图 书 馆开课单位--杜克大学
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11Brain-Machine Interfaces Based on Neuronal Ensemble Recordings[基于神经元集成记录的脑机接口]
Mikhail A. Lebedev(杜克大学) Brain-machine interfaces (BMIs) have experienced an explosive development during the last decade. Current state of the art BMIs convert neuronal ensem...
热度：47
Mikhail A. Lebedev(杜克大学) Brain-machine interfaces (BMIs) have experienced an explosive development during the last decade. Current state of the art BMIs convert neuronal ensem...
热度：47
12Estimation of gradients and coordinate covariation in classification[分类中梯度和坐标协变量的估计]
Sayan Mukherjee(杜克大学) We introduce an algorithm that simultaneously estimates a classification function as well as its gradient in the supervised learning framework. The mo...
热度：62
Sayan Mukherjee(杜克大学) We introduce an algorithm that simultaneously estimates a classification function as well as its gradient in the supervised learning framework. The mo...
热度：62
13The Kernel Beta Process[内核测试过程]
Lawrence Carin(杜克大学) For handling large-scale problems, methods like Gaussian processes can be computationally challenging. In this paper, we discuss how use of alternativ...
热度：69
Lawrence Carin(杜克大学) For handling large-scale problems, methods like Gaussian processes can be computationally challenging. In this paper, we discuss how use of alternativ...
热度：69
14Borrowing Strength, Learning Vector Valued Functions and Supervised Dimension Reduction[借用强度、学习向量值函数与监督降维]
Sayan Mukherjee(杜克大学) We study the problem of supervised dimension reduction from the perspective of learning vector valued functions and multi-task or hierarchical modelin...
热度：57
Sayan Mukherjee(杜克大学) We study the problem of supervised dimension reduction from the perspective of learning vector valued functions and multi-task or hierarchical modelin...
热度：57
15Lower Bounds for Passive and Active Learning[被动和主动学习的下界]
Maxim Raginsky(杜克大学) We develop unified information-theoretic machinery for deriving lower bounds for passive and active learning schemes. Our bounds involve the so-called...
热度：53
Maxim Raginsky(杜克大学) We develop unified information-theoretic machinery for deriving lower bounds for passive and active learning schemes. Our bounds involve the so-called...
热度：53
16Linear Complementarity for Regularized Policy Evaluation and Improvement[正规化政策评价与改进的线性互补]
Jeff Johns(杜克大学) Recent work in reinforcement learning has emphasized the power of L1 regularization to perform feature selection and prevent overfitting. We propose f...
热度：37
Jeff Johns(杜克大学) Recent work in reinforcement learning has emphasized the power of L1 regularization to perform feature selection and prevent overfitting. We propose f...
热度：37
17Non-Parametric Bayesian Dictionary Learning for Sparse Image Representations[稀疏图像表示的非参数贝叶斯字典学习]
Mingyuan Zhou(杜克大学) Non-parametric Bayesian techniques are considered for learning dictionaries for sparse image representations, with applications in denoising, inpainti...
热度：64
Mingyuan Zhou(杜克大学) Non-parametric Bayesian techniques are considered for learning dictionaries for sparse image representations, with applications in denoising, inpainti...
热度：64
18Locality-Sensitive Binary Codes from Shift-Invariant Kernels[移位不变内核中的位置敏感二进制代码]
Maxim Raginsky(杜克大学) This paper addresses the problem of designing binary codes for high-dimensional data such that vectors that are similar in the original space map to s...
热度：64
Maxim Raginsky(杜克大学) This paper addresses the problem of designing binary codes for high-dimensional data such that vectors that are similar in the original space map to s...
热度：64
19The Kernel Beta Process[内核测试过程]
David E Carlson(杜克大学) A new Lévy process prior is proposed for an uncountable collection of covariatedependent feature-learning measures; the model is called the ker...
热度：26
David E Carlson(杜克大学) A new Lévy process prior is proposed for an uncountable collection of covariatedependent feature-learning measures; the model is called the ker...
热度：26
20Deciphering transcription regulation: from individual sites to cell type specific expression[解读转录调控：从个体位点到细胞类型特异性表达]
Uwe Ohler(杜克大学) Understanding how transcription regulation is encoded in the genomes of complex multicellular organisms has been a big challenge, not least due to t...
热度：56
Uwe Ohler(杜克大学) Understanding how transcription regulation is encoded in the genomes of complex multicellular organisms has been a big challenge, not least due to t...
热度：56