Open AccessBook
Pattern Recognition and Machine Learning
TLDR
Probability Distributions, linear models for Regression, Linear Models for Classification, Neural Networks, Graphical Models, Mixture Models and EM, Sampling Methods, Continuous Latent Variables, Sequential Data are studied.Abstract:
Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.read more
Citations
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Proceedings ArticleDOI
Fully convolutional networks for semantic segmentation
TL;DR: The key insight is to build “fully convolutional” networks that take input of arbitrary size and produce correspondingly-sized output with efficient inference and learning.
Journal ArticleDOI
Deep learning in neural networks
TL;DR: This historical survey compactly summarizes relevant work, much of it from the previous millennium, review deep supervised learning, unsupervised learning, reinforcement learning & evolutionary computation, and indirect search for short programs encoding deep and large networks.
Journal ArticleDOI
A framework for variation discovery and genotyping using next-generation DNA sequencing data
Mark A. DePristo,Eric Banks,Ryan Poplin,Kiran V. Garimella,Jared Maguire,Christopher Hartl,Anthony A. Philippakis,Anthony A. Philippakis,Anthony A. Philippakis,Guillermo del Angel,Manuel A. Rivas,Manuel A. Rivas,Matt Hanna,Aaron McKenna,Timothy Fennell,Andrew Kernytsky,Andrey Sivachenko,Kristian Cibulskis,Stacey Gabriel,David Altshuler,David Altshuler,Mark J. Daly,Mark J. Daly +22 more
TL;DR: A unified analytic framework to discover and genotype variation among multiple samples simultaneously that achieves sensitive and specific results across five sequencing technologies and three distinct, canonical experimental designs is presented.
Posted Content
Fully Convolutional Networks for Semantic Segmentation
TL;DR: It is shown that convolutional networks by themselves, trained end- to-end, pixels-to-pixels, improve on the previous best result in semantic segmentation.
Journal ArticleDOI
Data clustering: 50 years beyond K-means
TL;DR: A brief overview of clustering is provided, well known clustering methods are summarized, the major challenges and key issues in designing clustering algorithms are discussed, and some of the emerging and useful research directions are pointed out.
References
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Book
Pattern recognition and neural networks
Brian D. Ripley,N. L. Hjort +1 more
TL;DR: Professor Ripley brings together two crucial ideas in pattern recognition; statistical methods and machine learning via neural networks in this self-contained account.