Training machines to learn more like humans do
Researchers identify a property that helps computer vision models learn to represent the visual world in a more stable, predictable way.
Researchers identify a property that helps computer vision models learn to represent the visual world in a more stable, predictable way.
“DribbleBot” can maneuver a soccer ball on landscapes such as sand, gravel, mud, and snow, using reinforcement learning to adapt to varying ball dynamics.
With the right building blocks, machine-learning models can more accurately perform tasks like fraud detection or spam filtering.
Researchers used machine learning to build faster and more efficient hash functions, which are a key component of databases.
The method enables a model to determine its confidence in a prediction, while using no additional data and far fewer computing resources than other methods.