Then a risk score assessment model was employed, using the determined dwelling type along with an inundation model of the regions.
This last task, the recognition of banknotes from different denominations, has been addressed by the use of computer vision models for image recognition.
1 code implementation • 31 Jan 2022 • Anthony Ortiz, Dhaval Negandhi, Sagar R Mysorekar, Joseph Kiesecker, Shivaprakash K Nagaraju, Caleb Robinson, Priyal Bhatia, Aditi Khurana, Jane Wang, Felipe Oviedo, Juan Lavista Ferres
Using this dataset, we measure the solar footprint across India and quantified the degree of landcover modification associated with the development of PV infrastructure.
While the uptake of data-driven approaches for materials science and chemistry is at an exciting, early stage, to realise the true potential of machine learning models for successful scientific discovery, they must have qualities beyond purely predictive power.
Conclusion: In the case of DR, most of the disease biomarkers are related topologically to the vasculature.
Diferentially private (DP) synthetic datasets are a powerful approach for training machine learning models while respecting the privacy of individual data providers.
Child trafficking in a serious problem around the world.
no code implementations • 4 May 2021 • Anusua Trivedi, Mohit Jain, Nikhil Kumar Gupta, Markus Hinsche, Prashant Singh, Markus Matiaschek, Tristan Behrens, Mirco Militeri, Cameron Birge, Shivangi Kaushik, Archisman Mohapatra, Rita Chatterjee, Rahul Dodhia, Juan Lavista Ferres
Malnutrition is a global health crisis and is the leading cause of death among children under five.
We show that by using the FELICIA mechanism, a data owner with limited image samples can generate high-quality synthetic images with high utility while neither data owners has to provide access to its data.
Glacier mapping is key to ecological monitoring in the hkh region.
In this work, we propose the first formal framework for membership privacy estimation in generative models.
It has been shown that such synthetic data can be used for a variety of downstream tasks such as training classifiers that would otherwise require the original dataset to be shared.
As a result, we can often end up using data that is not representative of the problem we are trying to solve.
Moreover, since these transformations are usually unknown, we employ the learning with experts setting to develop a fully online method (NonSTOP-NonSTationary Online Prediction) for predicting nonstationary time series.