What’s often misunderstood about Google’s incrementality testing and how Bayesian models use probability to guide better decisions.

MESU is a Bayesian framework that balances learning and forgetting by leveraging synaptic uncertainty, enabling continual learning without task boundaries while mitigating catastrophic forgetting, and ...

Concept Bottleneck Models (CBMs) have been proposed as a compromise between white-box and black-box models, aiming to achieve interpretability without sacrificing accuracy. The standard training ...

Abstract: Concept drift in streaming data poses a significant challenge to the stability and performance of deep learning models. This study explores an uncertainty-based drift detection approach ...

In this paper, a fast temporal multiple sparse Bayesian learning (FTMSBL)-based channel estimation method for underwater acoustic (UWA) orthogonal frequency division multiplexing (OFDM) systems is ...

The boat executive, Mr. Costantino, has argued that the Bayesian was an extremely safe vessel that could list even to 75 degrees without capsizing. His company, the Italian Sea Group, in 2022 bought ...

The Bayesian set off on a leisurely cruise around Italy's southern coast on a sunny day in late July. The luxurious super yacht − which boasted one of the largest masts in the world and carried a crew ...

This study focuses on a rescue mission problem, particularly enabling agents/robots to navigate efficiently in unknown environments. Technological advances, including manufacturing, sensing, and ...

Concept-based learning (CBL) in machine learning emphasizes using high-level concepts from raw features for predictions, enhancing model interpretability and efficiency. A prominent type, the ...