Task-Specific Optimisation: SLMs can be fine-tuned for specific tasks or domains, making them more efficient and enabling ...

The MLSys Initiative is led by a group of researchers at the intersection of machine learning and systems at UC San Diego’s Halıcıoğlu Data Science Institute (HDSI), within the School of Computing, ...

Research on climate policy is growing exponentially. Of the approximately 85,000 individual studies ever published on policy ...

A time series database management system (DBMS) efficiently handles large volumes of time-stamped data from sensors and ...

Lifan Wu, an Applied Scientist at Amazon, specializes in high-dimensional data analysis and machine learning. Her research on regularly varying ...

Developed by Google, TensorFlow is a software framework that’s widely used for training and inference of neural networks.

The UCR researchers presented a paper at a recent IEEE big-data workshop, demonstrating a new, unsupervised machine learning ...

Reconstructing unmeasured causal drivers of complex time series from observed response data represents ... causal driver reconstruction usually rely on signal processing or machine learning frameworks ...

Furthermore, the utility of machine learning techniques in the energy sector ... For this purpose, recurrent neural networks, commonly used for time-series analysis (Wang et al., 2022), could be ...

Maximizing the amount of time students spend in class and focused on learning can have long-term benefits. But that’s a tall order for teachers for reasons largely outside of their control.