Graph Neural Networks
OpenAI now can solve grade level math problems
Articles
Tensorflow released a graph neural networks with announcement of a blog post.
GNNs can be used to answer questions about multiple characteristics of these graphs. By working at the graph level, we try to predict characteristics of the entire graph. We can identify the presence of certain “shapes,” like circles in a graph that might represent sub-molecules or perhaps close social relationships. GNNs can be used on node-level tasks, to classify the nodes of a graph, and predict partitions and affinity in a graph similar to image classification or segmentation. Finally, we can use GNNs at the edge level to discover connections between entities, perhaps using GNNs to “prune” edges to identify the state of objects in a scene.
The library is available in GitHub.
StitchFix wrote about a nifty library that they open-sourced in this blog post. The library Hamilton is a framework that helps a team of Data Scientists manage the creation of a complex dataframe in a shared code base by writing specially shaped functions.
OpenAI wrote about a model that can solve grade school math problems. They use a GPT-3 like model with fine-tuned on the math problems. The dataset is available in the blog post and available in GitHub.
Amazon released a counterfactual dataset in this blog post.
Counterfactual statements in reviews are rare, but they can lead to frustrating experiences for customers — as when, for instance, a search for “red shirt” pulls up a product whose reviews make clear that it is not available in red. To help ease that frustration, we have publicly released a new dataset to help train machine learning models to recognize counterfactual statements.
