Search Results for author:
Found 6 papers, 6 papers with code
Estimating Divergences in High Dimensions
To this end, we show empirically that estimating the Kullback-Leibler divergence using decomposable models from a maximum likelihood estimator outperforms existing methods for divergence estimation in situations where dimensionality is high and useful decomposable models can be learnt from the available data.
A Bayesian-inspired, deep learning-based, semi-supervised domain adaptation technique for land cover mapping
In this paper we present Sourcerer, a Bayesian-inspired, deep learning-based, semi-supervised DA technique for producing land cover maps from SITS data.
ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernels
Most methods for time series classification that attain state-of-the-art accuracy have high computational complexity, requiring significant training time even for smaller datasets, and are intractable for larger datasets.
InceptionTime: Finding AlexNet for Time Series Classification
TSC is the area of machine learning tasked with the categorization (or labelling) of time series.
Automatic alignment of surgical videos using kinematic data
Over the past one hundred years, the classic teaching methodology of "see one, do one, teach one" has governed the surgical education systems worldwide.
Understanding Concept Drift
Concept drift is a major issue that greatly affects the accuracy and reliability of many real-world applications of machine learning.
