/explore

Click through on any links that interest you or select the planets on the right to continue exploring the Outer Web.
You are here

 www.nature.com
Scale-free networks are rare | Nature Communications
| | distill.pub
A Gentle Introduction to Graph Neural Networks
6.9 parsecs away

Travel
| | What components are needed for building learning algorithms that leverage the structure and properties of graphs?
| | educationaltechnologyjournal.springeropen.com
Is it harmful or helpful? Examining the causes and consequences of generative AI usage among university students | Internatio...
3.8 parsecs away

Travel
| | While the discussion on generative artificial intelligence, such as ChatGPT, is making waves in academia and the popular press, there is a need for more insight into the use of ChatGPT among students and the potential harmful or beneficial consequences associated with its usage. Using samples from two studies, the current research examined the causes and consequences of ChatGPT usage among university students. Study 1 developed and validated an eight-item scale to measure ChatGPT usage by conducting a su...
| | progearthplanetsci.springeropen.com
Update of global maps of Alisov's climate classification | Progress in Earth and Planetary Science | Full Text
3.8 parsecs away

Travel
| | Proposed in 1954, Alisov's climate classification (CC) focuses on climatic changes observed in January-July in large-scale air mass zones and their fronts. Herein, data clustering by machine learning was applied to global reanalysis data to quantitatively and objectively determine air mass zones, which were then used to classify the global climate. The differences in air mass zones between two half-year seasons were used to determine climatic zones, which were then subdivided into continental or maritime climatic regions or according to east-west climatic differences. This study renews Alisov's CC for the first time in almost 70years and employs data-driven machine learning to establish a standard for causal CC based on air masses.
| | www.paepper.com
What are embeddings in machine learning? :: Päpper's Machine Learning Blog - This blog features state of the art applications...
20.2 parsecs away

Travel
| Every now and then, you need embeddings when training machine learning models. But what exactly is such an embedding and why do we use it? Basically, an embedding is used when we want to map some representation into another dimensional space. Doesn't make things much clearer, does it? So, let's consider an example: we want to train a recommender system on a movie database (typical Netflix use case). We have many movies and information about the ratings of users given to movies.