منابع مشابه
Exploring the Shallow End; Estimating Information Content in Transcriptomics Studies
Transcriptomics is a major platform to study organismal biology. The advent of new parallel sequencing technologies has opened up a new avenue of transcriptomics with ever deeper and deeper sequencing to identify and quantify each and every transcript in a sample. However, this may not be the best usage of the parallel sequencing technology for all transcriptomics experiments. I utilized the Sh...
متن کاملTwo End-to-end Shallow Discourse Parsers for English and Chinese in CoNLL-2016 Shared Task
This paper describes our two discourse parsers (i.e., English discourse parser and Chinese discourse parser) for submission to CoNLL-2016 shared task on Shallow Discourse Parsing. For English discourse parser, we build two separate argument extractors for single sentence (SS) case, and adopt a convolutional neural network for Non-Explicit sense classification based on (Wang and Lan, 2015b)’s wo...
متن کاملEnd-to-End Image Super-Resolution via Deep and Shallow Convolutional Networks
One impressive advantage of convolutional neural networks (CNNs) is their ability to automatically learn feature representation from raw pixels, eliminating the need for handdesigned procedures. However, recent methods for single image super-resolution (SR) fail to maintain this advantage. They utilize CNNs in two decoupled steps, i.e., first upsampling the low resolution (LR) image to the high...
متن کاملStreptokinase for Treatment of Thrombotic Disorders: The End? Or the End of the Beginning?
Thrombotic disorders, such as myocardial infarction, ischemic stroke, peripheral arterial disease, deep venous thrombosis, pulmonary embolism, or other embolic diseases that are responsible for worldwide mortality and morbidity, are manifestations of the formed thrombi by blood clots during a pathologic blood coagulation process. Once thrombi are formed, the only way to resolve the blood clot i...
متن کاملThe Shallow End: Empowering Shallower Deep-Convolutional Networks through Auxiliary Outputs
Convolutional neural networks (CNNs) with very deep architectures, such as the residual network (ResNet) [6], have shown encouraging results in various tasks in computer vision and machine learning. Their depth has been one of the key factors behind the great success of CNNs, with the gradient vanishing issue having been largely addressed by ResNet. However, there are other issues associated wi...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: The Iowa Review
سال: 2013
ISSN: 0021-065X,2330-0361
DOI: 10.17077/0021-065x.7359