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...
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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...
As a present to Mizar on its 50th anniversary, we develop an AI/TP system that automatically proves about 60% of the Mizar theorems in the hammer setting. We also automatically ...
Recent methods for learning vector space representations of words have succeeded in capturing fine-grained semantic and syntactic regularities using vector arithmetic, but the o...
This paper focuses on scoping studies, an approach to reviewing the literature which to date has received little attention in the research methods literature. We distinguish bet...
BACKGROUND: Since December 2019, when coronavirus disease 2019 (Covid-19) emerged in Wuhan city and rapidly spread throughout China, data have been needed on the clinical charac...
Object detection performance, as measured on the canonical PASCAL VOC dataset, has plateaued in the last few years. The best-performing methods are complex ensemble systems that...
Convolutional networks are at the core of most state of-the-art computer vision solutions for a wide variety of tasks. Since 2014 very deep convolutional networks started to bec...
This paper presents a new vision Transformer, called Swin Transformer, that capably serves as a general-purpose backbone for computer vision. Challenges in adapting Transformer ...
Leon Festinger's theory of cognitive dissonance has been widely recognized for its important and influential concepts in areas of motivation and social psychology. The theory of...