Understanding and predicting complex physical systems remain significant challenges in scientific research and engineering. Machine learning models, while powerful, often fail to follow the ...

We train the parameters of this dynamical system employing modern machine learning methods such as gradient descent optimization. Our case study demonstrates how the employment of complex circuit ...

Eventual's data processing engine Daft was inspried by the founders' experience working on Lyft's autonomous vehicle project.

Machine learning helps improve accuracy and efficiency of small-molecule calculations Microsoft researchers used deep learning to create new DFT model by Sam Lemonick, special to C&EN June 20, 2025 ...

Training LLMs on trajectories of reasoning and tool use makes them superior at multi-step reasoning tasks.

The classification models built on class imbalanced data sets tend to prioritize the accuracy of the majority class, and thus, the minority class generally has a higher misclassification rate.

Many companies fall into the trap of single-loop learning, where they fix immediate issues without questioning the underlying assumptions that led to those problems.

Modeling how cars deform in a crash, how spacecraft respond to extreme environments, or how bridges resist stress could be made thousands of times faster thanks to new artificial intelligence that ...

It is challenging to learn machine learning. For me, great examples for common workflows are crtical. So I built out over 20 well-documented demonstration workflows that apply machine learning to ...