Mental math's shortcut—pupil dilation suggests people start solving before all numbers are in

People often solve simple arithmetic problems, such as basic addition, subtraction, multiplication or division, in their ...
Frontiers

Computational Frameworks for Decision-Making: From Bayesian Inference to Reinforcement Learning Models

The ability to make adaptive decisions in uncertain environments is a fundamental characteristic of biological intelligence. Historically, computational ...
Scientific Research Publishing

Predicting Stock Price Movements Using Deep Learning Techniques ()

Stock Price Prediction, Deep Learning, LSTM, GRU, Attention Mechanism, Financial Time Series Share and Cite: Kirui, D. (2026) ...
Search Engine Land

How Bayesian testing lets Google measure incrementality with $5,000

Incrementality testing in Google Ads is suddenly within reach for far more advertisers than before. Google has lowered the barriers to running these tests, making lift measurement possible even ...
Frontiers

Enhancing disaster prediction with Bayesian deep learning: a robust approach for uncertainty estimation

Accurate disaster prediction combined with reliable uncertainty quantification is crucial for timely and effective decision-making in emergency management. However, traditional deep learning methods ...
C&EN

Bayesian Optimization with Gaussian Processes Assisted by Deep Learning for Material Designs

Article subjects are automatically applied from the ACS Subject Taxonomy and describe the scientific concepts and themes of the article. Developing novel materials drives significant breakthroughs ...
IEEE

A Bayesian Framework of Deep Reinforcement Learning for Joint O-RAN/MEC Orchestration

Abstract: Multi-access Edge Computing (MEC) can be implemented together with Open Radio Access Network (O-RAN) to offer low-cost deployment and bring services closer to end-users. In this paper, the ...
marktechpost

iP-VAE: A Spiking Neural Network for Iterative Bayesian Inference and ELBO Maximization

The Evidence Lower Bound (ELBO) is a key objective for training generative models like Variational Autoencoders (VAEs). It parallels neuroscience, aligning with the Free Energy Principle (FEP) for ...