Learning Graphical Models
A graphical model or probabilistic graphical model (PGM) or structured probabilistic model is a probabilistic model for which a graph expresses the conditional dependence structure between random variables. They are commonly used in probability theory, statistics—particularly Bayesian statistics—and machine learning. (Wikipedia)
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A Convolutional Auto-Encoder for Haplotype Assembly and Viral Quasispecies Reconstruction
Neural Information Processing Systems
Haplotype assembly and viral quasispecies reconstruction are challenging tasks concerned with analysis of genomic mixtures using sequencing data.
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Semi-Supervised Sparse Gaussian Classification: Provable Benefits of Unlabeled Data
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The premise of semi-supervised learning (SSL) is that combining labeled and unlabeled data yields significantly more accurate models.
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67496dfa96afddab795530cc7c69b57a-Supplemental-Conference.pdf
Neural Information Processing Systems
Theoptimalbaseline, however, israrelyusedinpractice (Sutton & Barto (2018); foran exception, see (Peters & Schaal, 2008)). Equation (1) thentakesthefollowingform: r E R(x)= E (R(x) B)r log (x).
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