Machines are increasingly doing “intelligent” things. Face recognition algorithms use a large dataset of photos labeled as having a face or not to estimate a function that predicts the pre...

https://pubs.aeaweb.org/doi/pdfplus/10.1257/jep.31.2.87

Machine Learning: An Applied Econometric Approach

Climate change is one of the greatest challenges facing humanity, and we, as machine learning experts, may wonder how we can help. Here we describe how machine learning can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a changing climate. From smart grids to disaster management, we identify high impact problems where existing gaps can be filled by machine learning, in collaboration with other fields. Our recommendations encompass exciting research questions as well as promising business opportunities. We call on the machine learning community to join the global effort against climate change.

Tackling Climate Change with Machine Learning

A machine learning approach on the relationship among solar and wind energy production, coal consumption, GDP, and CO2 emissions

Artificial intelligence in sustainable energy industry: Status Quo, challenges and opportunities

The unprecedented success of deep neural networks in many applications has made these networks a prime target for adversarial exploitation. In this paper, we introduce a benchmark technique for detecting backdoor attacks (aka Trojan attacks) on deep convolutional neural networks (CNNs). We introduce the concept of Universal Litmus Patterns (ULPs), which enable one to reveal backdoor attacks by feeding these universal patterns to the network and analyzing the output (i.e., classifying the network as `clean' or `corrupted'). This detection is fast because it requires only a few forward passes through a CNN. We demonstrate the effectiveness of ULPs for detecting backdoor attacks on thousands of networks with different architectures trained on four benchmark datasets, namely the German Traffic Sign Recognition Benchmark (GTSRB), MNIST, CIFAR10, and Tiny-ImageNet. The codes and train/test models for this paper can be found here: https://umbcvision.github.io/Universal-Litmus-Patterns/.

/pdf/universal-litmus-patterns-revealing-backdoor-attacks-in-cnns-3n1wr6jurg.pdf

Universal Litmus Patterns: Revealing Backdoor Attacks in CNNs

Machine learning in energy economics and finance: A review

Price Elasticity of Gasoline Smuggling: a Semi-Structural Estimation Approach

Foreign Exchange Shocks and Gasoline Consumption

We estimate the price elasticity of the demand for gasoline smuggling in Iran For this purpose, we employ a detailed panel of monthly gasoline consumption data from 160 distribution hubs during the period 2005-2014 We apply two different approaches which are diff-in-diff and panel data estimations The results suggest that the foreign-to-home gasoline price ratios have a significant impact on the time-varying elasticity of demand This finding supports the hypothesis that an increase in domestic gasoline prices will significantly reduce the demand for smuggling In addition, as the distance of a region from its closest higher-price neighboring country diminishes, the price elasticity of demand for gasoline smuggling for that region declines as well Finally, we find that when the ratio of foreign to domestic prices is higher, the gasoline demand sensitivities to price in neighboring countries go up Our work offers new insights regarding the welfare impact of removing fuel subsidies and modifying fuel pricing policies

Nima Rafizadeh

Papers

Machine learning in energy economics and finance: A review

Price Elasticity of Gasoline Smuggling: a Semi-Structural Estimation Approach

Foreign Exchange Shocks and Gasoline Consumption

Price Elasticity of Gasoline Smuggling: A Semi-Structural Estimation Approach

Google Search Explains Your Gasoline Consumption