Deep Residual Learning for Image Recognition
Actualmente diversas investigaciones se han enfocado en analizar a partir de videos de alta velocidad, características de las descargas eléctricas atmosféricas con el fin de adq...
Publications
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2,247 publications
Development of the Colle-Salvetti correlation-energy formula into a functional of the electron density
A correlation-energy formula due to Colle and Salvetti [Theor. Chim. Acta 37, 329 (1975)], in which the correlation energy density is expressed in terms of the electron density ...
DNA sequencing with chain-terminating inhibitors
A new method for determining nucleotide sequences in DNA is described. It is similar to the “plus and minus” method [Sanger, F. & Coulson, A. R. (1975) J. Mol. Biol. 94, 441...
The Sequence Alignment/Map format and SAMtools
Abstract Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads ...
The CES-D Scale
The CES-D scale is a short self-report scale designed to measure depressive symptomatology in the general population. The items of the scale are symptoms associated with depress...
Densely Connected Convolutional Networks
Recent work has shown that convolutional networks can be substantially deeper, more accurate, and efficient to train if they contain shorter connections between layers close to ...
Fully convolutional networks for semantic segmentation
Convolutional networks are powerful visual models that yield hierarchies of features. We show that convolutional networks by themselves, trained end-to-end, pixels-to-pixels, ex...
A Global Measure of Perceived Stress
This paper presents evidence from three samples, two of college students and one of participants in a community smoking-cessation program, for the reliability and validity of a ...
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Training Deep Neural Networks is complicated by the fact that the distribution of each layer's inputs changes during training, as the parameters of the previous layers change. T...
MobileNetV2: Inverted Residuals and Linear Bottlenecks
In this paper we describe a new mobile architecture, MobileNetV2, that improves the state of the art performance of mobile models on multiple tasks and benchmarks as well as acr...