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Found 11 papers, 3 papers with code
What's in the Box? An Analysis of Undesirable Content in the Common Crawl Corpus
Whereas much of the success of the current generation of neural language models has been driven by increasingly large training corpora, relatively little research has been dedicated to analyzing these massive sources of textual data.
Geo-Spatiotemporal Features and Shape-Based Prior Knowledge for Fine-grained Imbalanced Data Classification
Fine-grained classification aims at distinguishing between items with similar global perception and patterns, but that differ by minute details.
Analyzing Sustainability Reports Using Natural Language Processing
Climate change is a far-reaching, global phenomenon that will impact many aspects of our society, including the global stock market \cite{dietz2016climate}.
Considerations, Good Practices, Risks and Pitfalls in Developing AI Solutions Against COVID-19
The COVID-19 pandemic has been a major challenge to humanity, with 12. 7 million confirmed cases as of July 13th, 2020 [1].
Mapping the Landscape of Artificial Intelligence Applications against COVID-19
COVID-19, the disease caused by the SARS-CoV-2 virus, has been declared a pandemic by the World Health Organization, which has reported over 18 million confirmed cases as of August 5, 2020.
Using Simulated Data to Generate Images of Climate Change
In our paper, we explore the potential of using images from a simulated 3D environment to improve a domain adaptation task carried out by the MUNIT architecture, aiming to use the resulting images to raise awareness of the potential future impacts of climate change.
On the Morality of Artificial Intelligence
Much of the existing research on the social and ethical impact of Artificial Intelligence has been focused on defining ethical principles and guidelines surrounding Machine Learning (ML) and other Artificial Intelligence (AI) algorithms [IEEE, 2017, Jobin et al., 2019].
Establishing an Evaluation Metric to Quantify Climate Change Image Realism
Because metrics for comparing the realism of different modes in a conditional generative model do not exist, we propose several automated and human-based methods for evaluation.
Quantifying the Carbon Emissions of Machine Learning
From an environmental standpoint, there are a few crucial aspects of training a neural network that have a major impact on the quantity of carbon that it emits.
Tackling Climate Change with Machine Learning
Climate change is one of the greatest challenges facing humanity, and we, as machine learning experts, may wonder how we can help.
Visualizing the Consequences of Climate Change Using Cycle-Consistent Adversarial Networks
We present a project that aims to generate images that depict accurate, vivid, and personalized outcomes of climate change using Cycle-Consistent Adversarial Networks (CycleGANs).
