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Precise localization within the GI tract by combining classification of CNNs and time-series analysis of HMMs
This paper presents a method to efficiently classify the gastroenterologic section of images derived from Video Capsule Endoscopy (VCE) studies by exploring the combination of a Convolutional Neural Network (CNN) for classification with the time-series analysis properties of a Hidden Markov Model (HMM).
HW-Aware Initialization of DNN Auto-Tuning to Improve Exploration Time and Robustness
From these results, a validity-driven initialization method for AutoTVM is developed, only requiring 41. 6% of the necessary hardware measurements to find the best solution, while improving search robustness.
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