Nature

Information Bottleneck in Deep Learning and Neural Networks

The information bottleneck (IB) principle is a powerful information‐theoretic framework that seeks to compress data representations while preserving the information most pertinent to a given task.
Digital Journal

The pen that reshaped deep learning: A bold take on neural networks

In recent years, as the field of deep learning has matured, a small but growing group of researchers and technologists has begun to question the prevailing assumptions behind neural networks. Among ...
AlphaGalileo

Interpretability in deep learning for finance: A case study for the Heston model

Deep learning is increasingly used in financial modeling, but its lack of transparency raises risks. Using the well-known ...
EurekAlert!

Dynamic network plasticity and sample efficiency in biological neural cultures: A comparative study with deep reinforcement learning

(A) Schematic illustration of the DishBrain feedback loop, the simulated game environment, and electrode configurations. (B) A schematic illustration of the overall network construction framework. The ...
The Scientist

Artificial Intelligence in Biology: From Neural Networks to AlphaFold

Previously met with skepticism, AI won scientists a Nobel Prize for Chemistry in 2024 after they used it to solve the protein folding and design problem, and it has now been adopted by biologists ...
AZoRobotics

Using Deep Learning to Predict Disease Progression in Chronic Illnesses

Can deep learning catch chronic illness before symptoms show? This article explores how time-aware neural networks are reshaping early detection and care planning for conditions like diabetes and COPD ...