Diet Networks: Thin Parameters for Fat Genomics

Learning a synaptic learning rule

Y. Bengio, S. Bengio, and J. Cloutier · 1991

Earlier work this paper cites.

Learning to control fast-weight memories: An alternative to dynamic recurrent networks

Jürgen Schmidhuber · 1992

Earlier work this paper cites.

Random projection in dimensionality reduction: Applications to image and text data

Ella Bingham and Heikki Mannila · 2001

Earlier work this paper cites.

Evolving modular fast-weight networks for control

Faustino Gomez and Juergen Schmidhuber · 2005

Earlier work this paper cites.

Principal components analysis corrects for stratification in genome-wide association studies

Alkes L Price, Nick J Patterson, Robert M Plenge, Michael E Weinblatt, Nancy A Shadick, and D Reich · 2006

Earlier work this paper cites.

Extracting and composing robust features with denoising autoencoders

Pascal Vincent, Hugo Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol · 2008

Earlier work this paper cites.

A hypercube-based encoding for evolving large-scale neural networks

Kenneth O. Stanley, David B. D’Ambrosio, and Jason Gauci · 2009

Earlier work this paper cites.

Genetic ancestry inference using support vector machines, and the active emergence of a unique american population

Ryan J Haasl, Catherine A McCarty, and Bret A Payseur · 2012

Earlier work this paper cites.

Inferring genome-wide patterns of admixture in qataris using fifty-five ancestral populations

Larsson Omberg, Jacqueline Salit, Neil Hackett, Jennifer Fuller, Rebecca Matthew, Lotfi Chouchane, Juan L Rodriguez-Flores, Carlos Bustamante, Ronald G Crystal, and Jason G Mezey · 2012

Earlier work this paper cites.

Lecture 6.5—RmsProp: Divide the gradient by a running average of its recent magnitude

T. Tieleman and G. Hinton · 2012

Cited alongside, same era.

Predicting parameters in deep learning

Original

Misha Denil, Babak Shakibi, Laurent Dinh, Marc’Aurelio Ranzato, and Nando de Freitas · 2013

Cited alongside, same era.

Distributed representations of words and phrases and their compositionality

T. Mikolov, I. Sutskever, K. Chen, G.S. Corrado, and J. Dean · 2013

Cited alongside, same era.

Predicting the sequence specificities of dna-and rna-binding proteins by deep learning

Babak Alipanahi, Andrew Delong, Matthew T Weirauch, and Brendan J Frey · 2015

Cited alongside, same era.

A global reference for human genetic variation

The 1000 Genomes Project Consortium · 2015

Cited alongside, same era.

Learning feed-forward one-shot learners

Original

Luca Bertinetto, João F. Henriques, Jack Valmadre, Philip H. S. Torr, and Andrea Vedaldi · 2016

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Dynamic filter networks

Original

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Gene expression inference with deep learning

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Hypernetworks

Original

David Ha, Andrew Dai, and Quoc V. Le · 2016

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Basset: Learning the regulatory code of the accessible genome with deep convolutional neural networks

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