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Found 7 papers, 6 papers with code
Online learning for robust voltage control under uncertain grid topology
In this work, we combine a nested convex body chasing algorithm with a robust predictive controller to achieve provably finite-time convergence to safe voltage limits in the online setting where there is uncertainty in both the network topology as well as load and generation variations.
Robust Online Voltage Control with an Unknown Grid Topology
Voltage control generally requires accurate information about the grid's topology in order to guarantee network stability.
SustainBench: Benchmarks for Monitoring the Sustainable Development Goals with Machine Learning
Our goals for SustainBench are to (1) lower the barriers to entry for the machine learning community to contribute to measuring and achieving the SDGs; (2) provide standard benchmarks for evaluating machine learning models on tasks across a variety of SDGs; and (3) encourage the development of novel machine learning methods where improved model performance facilitates progress towards the SDGs.
A Framework for Sample Efficient Interval Estimation with Control Variates
We consider the problem of estimating confidence intervals for the mean of a random variable, where the goal is to produce the smallest possible interval for a given number of samples.
Efficient Object Detection in Large Images using Deep Reinforcement Learning
Traditionally, an object detector is applied to every part of the scene of interest, and its accuracy and computational cost increases with higher resolution images.
Selection via Proxy: Efficient Data Selection for Deep Learning
By removing hidden layers from the target model, using smaller architectures, and training for fewer epochs, we create proxies that are an order of magnitude faster to train.
Poverty Prediction with Public Landsat 7 Satellite Imagery and Machine Learning
Obtaining detailed and reliable data about local economic livelihoods in developing countries is expensive, and data are consequently scarce.
