Google's TabFM skips per-dataset training and still predicts on unseen tables, matching tuned baselines and cutting pipeline ...

Aims To develop prediction models for identifying cases with poor visual outcomes after surgery for primary rhegmatogenous ...

But unlike most quants, I run a concentrated, fundamentals-based portfolio. More than 50% of my fund is invested in only eight companies, and they're the kinds of stocks that Peter Lynch and Charlie ...

Abstract: The present work demonstrates a landslide hazard zonation technique based on a machine learning algorithm of Extreme Gradient Boosting (XGboost). The XGboost algorithm uses a multi-parameter ...

Abstract: Recently, multi-floor indoor positioning has become increasingly interesting for researchers, in which accurate recognition of indoor activities is critical for the detection of floor ...

The deep learning revolution has a curious blind spot: the spreadsheet. While Large Language Models (LLMs) have mastered the nuances of human prose and image generators have conquered the digital ...

If you’re a machine learning practitioner, you know this scene well. You’ve spent hours wrangling data, engineering the perfect features, and carefully designing your experiment. Everything is ready.

ABSTRACT: Bipolar disorder is a multifaceted psychiatric illness characterized by unpredictable mood episodes and highly variable treatment responses across individuals. Predicting response to ...

Lithium-ion batteries are quietly powering large parts of the world, including electric vehicles and smartphones. They have revolutionized how people store and use energy. But as these batteries ...