A new AI model learns to "think" longer on hard problems, achieving more robust reasoning and better generalization to novel, unseen tasks.

A major scientific advance in protein modeling developed by Microsoft Research AI for Science, has been published in Science.

Learn how to build your own GPT-style AI model with this step-by-step guide. Demystify large language models and unlock their ...

Implementation of credit scoring models is a demanding task and crucial for risk management. Wrong decisions can significantly affect revenue, increase costs, and can lead to bankruptcy. Together with ...

Deep Learning for empirical DownScaling DL4DS (Deep Learning for empirical DownScaling) is a Python package that implements state-of-the-art and novel deep learning algorithms for empirical ...

Training immunophenotyping deep learning models with the same-section ground truth cell label derivation method improves virtual staining accuracy ...

Bayesian inference provides a methodology for parameter estimation and uncertainty quantification in machine learning and deep learning methods. Variational inference and Markov Chain Monte-Carlo ...

In conclusion, the paper presents Quanto as a versatile PyTorch quantization toolkit that helps with the challenges of making deep learning models work best on devices with limited resources. Quanto ...

Objectives To develop an interpretable deep learning model of lupus nephritis (LN) relapse prediction based on dynamic multivariable time-series data. Design A single-centre, retrospective cohort ...