图 书 馆开课单位--卡内基梅隆大学
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111Some Challenging Machine Learning Problems in Computational Biology: Time-Varying Networks Inference and Sparse Structured Input-Out Learning[计算生物学中的一些挑战性机器学习问题：时变网络推理和稀疏结构化输入法学习]
Eric P. Xing(卡内基梅隆大学) Recent advances in high-throughput technologies such as microarrays and genome-wide sequencing have led to an avalanche of new biological data that ar...
热度：46
Eric P. Xing(卡内基梅隆大学) Recent advances in high-throughput technologies such as microarrays and genome-wide sequencing have led to an avalanche of new biological data that ar...
热度：46
112Object Recognition and Segmentation by Association[对象识别和分割协会]
Tomasz Malisiewicz(卡内基梅隆大学) Many object recognition systems train a different classifier for each object category and use the sliding window approach to classify image regions. I...
热度：27
Tomasz Malisiewicz(卡内基梅隆大学) Many object recognition systems train a different classifier for each object category and use the sliding window approach to classify image regions. I...
热度：27
113Local Minima Free Parameterized Appearance Models [局部最小自由参数化外观模型]
Minh Hoai Nguyen(卡内基梅隆大学) Parameterized Appearance Models (PAMs) (e.g. Eigen-tracking, Active Appearance Models, Morphable Models) are commonly used to model the appearance and...
热度：53
Minh Hoai Nguyen(卡内基梅隆大学) Parameterized Appearance Models (PAMs) (e.g. Eigen-tracking, Active Appearance Models, Morphable Models) are commonly used to model the appearance and...
热度：53
114115Dynamic Non-Parametric Mixture Models and The Recurrent Chinese Restaurant Process[动态非参数混合模型与经常性中餐回流过程]
Eric P. Xing(卡内基梅隆大学) Dirichlet process mixture models provide a °exible Bayesian framework for estimating a distribution as an in¯nite mixture of simpler distribu...
热度：29
Eric P. Xing(卡内基梅隆大学) Dirichlet process mixture models provide a °exible Bayesian framework for estimating a distribution as an in¯nite mixture of simpler distribu...
热度：29
116On the Quantitative Analysis of Deep Belief Networks[论深层信念网络的定量分析]
Ruslan Salakhutdinov(卡内基梅隆大学) Deep Belief Networks (DBN's) are generative models that contain many layers of hidden variables. Efficient greedy algorithms for learning and appr...
热度：58
Ruslan Salakhutdinov(卡内基梅隆大学) Deep Belief Networks (DBN's) are generative models that contain many layers of hidden variables. Efficient greedy algorithms for learning and appr...
热度：58
117Bayesian Probabilistic Matrix Factorization using Markov Chain Monte Carlo[马尔可夫链蒙特卡罗的贝叶斯概率矩阵分解]
Ruslan Salakhutdinov(卡内基梅隆大学) Low-rank matrix approximation methods provide one of the simplest and most effective approaches to collaborative filtering. Such models are usually fi...
热度：97
Ruslan Salakhutdinov(卡内基梅隆大学) Low-rank matrix approximation methods provide one of the simplest and most effective approaches to collaborative filtering. Such models are usually fi...
热度：97
118Recovering Temporally Rewiring Networks: A model-based approach[恢复暂时重新连线的网络：基于模型的方法]
Fan Guo(卡内基梅隆大学) A plausible representation of relational information among entities in dynamic systems such as a living cell or a social community is a stochastic net...
热度：39
Fan Guo(卡内基梅隆大学) A plausible representation of relational information among entities in dynamic systems such as a living cell or a social community is a stochastic net...
热度：39
119Space-indexed Dynamic Programming: Learning to Follow Trajectories[空间索引动态规划：学习跟踪轨迹]
J. Zico Kolter(卡内基梅隆大学) We consider the task of learning to accurately follow a trajectory in a vehicle such as a car or helicopter. A number of dynamic programming algorithm...
热度：62
J. Zico Kolter(卡内基梅隆大学) We consider the task of learning to accurately follow a trajectory in a vehicle such as a car or helicopter. A number of dynamic programming algorithm...
热度：62
120EDM and the 4th Paradigm of Scientific Discovery - Reflections on the 2010 KDD Cup Competition[EDM与科学发现的第四范式 - 对2010年KDD杯比赛的思考]
John Stamper(卡内基梅隆大学) Technology advances have made the ability to collect large amounts of data easier than ever before. These massive datasets provide both opportunities ...
热度：60
John Stamper(卡内基梅隆大学) Technology advances have made the ability to collect large amounts of data easier than ever before. These massive datasets provide both opportunities ...
热度：60