Generative AI’s current trajectory relies heavily on Latent Diffusion Models (LDMs) to manage the computational cost of high-resolution synthesis. By compressing data into a lower-dimensional latent ...

Copyright: © 2025 The Author(s). Published by Elsevier Ltd. Machine learning for health data science, fuelled by proliferation of data and reduced computational ...

Are Machine Learning (ML) algorithms superior to traditional econometric models for GDP nowcasting in a time series setting? Based on our evaluation of all models from both classes ever used in ...

DiT-Mem is a plug-and-play memory module for DiT-based video diffusion models designed to inject rich world knowledge during generation. Instead of scaling model size or data, DiT-Mem retrieves a few ...

How can we build AI systems that keep learning new information over time without forgetting what they learned before or retraining from scratch? Google Researchers has introduced Nested Learning, a ...

When you open your washing machine door, are you greeted by a damp, musty odor instead of the smell of clean laundry? There's one habit that can prevent that stinky smell from developing in your front ...

Automated Machine Learning (AutoML) aims to streamline the end-to-end process of ML models, yet current approaches remain constrained by rigid rule-based frameworks and structured input requirements ...

The Perspective by Tiwary et al. (8) offers a comprehensive overview of generative AI methods in computational chemistry. Approaches that generate new outputs (e.g., inferring phase transitions) by ...

Abstract: This study provides a practical solution for predicting the drug response by constructing deep learning algorithms and risk modelling. A suitable data set that is appropriate for building ...

Stroke remains one of the leading causes of global mortality and long-term disability, driving the urgent need for accurate and early risk prediction tools. Traditional models such as the Framingham ...