The field of interpretability investigates what machine learning (ML) models are learning from training datasets, the causes and effects of changes within a model, and the justifications ... machine ...

The intersection of machine learning and mathematical logic — spanning computer science ... Freitag discussed Littlestone dimension and its origins in model theory. He also connected trees and ...

“Essentially you download it, you write down your model or your knowledge or whatever assumptions your making in first-order logic,” he says. “And then you learn weights, and now you have a machine ...

Kraska’s team developed an algorithm that can also apply this kind of logic. They called it a “learned Bloom filter,” and it combines a small Bloom filter with a recurrent neural network (RNN) — a ...

Every day, some little piece of logic constructed by very specific ... My mission is to build a machine learning model that can calculate what makes a good Ars headline. And by "good," I mean ...

These are the building blocks of shapes ... known Fano varieties. Machine learning, however, is built to find patterns in large datasets. By training a machine learning model with some example ...

A machine learning model that processes text must not only ... Next, the input is passed to the first encoder block, which processes it through an “attention layer.” The attention layer ...

Cars are a popular target for machine learning, so large data sets with cars already exist. [Adam] didn’t have to train a neural network, either–he found a pre-trained Mask R-CNN model with ...