, the goal of rare category detection is to discover examples from the minority classes with a few label request...
热度：42
Jingrui He(卡内基梅隆大学) Given an unlabeled unbalanced data set, the goal of rare category detection is to discover examples from the minority classes with a few label request...
热度：42
108Relational Learning as Collective Matrix Factorization[集体矩阵分解的关系学习]
Ajit Singh(卡内基梅隆大学) We present a unified view of matrix factorization models, including singular value decompositions, non-negative matrix factorization, probabilistic la...
热度：78
Ajit Singh(卡内基梅隆大学) We present a unified view of matrix factorization models, including singular value decompositions, non-negative matrix factorization, probabilistic la...
热度：78
109Partially Observed Maximum Entropy Discrimination Markov Networks[部分可观测的最大熵辨别马尔可夫网络]
Jun Zhu(卡内基梅隆大学) Learning graphical models with hidden variables can offer semantic insights to complex data and lead to salient structured predictors without relying ...
热度：39
Jun Zhu(卡内基梅隆大学) Learning graphical models with hidden variables can offer semantic insights to complex data and lead to salient structured predictors without relying ...
热度：39
110Discovering Cyclic Causal Models by Independent Components Analysis[通过独立成分分析发现循环因果模型]
Gustavo Lacerda(卡内基梅隆大学) This talk will start by presenting Shimizu et al's (2006) ICA-based approach (LiNGAM) for discovering acyclic (DAG) linear Structural Equation Mod...
热度：50
Gustavo Lacerda(卡内基梅隆大学) This talk will start by presenting Shimizu et al's (2006) ICA-based approach (LiNGAM) for discovering acyclic (DAG) linear Structural Equation Mod...
热度：50