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Probabilistic Graphical Models - Principles and Techniques
TLDR
The framework of probabilistic graphical models, presented in this book, provides a general approach for causal reasoning and decision making under uncertainty, allowing interpretable models to be constructed and then manipulated by reasoning algorithms. Expand
FastSLAM: a factored solution to the simultaneous localization and mapping problem
TLDR
This paper presents FastSLAM, an algorithm that recursively estimates the full posterior distribution over robot pose and landmark locations, yet scales logarithmically with the number of landmarks in the map. Expand
Support Vector Machine Active Learning with Applications to Text Classification
TLDR
Experimental results showing that employing the active learning method can significantly reduce the need for labeled training instances in both the standard inductive and transductive settings are presented. Expand
Max-Margin Markov Networks
TLDR
Maximum margin Markov (M3) networks incorporate both kernels, which efficiently deal with high-dimensional features, and the ability to capture correlations in structured data, and a new theoretical bound for generalization in structured domains is provided. Expand
The Genotype-Tissue Expression (GTEx) project
TLDR
The Genotype-Tissue Expression (GTEx) project is described, which will establish a resource database and associated tissue bank for the scientific community to study the relationship between genetic variation and gene expression in human tissues. Expand
The Genotype-Tissue Expression (GTEx) pilot analysis: Multitissue gene regulation in humans
TLDR
The landscape of gene expression across tissues is described, thousands of tissue-specific and shared regulatory expression quantitative trait loci (eQTL) variants are cataloged, complex network relationships are described, and signals from genome-wide association studies explained by eQTLs are identified. Expand
Self-Paced Learning for Latent Variable Models
TLDR
A novel, iterative self-paced learning algorithm where each iteration simultaneously selects easy samples and learns a new parameter vector that outperforms the state of the art method for learning a latent structural SVM on four applications. Expand
SCAPE: shape completion and animation of people
TLDR
The SCAPE method is capable of constructing a high-quality animated surface model of a moving person, with realistic muscle deformation, using just a single static scan and a marker motion capture sequence of the person. Expand
Decomposing a scene into geometric and semantically consistent regions
TLDR
A region-based model which combines appearance and scene geometry to automatically decompose a scene into semantically meaningful regions and which achieves state-of-the-art performance on the tasks of both multi-class image segmentation and geometric reasoning. Expand
SCAPE: shape completion and animation of people
TLDR
The SCAPE method is capable of constructing a high-quality animated surface model of a moving person, with realistic muscle deformation, using just a single static scan and a marker motion capture sequence of the person. Expand
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