Sharad Vikram

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Recent technologies in vision sensors are capable of capturing 3D finger positions and movements. We propose a novel way to control and interact with computers by moving fingers in the air. The positions of fingers are precisely captured by a computer vision device. By tracking the moving patterns of fingers, we can then recognize users' intended control(More)
We present a character-level recurrent neural network that generates relevant and coherent text given auxiliary information such as a sentiment or topic. 1 Using a simple input replication strategy, we preserve the signal of auxiliary input across wider sequence intervals than can feasibly be trained by back-propagation through time. Our main results center(More)
The advent of cost-effective DNA sequencing has provided clinics with high-resolution information about patient's genetic variants, which has resulted in the need for efficient interpretation of this genomic data. Traditionally, variant interpretation has been dominated by many manual, time-consuming processes due to the disparate forms of relevant(More)
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